diff --git a/annotate/PWC_AIO_terms_wcounts.txt b/annotate/PWC_AIO_terms_wcounts.txt
index d50a8c9..6f59763 100644
--- a/annotate/PWC_AIO_terms_wcounts.txt
+++ b/annotate/PWC_AIO_terms_wcounts.txt
@@ -1,15 +1,14 @@
-3218 model
+1609 model
1608 network
535 arch
529 layer
- 526 function
- 358 language model
319 var
308 convolutional neural network
233 normalization
202 natural language processing
192 reinforcement learning
192 classification
+ 179 language model
179 cnn
165 kn
152 representation learning
@@ -34,7 +33,6 @@
62 adaptation
60 bert
53 distillation
- 52 masked language model
50 convolutional layer
47 mlp
45 domain adaptation
@@ -49,7 +47,7 @@
28 vae
27 dn
27 convnet
- 26 word2vec
+ 26 masked language model
26 gcn
22 attention layer
21 interpretability
@@ -71,15 +69,13 @@
14 perceptron
14 nlp
14 denoising autoencoder
+ 13 word2vec
13 tokenization
13 mp
13 mc
- 12 knn
- 12 autoregressive language model
11 unsupervised learning
11 embedding layer
10 transformer network
- 10 node2vec
10 exponential linear unit
10 active learning
9 metric learning
@@ -96,6 +92,7 @@
8 dcn
8 curriculum learning
8 cbow
+ 8 batchnorm
8 activation layer
7 text generation
7 sparse autoencoder
@@ -108,8 +105,10 @@
6 deep convolutional network
6 decision tree
6 catastrophic forgetting
+ 6 autoregressive language model
5 sentencepiece
5 recurrent network
+ 5 node2vec
5 logistic regression
5 linear function
5 glm
@@ -121,8 +120,8 @@
4 skipgram
4 refining
4 parameter efficiency
- 4 multimodal transformer
4 linear regression
+ 4 knn
4 graphical model
4 ensemble learning
4 deep belief network
@@ -149,14 +148,12 @@
3 model parallelism
3 lifelong learning
3 lambda layer
- 3 k-nn
3 input layer
3 fragmentation
3 dimension reduction
3 data processing
3 dae
3 conv1d
- 3 cluster analysis
3 bidirectional encoder
2 zero-shot learning
2 text normalization
@@ -167,6 +164,8 @@
2 self-organizing map
2 scaled exponential linear unit
2 recursive neural network
+ 2 multimodal transformer
+ 2 machine learning task
2 iterative refinement
2 hopfield network
2 hierarchical clustering
@@ -203,6 +202,7 @@
1 kohonen network
1 knowledge transfer
1 knowledge integration
+ 1 k-nn
1 hn
1 dropout layer
1 deep residual network
@@ -210,4 +210,5 @@
1 data cleaning
1 conv2d
1 conditional masking
+ 1 cluster analysis
1 cleaning
diff --git a/annotate/analyze_PWC_annotations.ipynb b/annotate/analyze_PWC_annotations.ipynb
index b43a6f1..8fb3954 100644
--- a/annotate/analyze_PWC_annotations.ipynb
+++ b/annotate/analyze_PWC_annotations.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": 3,
"id": "6b269c91-1f75-49b5-9dac-25b969a88936",
"metadata": {},
"outputs": [
@@ -10,18040 +10,59 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Requirement already satisfied: matplotlib in /Users/marcin/.pyenv/versions/3.10.11/lib/python3.10/site-packages (3.8.0)\n",
- "Requirement already satisfied: contourpy>=1.0.1 in /Users/marcin/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from matplotlib) (1.1.1)\n",
- "Requirement already satisfied: cycler>=0.10 in /Users/marcin/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from matplotlib) (0.11.0)\n",
- "Requirement already satisfied: fonttools>=4.22.0 in /Users/marcin/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from matplotlib) (4.42.1)\n",
- "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/marcin/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from matplotlib) (1.4.5)\n",
- "Requirement already satisfied: numpy<2,>=1.21 in /Users/marcin/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from matplotlib) (1.26.0)\n",
- "Requirement already satisfied: packaging>=20.0 in /Users/marcin/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from matplotlib) (23.2)\n",
- "Requirement already satisfied: pillow>=6.2.0 in /Users/marcin/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from matplotlib) (10.0.1)\n",
- "Requirement already satisfied: pyparsing>=2.3.1 in /Users/marcin/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from matplotlib) (3.1.1)\n",
- "Requirement already satisfied: python-dateutil>=2.7 in /Users/marcin/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from matplotlib) (2.8.2)\n",
- "Requirement already satisfied: six>=1.5 in /Users/marcin/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
- "\n",
- "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
- "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
- ]
- }
- ],
- "source": [
- "!pip install matplotlib"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "id": "35f54f59-325a-44e2-aa3e-35a32e4b8322",
- "metadata": {},
- "outputs": [],
- "source": [
- "import matplotlib.pyplot as plt\n",
- "import yaml\n",
- "import pandas as pd\n",
- "import numpy as np\n",
- "#import oak"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 46,
- "id": "e9b271b8-b757-4c61-ae2f-9177d28e4929",
- "metadata": {},
- "outputs": [],
- "source": [
- "#methods_reformat_annotate.out"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 47,
- "id": "3545af76-d45c-4e68-90db-ccc3a26a0ac0",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " subject \\\n",
- "0 aio:AE \n",
- "1 aio:AE \n",
- "2 aio:AE \n",
- "3 aio:ANN \n",
- "4 aio:ANN \n",
- "... ... \n",
- "1074 aio:t-Distributed_Stochastic_Neighbor_embedding \n",
- "1075 aio:t-Distributed_Stochastic_Neighbor_embedding \n",
- "1076 aio:t-Distributed_Stochastic_Neighbor_embedding \n",
- "1077 aio:t-Distributed_Stochastic_Neighbor_embedding \n",
- "1078 aio:t-Distributed_Stochastic_Neighbor_embedding \n",
- "\n",
- " subject_label predicate \\\n",
- "0 Auto Encoder Network rdfs:subClassOf \n",
- "1 Auto Encoder Network rdfs:subClassOf \n",
- "2 Auto Encoder Network rdfs:subClassOf \n",
- "3 Artificial Neural Network rdfs:subClassOf \n",
- "4 Artificial Neural Network rdfs:subClassOf \n",
- "... ... ... \n",
- "1074 t-Distributed Stochastic Neighbor embedding rdfs:subClassOf \n",
- "1075 t-Distributed Stochastic Neighbor embedding rdfs:subClassOf \n",
- "1076 t-Distributed Stochastic Neighbor embedding rdfs:subClassOf \n",
- "1077 t-Distributed Stochastic Neighbor embedding rdfs:subClassOf \n",
- "1078 t-Distributed Stochastic Neighbor embedding rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "0 None aio:AE \n",
- "1 None aio:Network \n",
- "2 None aio:UPN \n",
- "3 None aio:ANN \n",
- "4 None aio:Network \n",
- "... ... ... \n",
- "1074 None aio:Dimensionality_Reduction \n",
- "1075 None aio:Machine_Learning \n",
- "1076 None aio:Method \n",
- "1077 None aio:Unsupervised_Learning \n",
- "1078 None aio:t-Distributed_Stochastic_Neighbor_embedding \n",
- "\n",
- " object_label \n",
- "0 Auto Encoder Network \n",
- "1 Network \n",
- "2 Unsupervised Pretrained Network \n",
- "3 Artificial Neural Network \n",
- "4 Network \n",
- "... ... \n",
- "1074 Dimensionality Reduction \n",
- "1075 Machine Learning \n",
- "1076 Method \n",
- "1077 Unsupervised Learning \n",
- "1078 t-Distributed Stochastic Neighbor embedding \n",
- "\n",
- "[1079 rows x 6 columns]\n"
- ]
- }
- ],
- "source": [
- "file_path = 'children_for_branches_v4.out'\n",
- "\n",
- "# Load the file into a DataFrame\n",
- "branches_df = pd.read_csv(file_path, sep='\\t', header=0)\n",
- "\n",
- "# Name the columns\n",
- "#branches_df.columns = ['ID', 'Label', 'Predicate', 'Object', 'ParentID', 'ParentLabel']\n",
- "\n",
- "# Remove the appended AIO branch name from the ID column\n",
- "# This regex pattern looks for an underscore followed by any characters at the end of the string and replaces it with nothing\n",
- "#branches_df['ID'] = branches_df['ID'].str.replace(r'_[A-Za-z]+$', '', regex=True)\n",
- "\n",
- "print(branches_df)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 48,
- "id": "5946cdab-34dd-44b3-99b3-79ab44adccd4",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "aio:AE\n",
- " subject subject_label predicate predicate_label object \\\n",
- "0 aio:AE Auto Encoder Network rdfs:subClassOf None aio:AE \n",
- "1 aio:AE Auto Encoder Network rdfs:subClassOf None aio:Network \n",
- "2 aio:AE Auto Encoder Network rdfs:subClassOf None aio:UPN \n",
- "\n",
- " object_label \n",
- "0 Auto Encoder Network \n",
- "1 Network \n",
- "2 Unsupervised Pretrained Network \n",
- "parent aio:AE\n",
- "aio:AE\n",
- " subject subject_label predicate predicate_label object \\\n",
- "0 aio:AE Auto Encoder Network rdfs:subClassOf None aio:AE \n",
- "1 aio:AE Auto Encoder Network rdfs:subClassOf None aio:Network \n",
- "2 aio:AE Auto Encoder Network rdfs:subClassOf None aio:UPN \n",
- "\n",
- " object_label \n",
- "0 Auto Encoder Network \n",
- "1 Network \n",
- "2 Unsupervised Pretrained Network \n",
- "parent aio:AE\n",
- "aio:AE\n",
- " subject subject_label predicate predicate_label object \\\n",
- "0 aio:AE Auto Encoder Network rdfs:subClassOf None aio:AE \n",
- "1 aio:AE Auto Encoder Network rdfs:subClassOf None aio:Network \n",
- "2 aio:AE Auto Encoder Network rdfs:subClassOf None aio:UPN \n",
- "\n",
- " object_label \n",
- "0 Auto Encoder Network \n",
- "1 Network \n",
- "2 Unsupervised Pretrained Network \n",
- "parent aio:AE\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:AbstractRNNCell\n",
- " subject subject_label predicate predicate_label \\\n",
- "5 aio:AbstractRNNCell AbstractRNNCell rdfs:subClassOf None \n",
- "6 aio:AbstractRNNCell AbstractRNNCell rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "5 aio:AbstractRNNCell AbstractRNNCell \n",
- "6 aio:Layer Layer \n",
- "parent aio:AbstractRNNCell\n",
- "aio:AbstractRNNCell\n",
- " subject subject_label predicate predicate_label \\\n",
- "5 aio:AbstractRNNCell AbstractRNNCell rdfs:subClassOf None \n",
- "6 aio:AbstractRNNCell AbstractRNNCell rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "5 aio:AbstractRNNCell AbstractRNNCell \n",
- "6 aio:Layer Layer \n",
- "parent aio:AbstractRNNCell\n",
- "aio:Activation_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "7 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "8 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "7 aio:Activation_Layer Activation Layer \n",
- "8 aio:Layer Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:Activation_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "7 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "8 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "7 aio:Activation_Layer Activation Layer \n",
- "8 aio:Layer Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:Active_Learning\n",
- " subject subject_label predicate predicate_label \\\n",
- "9 aio:Active_Learning Active Learning rdfs:subClassOf None \n",
- "10 aio:Active_Learning Active Learning rdfs:subClassOf None \n",
- "11 aio:Active_Learning Active Learning rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "9 aio:Active_Learning Active Learning \n",
- "10 aio:Machine_Learning Machine Learning \n",
- "11 aio:Method Method \n",
- "parent aio:Active_Learning\n",
- "aio:Active_Learning\n",
- " subject subject_label predicate predicate_label \\\n",
- "9 aio:Active_Learning Active Learning rdfs:subClassOf None \n",
- "10 aio:Active_Learning Active Learning rdfs:subClassOf None \n",
- "11 aio:Active_Learning Active Learning rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "9 aio:Active_Learning Active Learning \n",
- "10 aio:Machine_Learning Machine Learning \n",
- "11 aio:Method Method \n",
- "parent aio:Active_Learning\n",
- "aio:Active_Learning\n",
- " subject subject_label predicate predicate_label \\\n",
- "9 aio:Active_Learning Active Learning rdfs:subClassOf None \n",
- "10 aio:Active_Learning Active Learning rdfs:subClassOf None \n",
- "11 aio:Active_Learning Active Learning rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "9 aio:Active_Learning Active Learning \n",
- "10 aio:Machine_Learning Machine Learning \n",
- "11 aio:Method Method \n",
- "parent aio:Active_Learning\n",
- "aio:ActivityRegularization_Layer\n",
- " subject subject_label \\\n",
- "12 aio:ActivityRegularization_Layer ActivityRegularization Layer \n",
- "13 aio:ActivityRegularization_Layer ActivityRegularization Layer \n",
- "14 aio:ActivityRegularization_Layer ActivityRegularization Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "12 rdfs:subClassOf None aio:ActivityRegularization_Layer \n",
- "13 rdfs:subClassOf None aio:Layer \n",
- "14 rdfs:subClassOf None aio:Regularization_Layer \n",
- "\n",
- " object_label \n",
- "12 ActivityRegularization Layer \n",
- "13 Layer \n",
- "14 Regularization Layer \n",
- "parent aio:ActivityRegularization_Layer\n",
- "aio:ActivityRegularization_Layer\n",
- " subject subject_label \\\n",
- "12 aio:ActivityRegularization_Layer ActivityRegularization Layer \n",
- "13 aio:ActivityRegularization_Layer ActivityRegularization Layer \n",
- "14 aio:ActivityRegularization_Layer ActivityRegularization Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "12 rdfs:subClassOf None aio:ActivityRegularization_Layer \n",
- "13 rdfs:subClassOf None aio:Layer \n",
- "14 rdfs:subClassOf None aio:Regularization_Layer \n",
- "\n",
- " object_label \n",
- "12 ActivityRegularization Layer \n",
- "13 Layer \n",
- "14 Regularization Layer \n",
- "parent aio:ActivityRegularization_Layer\n",
- "aio:ActivityRegularization_Layer\n",
- " subject subject_label \\\n",
- "12 aio:ActivityRegularization_Layer ActivityRegularization Layer \n",
- "13 aio:ActivityRegularization_Layer ActivityRegularization Layer \n",
- "14 aio:ActivityRegularization_Layer ActivityRegularization Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "12 rdfs:subClassOf None aio:ActivityRegularization_Layer \n",
- "13 rdfs:subClassOf None aio:Layer \n",
- "14 rdfs:subClassOf None aio:Regularization_Layer \n",
- "\n",
- " object_label \n",
- "12 ActivityRegularization Layer \n",
- "13 Layer \n",
- "14 Regularization Layer \n",
- "parent aio:ActivityRegularization_Layer\n",
- "aio:Activity_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "15 aio:Activity_Bias Activity Bias rdfs:subClassOf None \n",
- "16 aio:Activity_Bias Activity Bias rdfs:subClassOf None \n",
- "17 aio:Activity_Bias Activity Bias rdfs:subClassOf None \n",
- "18 aio:Activity_Bias Activity Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "15 aio:Activity_Bias Activity Bias \n",
- "16 aio:Bias Bias \n",
- "17 aio:Computational_Bias Computational Bias \n",
- "18 aio:Use_And_Interpretation_Bias Use And Interpretation Bias \n",
- "parent aio:Activity_Bias\n",
- "aio:Activity_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "15 aio:Activity_Bias Activity Bias rdfs:subClassOf None \n",
- "16 aio:Activity_Bias Activity Bias rdfs:subClassOf None \n",
- "17 aio:Activity_Bias Activity Bias rdfs:subClassOf None \n",
- "18 aio:Activity_Bias Activity Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "15 aio:Activity_Bias Activity Bias \n",
- "16 aio:Bias Bias \n",
- "17 aio:Computational_Bias Computational Bias \n",
- "18 aio:Use_And_Interpretation_Bias Use And Interpretation Bias \n",
- "parent aio:Activity_Bias\n",
- "aio:Activity_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "15 aio:Activity_Bias Activity Bias rdfs:subClassOf None \n",
- "16 aio:Activity_Bias Activity Bias rdfs:subClassOf None \n",
- "17 aio:Activity_Bias Activity Bias rdfs:subClassOf None \n",
- "18 aio:Activity_Bias Activity Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "15 aio:Activity_Bias Activity Bias \n",
- "16 aio:Bias Bias \n",
- "17 aio:Computational_Bias Computational Bias \n",
- "18 aio:Use_And_Interpretation_Bias Use And Interpretation Bias \n",
- "parent aio:Activity_Bias\n",
- "aio:Activity_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "15 aio:Activity_Bias Activity Bias rdfs:subClassOf None \n",
- "16 aio:Activity_Bias Activity Bias rdfs:subClassOf None \n",
- "17 aio:Activity_Bias Activity Bias rdfs:subClassOf None \n",
- "18 aio:Activity_Bias Activity Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "15 aio:Activity_Bias Activity Bias \n",
- "16 aio:Bias Bias \n",
- "17 aio:Computational_Bias Computational Bias \n",
- "18 aio:Use_And_Interpretation_Bias Use And Interpretation Bias \n",
- "parent aio:Activity_Bias\n",
- "aio:AdaptiveAvgPool1D_Layer\n",
- " subject subject_label predicate \\\n",
- "19 aio:AdaptiveAvgPool1D_Layer AdaptiveAvgPool1D Layer rdfs:subClassOf \n",
- "20 aio:AdaptiveAvgPool1D_Layer AdaptiveAvgPool1D Layer rdfs:subClassOf \n",
- "21 aio:AdaptiveAvgPool1D_Layer AdaptiveAvgPool1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "19 None aio:AdaptiveAvgPool1D_Layer AdaptiveAvgPool1D Layer \n",
- "20 None aio:Layer Layer \n",
- "21 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AdaptiveAvgPool1D_Layer\n",
- "aio:AdaptiveAvgPool1D_Layer\n",
- " subject subject_label predicate \\\n",
- "19 aio:AdaptiveAvgPool1D_Layer AdaptiveAvgPool1D Layer rdfs:subClassOf \n",
- "20 aio:AdaptiveAvgPool1D_Layer AdaptiveAvgPool1D Layer rdfs:subClassOf \n",
- "21 aio:AdaptiveAvgPool1D_Layer AdaptiveAvgPool1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "19 None aio:AdaptiveAvgPool1D_Layer AdaptiveAvgPool1D Layer \n",
- "20 None aio:Layer Layer \n",
- "21 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AdaptiveAvgPool1D_Layer\n",
- "aio:AdaptiveAvgPool1D_Layer\n",
- " subject subject_label predicate \\\n",
- "19 aio:AdaptiveAvgPool1D_Layer AdaptiveAvgPool1D Layer rdfs:subClassOf \n",
- "20 aio:AdaptiveAvgPool1D_Layer AdaptiveAvgPool1D Layer rdfs:subClassOf \n",
- "21 aio:AdaptiveAvgPool1D_Layer AdaptiveAvgPool1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "19 None aio:AdaptiveAvgPool1D_Layer AdaptiveAvgPool1D Layer \n",
- "20 None aio:Layer Layer \n",
- "21 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AdaptiveAvgPool1D_Layer\n",
- "aio:AdaptiveAvgPool2D_Layer\n",
- " subject subject_label predicate \\\n",
- "22 aio:AdaptiveAvgPool2D_Layer AdaptiveAvgPool2D Layer rdfs:subClassOf \n",
- "23 aio:AdaptiveAvgPool2D_Layer AdaptiveAvgPool2D Layer rdfs:subClassOf \n",
- "24 aio:AdaptiveAvgPool2D_Layer AdaptiveAvgPool2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "22 None aio:AdaptiveAvgPool2D_Layer AdaptiveAvgPool2D Layer \n",
- "23 None aio:Layer Layer \n",
- "24 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AdaptiveAvgPool2D_Layer\n",
- "aio:AdaptiveAvgPool2D_Layer\n",
- " subject subject_label predicate \\\n",
- "22 aio:AdaptiveAvgPool2D_Layer AdaptiveAvgPool2D Layer rdfs:subClassOf \n",
- "23 aio:AdaptiveAvgPool2D_Layer AdaptiveAvgPool2D Layer rdfs:subClassOf \n",
- "24 aio:AdaptiveAvgPool2D_Layer AdaptiveAvgPool2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "22 None aio:AdaptiveAvgPool2D_Layer AdaptiveAvgPool2D Layer \n",
- "23 None aio:Layer Layer \n",
- "24 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AdaptiveAvgPool2D_Layer\n",
- "aio:AdaptiveAvgPool2D_Layer\n",
- " subject subject_label predicate \\\n",
- "22 aio:AdaptiveAvgPool2D_Layer AdaptiveAvgPool2D Layer rdfs:subClassOf \n",
- "23 aio:AdaptiveAvgPool2D_Layer AdaptiveAvgPool2D Layer rdfs:subClassOf \n",
- "24 aio:AdaptiveAvgPool2D_Layer AdaptiveAvgPool2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "22 None aio:AdaptiveAvgPool2D_Layer AdaptiveAvgPool2D Layer \n",
- "23 None aio:Layer Layer \n",
- "24 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AdaptiveAvgPool2D_Layer\n",
- "aio:AdaptiveAvgPool3D_Layer\n",
- " subject subject_label predicate \\\n",
- "25 aio:AdaptiveAvgPool3D_Layer AdaptiveAvgPool3D Layer rdfs:subClassOf \n",
- "26 aio:AdaptiveAvgPool3D_Layer AdaptiveAvgPool3D Layer rdfs:subClassOf \n",
- "27 aio:AdaptiveAvgPool3D_Layer AdaptiveAvgPool3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "25 None aio:AdaptiveAvgPool3D_Layer AdaptiveAvgPool3D Layer \n",
- "26 None aio:Layer Layer \n",
- "27 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AdaptiveAvgPool3D_Layer\n",
- "aio:AdaptiveAvgPool3D_Layer\n",
- " subject subject_label predicate \\\n",
- "25 aio:AdaptiveAvgPool3D_Layer AdaptiveAvgPool3D Layer rdfs:subClassOf \n",
- "26 aio:AdaptiveAvgPool3D_Layer AdaptiveAvgPool3D Layer rdfs:subClassOf \n",
- "27 aio:AdaptiveAvgPool3D_Layer AdaptiveAvgPool3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "25 None aio:AdaptiveAvgPool3D_Layer AdaptiveAvgPool3D Layer \n",
- "26 None aio:Layer Layer \n",
- "27 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AdaptiveAvgPool3D_Layer\n",
- "aio:AdaptiveAvgPool3D_Layer\n",
- " subject subject_label predicate \\\n",
- "25 aio:AdaptiveAvgPool3D_Layer AdaptiveAvgPool3D Layer rdfs:subClassOf \n",
- "26 aio:AdaptiveAvgPool3D_Layer AdaptiveAvgPool3D Layer rdfs:subClassOf \n",
- "27 aio:AdaptiveAvgPool3D_Layer AdaptiveAvgPool3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "25 None aio:AdaptiveAvgPool3D_Layer AdaptiveAvgPool3D Layer \n",
- "26 None aio:Layer Layer \n",
- "27 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AdaptiveAvgPool3D_Layer\n",
- "aio:AdaptiveMaxPool1D_Layer\n",
- " subject subject_label predicate \\\n",
- "28 aio:AdaptiveMaxPool1D_Layer AdaptiveMaxPool1D Layer rdfs:subClassOf \n",
- "29 aio:AdaptiveMaxPool1D_Layer AdaptiveMaxPool1D Layer rdfs:subClassOf \n",
- "30 aio:AdaptiveMaxPool1D_Layer AdaptiveMaxPool1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "28 None aio:AdaptiveMaxPool1D_Layer AdaptiveMaxPool1D Layer \n",
- "29 None aio:Layer Layer \n",
- "30 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AdaptiveMaxPool1D_Layer\n",
- "aio:AdaptiveMaxPool1D_Layer\n",
- " subject subject_label predicate \\\n",
- "28 aio:AdaptiveMaxPool1D_Layer AdaptiveMaxPool1D Layer rdfs:subClassOf \n",
- "29 aio:AdaptiveMaxPool1D_Layer AdaptiveMaxPool1D Layer rdfs:subClassOf \n",
- "30 aio:AdaptiveMaxPool1D_Layer AdaptiveMaxPool1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "28 None aio:AdaptiveMaxPool1D_Layer AdaptiveMaxPool1D Layer \n",
- "29 None aio:Layer Layer \n",
- "30 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AdaptiveMaxPool1D_Layer\n",
- "aio:AdaptiveMaxPool1D_Layer\n",
- " subject subject_label predicate \\\n",
- "28 aio:AdaptiveMaxPool1D_Layer AdaptiveMaxPool1D Layer rdfs:subClassOf \n",
- "29 aio:AdaptiveMaxPool1D_Layer AdaptiveMaxPool1D Layer rdfs:subClassOf \n",
- "30 aio:AdaptiveMaxPool1D_Layer AdaptiveMaxPool1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "28 None aio:AdaptiveMaxPool1D_Layer AdaptiveMaxPool1D Layer \n",
- "29 None aio:Layer Layer \n",
- "30 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AdaptiveMaxPool1D_Layer\n",
- "aio:AdaptiveMaxPool2D_Layer\n",
- " subject subject_label predicate \\\n",
- "31 aio:AdaptiveMaxPool2D_Layer AdaptiveMaxPool2D Layer rdfs:subClassOf \n",
- "32 aio:AdaptiveMaxPool2D_Layer AdaptiveMaxPool2D Layer rdfs:subClassOf \n",
- "33 aio:AdaptiveMaxPool2D_Layer AdaptiveMaxPool2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "31 None aio:AdaptiveMaxPool2D_Layer AdaptiveMaxPool2D Layer \n",
- "32 None aio:Layer Layer \n",
- "33 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AdaptiveMaxPool2D_Layer\n",
- "aio:AdaptiveMaxPool2D_Layer\n",
- " subject subject_label predicate \\\n",
- "31 aio:AdaptiveMaxPool2D_Layer AdaptiveMaxPool2D Layer rdfs:subClassOf \n",
- "32 aio:AdaptiveMaxPool2D_Layer AdaptiveMaxPool2D Layer rdfs:subClassOf \n",
- "33 aio:AdaptiveMaxPool2D_Layer AdaptiveMaxPool2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "31 None aio:AdaptiveMaxPool2D_Layer AdaptiveMaxPool2D Layer \n",
- "32 None aio:Layer Layer \n",
- "33 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AdaptiveMaxPool2D_Layer\n",
- "aio:AdaptiveMaxPool2D_Layer\n",
- " subject subject_label predicate \\\n",
- "31 aio:AdaptiveMaxPool2D_Layer AdaptiveMaxPool2D Layer rdfs:subClassOf \n",
- "32 aio:AdaptiveMaxPool2D_Layer AdaptiveMaxPool2D Layer rdfs:subClassOf \n",
- "33 aio:AdaptiveMaxPool2D_Layer AdaptiveMaxPool2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "31 None aio:AdaptiveMaxPool2D_Layer AdaptiveMaxPool2D Layer \n",
- "32 None aio:Layer Layer \n",
- "33 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AdaptiveMaxPool2D_Layer\n",
- "aio:AdaptiveMaxPool3D_Layer\n",
- " subject subject_label predicate \\\n",
- "34 aio:AdaptiveMaxPool3D_Layer AdaptiveMaxPool3D Layer rdfs:subClassOf \n",
- "35 aio:AdaptiveMaxPool3D_Layer AdaptiveMaxPool3D Layer rdfs:subClassOf \n",
- "36 aio:AdaptiveMaxPool3D_Layer AdaptiveMaxPool3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "34 None aio:AdaptiveMaxPool3D_Layer AdaptiveMaxPool3D Layer \n",
- "35 None aio:Layer Layer \n",
- "36 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AdaptiveMaxPool3D_Layer\n",
- "aio:AdaptiveMaxPool3D_Layer\n",
- " subject subject_label predicate \\\n",
- "34 aio:AdaptiveMaxPool3D_Layer AdaptiveMaxPool3D Layer rdfs:subClassOf \n",
- "35 aio:AdaptiveMaxPool3D_Layer AdaptiveMaxPool3D Layer rdfs:subClassOf \n",
- "36 aio:AdaptiveMaxPool3D_Layer AdaptiveMaxPool3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "34 None aio:AdaptiveMaxPool3D_Layer AdaptiveMaxPool3D Layer \n",
- "35 None aio:Layer Layer \n",
- "36 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AdaptiveMaxPool3D_Layer\n",
- "aio:AdaptiveMaxPool3D_Layer\n",
- " subject subject_label predicate \\\n",
- "34 aio:AdaptiveMaxPool3D_Layer AdaptiveMaxPool3D Layer rdfs:subClassOf \n",
- "35 aio:AdaptiveMaxPool3D_Layer AdaptiveMaxPool3D Layer rdfs:subClassOf \n",
- "36 aio:AdaptiveMaxPool3D_Layer AdaptiveMaxPool3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "34 None aio:AdaptiveMaxPool3D_Layer AdaptiveMaxPool3D Layer \n",
- "35 None aio:Layer Layer \n",
- "36 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AdaptiveMaxPool3D_Layer\n",
- "aio:Add_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "37 aio:Add_Layer Add Layer rdfs:subClassOf None \n",
- "38 aio:Add_Layer Add Layer rdfs:subClassOf None \n",
- "39 aio:Add_Layer Add Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "37 aio:Add_Layer Add Layer \n",
- "38 aio:Layer Layer \n",
- "39 aio:Merging_Layer Merging Layer \n",
- "parent aio:Add_Layer\n",
- "aio:Add_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "37 aio:Add_Layer Add Layer rdfs:subClassOf None \n",
- "38 aio:Add_Layer Add Layer rdfs:subClassOf None \n",
- "39 aio:Add_Layer Add Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "37 aio:Add_Layer Add Layer \n",
- "38 aio:Layer Layer \n",
- "39 aio:Merging_Layer Merging Layer \n",
- "parent aio:Add_Layer\n",
- "aio:Add_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "37 aio:Add_Layer Add Layer rdfs:subClassOf None \n",
- "38 aio:Add_Layer Add Layer rdfs:subClassOf None \n",
- "39 aio:Add_Layer Add Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "37 aio:Add_Layer Add Layer \n",
- "38 aio:Layer Layer \n",
- "39 aio:Merging_Layer Merging Layer \n",
- "parent aio:Add_Layer\n",
- "aio:AdditiveAttention_Layer\n",
- " subject subject_label predicate \\\n",
- "40 aio:AdditiveAttention_Layer AdditiveAttention Layer rdfs:subClassOf \n",
- "41 aio:AdditiveAttention_Layer AdditiveAttention Layer rdfs:subClassOf \n",
- "42 aio:AdditiveAttention_Layer AdditiveAttention Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "40 None aio:AdditiveAttention_Layer AdditiveAttention Layer \n",
- "41 None aio:Attention_Layer Attention Layer \n",
- "42 None aio:Layer Layer \n",
- "parent aio:AdditiveAttention_Layer\n",
- "aio:AdditiveAttention_Layer\n",
- " subject subject_label predicate \\\n",
- "40 aio:AdditiveAttention_Layer AdditiveAttention Layer rdfs:subClassOf \n",
- "41 aio:AdditiveAttention_Layer AdditiveAttention Layer rdfs:subClassOf \n",
- "42 aio:AdditiveAttention_Layer AdditiveAttention Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "40 None aio:AdditiveAttention_Layer AdditiveAttention Layer \n",
- "41 None aio:Attention_Layer Attention Layer \n",
- "42 None aio:Layer Layer \n",
- "parent aio:AdditiveAttention_Layer\n",
- "aio:AdditiveAttention_Layer\n",
- " subject subject_label predicate \\\n",
- "40 aio:AdditiveAttention_Layer AdditiveAttention Layer rdfs:subClassOf \n",
- "41 aio:AdditiveAttention_Layer AdditiveAttention Layer rdfs:subClassOf \n",
- "42 aio:AdditiveAttention_Layer AdditiveAttention Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "40 None aio:AdditiveAttention_Layer AdditiveAttention Layer \n",
- "41 None aio:Attention_Layer Attention Layer \n",
- "42 None aio:Layer Layer \n",
- "parent aio:AdditiveAttention_Layer\n",
- "aio:AlphaDropout_Layer\n",
- " subject subject_label predicate \\\n",
- "43 aio:AlphaDropout_Layer AlphaDropout Layer rdfs:subClassOf \n",
- "44 aio:AlphaDropout_Layer AlphaDropout Layer rdfs:subClassOf \n",
- "45 aio:AlphaDropout_Layer AlphaDropout Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "43 None aio:AlphaDropout_Layer AlphaDropout Layer \n",
- "44 None aio:Layer Layer \n",
- "45 None aio:Regularization_Layer Regularization Layer \n",
- "parent aio:AlphaDropout_Layer\n",
- "aio:AlphaDropout_Layer\n",
- " subject subject_label predicate \\\n",
- "43 aio:AlphaDropout_Layer AlphaDropout Layer rdfs:subClassOf \n",
- "44 aio:AlphaDropout_Layer AlphaDropout Layer rdfs:subClassOf \n",
- "45 aio:AlphaDropout_Layer AlphaDropout Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "43 None aio:AlphaDropout_Layer AlphaDropout Layer \n",
- "44 None aio:Layer Layer \n",
- "45 None aio:Regularization_Layer Regularization Layer \n",
- "parent aio:AlphaDropout_Layer\n",
- "aio:AlphaDropout_Layer\n",
- " subject subject_label predicate \\\n",
- "43 aio:AlphaDropout_Layer AlphaDropout Layer rdfs:subClassOf \n",
- "44 aio:AlphaDropout_Layer AlphaDropout Layer rdfs:subClassOf \n",
- "45 aio:AlphaDropout_Layer AlphaDropout Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "43 None aio:AlphaDropout_Layer AlphaDropout Layer \n",
- "44 None aio:Layer Layer \n",
- "45 None aio:Regularization_Layer Regularization Layer \n",
- "parent aio:AlphaDropout_Layer\n",
- "aio:Amplification_Bias\n",
- " subject subject_label predicate \\\n",
- "46 aio:Amplification_Bias Amplification Bias rdfs:subClassOf \n",
- "47 aio:Amplification_Bias Amplification Bias rdfs:subClassOf \n",
- "48 aio:Amplification_Bias Amplification Bias rdfs:subClassOf \n",
- "49 aio:Amplification_Bias Amplification Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "46 None aio:Amplification_Bias Amplification Bias \n",
- "47 None aio:Bias Bias \n",
- "48 None aio:Computational_Bias Computational Bias \n",
- "49 None aio:Processing_Bias Processing Bias \n",
- "parent aio:Amplification_Bias\n",
- "aio:Amplification_Bias\n",
- " subject subject_label predicate \\\n",
- "46 aio:Amplification_Bias Amplification Bias rdfs:subClassOf \n",
- "47 aio:Amplification_Bias Amplification Bias rdfs:subClassOf \n",
- "48 aio:Amplification_Bias Amplification Bias rdfs:subClassOf \n",
- "49 aio:Amplification_Bias Amplification Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "46 None aio:Amplification_Bias Amplification Bias \n",
- "47 None aio:Bias Bias \n",
- "48 None aio:Computational_Bias Computational Bias \n",
- "49 None aio:Processing_Bias Processing Bias \n",
- "parent aio:Amplification_Bias\n",
- "aio:Amplification_Bias\n",
- " subject subject_label predicate \\\n",
- "46 aio:Amplification_Bias Amplification Bias rdfs:subClassOf \n",
- "47 aio:Amplification_Bias Amplification Bias rdfs:subClassOf \n",
- "48 aio:Amplification_Bias Amplification Bias rdfs:subClassOf \n",
- "49 aio:Amplification_Bias Amplification Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "46 None aio:Amplification_Bias Amplification Bias \n",
- "47 None aio:Bias Bias \n",
- "48 None aio:Computational_Bias Computational Bias \n",
- "49 None aio:Processing_Bias Processing Bias \n",
- "parent aio:Amplification_Bias\n",
- "aio:Amplification_Bias\n",
- " subject subject_label predicate \\\n",
- "46 aio:Amplification_Bias Amplification Bias rdfs:subClassOf \n",
- "47 aio:Amplification_Bias Amplification Bias rdfs:subClassOf \n",
- "48 aio:Amplification_Bias Amplification Bias rdfs:subClassOf \n",
- "49 aio:Amplification_Bias Amplification Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "46 None aio:Amplification_Bias Amplification Bias \n",
- "47 None aio:Bias Bias \n",
- "48 None aio:Computational_Bias Computational Bias \n",
- "49 None aio:Processing_Bias Processing Bias \n",
- "parent aio:Amplification_Bias\n",
- "aio:Anchoring_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "50 aio:Anchoring_Bias Anchoring Bias rdfs:subClassOf None \n",
- "51 aio:Anchoring_Bias Anchoring Bias rdfs:subClassOf None \n",
- "52 aio:Anchoring_Bias Anchoring Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "50 aio:Anchoring_Bias Anchoring Bias \n",
- "51 aio:Bias Bias \n",
- "52 aio:Individual_Bias Individual Bias \n",
- "parent aio:Anchoring_Bias\n",
- "aio:Anchoring_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "50 aio:Anchoring_Bias Anchoring Bias rdfs:subClassOf None \n",
- "51 aio:Anchoring_Bias Anchoring Bias rdfs:subClassOf None \n",
- "52 aio:Anchoring_Bias Anchoring Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "50 aio:Anchoring_Bias Anchoring Bias \n",
- "51 aio:Bias Bias \n",
- "52 aio:Individual_Bias Individual Bias \n",
- "parent aio:Anchoring_Bias\n",
- "aio:Anchoring_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "50 aio:Anchoring_Bias Anchoring Bias rdfs:subClassOf None \n",
- "51 aio:Anchoring_Bias Anchoring Bias rdfs:subClassOf None \n",
- "52 aio:Anchoring_Bias Anchoring Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "50 aio:Anchoring_Bias Anchoring Bias \n",
- "51 aio:Bias Bias \n",
- "52 aio:Individual_Bias Individual Bias \n",
- "parent aio:Anchoring_Bias\n",
- "aio:Annotator_Reporting_Bias\n",
- " subject subject_label predicate \\\n",
- "53 aio:Annotator_Reporting_Bias Annotator Reporting Bias rdfs:subClassOf \n",
- "54 aio:Annotator_Reporting_Bias Annotator Reporting Bias rdfs:subClassOf \n",
- "55 aio:Annotator_Reporting_Bias Annotator Reporting Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "53 None aio:Annotator_Reporting_Bias Annotator Reporting Bias \n",
- "54 None aio:Bias Bias \n",
- "55 None aio:Individual_Bias Individual Bias \n",
- "parent aio:Annotator_Reporting_Bias\n",
- "aio:Annotator_Reporting_Bias\n",
- " subject subject_label predicate \\\n",
- "53 aio:Annotator_Reporting_Bias Annotator Reporting Bias rdfs:subClassOf \n",
- "54 aio:Annotator_Reporting_Bias Annotator Reporting Bias rdfs:subClassOf \n",
- "55 aio:Annotator_Reporting_Bias Annotator Reporting Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "53 None aio:Annotator_Reporting_Bias Annotator Reporting Bias \n",
- "54 None aio:Bias Bias \n",
- "55 None aio:Individual_Bias Individual Bias \n",
- "parent aio:Annotator_Reporting_Bias\n",
- "aio:Annotator_Reporting_Bias\n",
- " subject subject_label predicate \\\n",
- "53 aio:Annotator_Reporting_Bias Annotator Reporting Bias rdfs:subClassOf \n",
- "54 aio:Annotator_Reporting_Bias Annotator Reporting Bias rdfs:subClassOf \n",
- "55 aio:Annotator_Reporting_Bias Annotator Reporting Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "53 None aio:Annotator_Reporting_Bias Annotator Reporting Bias \n",
- "54 None aio:Bias Bias \n",
- "55 None aio:Individual_Bias Individual Bias \n",
- "parent aio:Annotator_Reporting_Bias\n",
- "aio:Association_Rule_Learning\n",
- " subject subject_label predicate \\\n",
- "56 aio:Association_Rule_Learning Association Rule Learning rdfs:subClassOf \n",
- "57 aio:Association_Rule_Learning Association Rule Learning rdfs:subClassOf \n",
- "58 aio:Association_Rule_Learning Association Rule Learning rdfs:subClassOf \n",
- "59 aio:Association_Rule_Learning Association Rule Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "56 None aio:Association_Rule_Learning Association Rule Learning \n",
- "57 None aio:Machine_Learning Machine Learning \n",
- "58 None aio:Method Method \n",
- "59 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Association_Rule_Learning\n",
- "aio:Association_Rule_Learning\n",
- " subject subject_label predicate \\\n",
- "56 aio:Association_Rule_Learning Association Rule Learning rdfs:subClassOf \n",
- "57 aio:Association_Rule_Learning Association Rule Learning rdfs:subClassOf \n",
- "58 aio:Association_Rule_Learning Association Rule Learning rdfs:subClassOf \n",
- "59 aio:Association_Rule_Learning Association Rule Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "56 None aio:Association_Rule_Learning Association Rule Learning \n",
- "57 None aio:Machine_Learning Machine Learning \n",
- "58 None aio:Method Method \n",
- "59 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Association_Rule_Learning\n",
- "aio:Association_Rule_Learning\n",
- " subject subject_label predicate \\\n",
- "56 aio:Association_Rule_Learning Association Rule Learning rdfs:subClassOf \n",
- "57 aio:Association_Rule_Learning Association Rule Learning rdfs:subClassOf \n",
- "58 aio:Association_Rule_Learning Association Rule Learning rdfs:subClassOf \n",
- "59 aio:Association_Rule_Learning Association Rule Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "56 None aio:Association_Rule_Learning Association Rule Learning \n",
- "57 None aio:Machine_Learning Machine Learning \n",
- "58 None aio:Method Method \n",
- "59 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Association_Rule_Learning\n",
- "aio:Association_Rule_Learning\n",
- " subject subject_label predicate \\\n",
- "56 aio:Association_Rule_Learning Association Rule Learning rdfs:subClassOf \n",
- "57 aio:Association_Rule_Learning Association Rule Learning rdfs:subClassOf \n",
- "58 aio:Association_Rule_Learning Association Rule Learning rdfs:subClassOf \n",
- "59 aio:Association_Rule_Learning Association Rule Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "56 None aio:Association_Rule_Learning Association Rule Learning \n",
- "57 None aio:Machine_Learning Machine Learning \n",
- "58 None aio:Method Method \n",
- "59 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Association_Rule_Learning\n",
- "aio:Attention_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "60 aio:Attention_Layer Attention Layer rdfs:subClassOf None \n",
- "61 aio:Attention_Layer Attention Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "60 aio:Attention_Layer Attention Layer \n",
- "61 aio:Layer Layer \n",
- "parent aio:Attention_Layer\n",
- "aio:Attention_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "60 aio:Attention_Layer Attention Layer rdfs:subClassOf None \n",
- "61 aio:Attention_Layer Attention Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "60 aio:Attention_Layer Attention Layer \n",
- "61 aio:Layer Layer \n",
- "parent aio:Attention_Layer\n",
- "aio:Automation_Complacency_Bias\n",
- " subject subject_label \\\n",
- "62 aio:Automation_Complacency_Bias Automation Complacency Bias \n",
- "63 aio:Automation_Complacency_Bias Automation Complacency Bias \n",
- "64 aio:Automation_Complacency_Bias Automation Complacency Bias \n",
- "\n",
- " predicate predicate_label object \\\n",
- "62 rdfs:subClassOf None aio:Automation_Complacency_Bias \n",
- "63 rdfs:subClassOf None aio:Bias \n",
- "64 rdfs:subClassOf None aio:Individual_Bias \n",
- "\n",
- " object_label \n",
- "62 Automation Complacency Bias \n",
- "63 Bias \n",
- "64 Individual Bias \n",
- "parent aio:Automation_Complacency_Bias\n",
- "aio:Automation_Complacency_Bias\n",
- " subject subject_label \\\n",
- "62 aio:Automation_Complacency_Bias Automation Complacency Bias \n",
- "63 aio:Automation_Complacency_Bias Automation Complacency Bias \n",
- "64 aio:Automation_Complacency_Bias Automation Complacency Bias \n",
- "\n",
- " predicate predicate_label object \\\n",
- "62 rdfs:subClassOf None aio:Automation_Complacency_Bias \n",
- "63 rdfs:subClassOf None aio:Bias \n",
- "64 rdfs:subClassOf None aio:Individual_Bias \n",
- "\n",
- " object_label \n",
- "62 Automation Complacency Bias \n",
- "63 Bias \n",
- "64 Individual Bias \n",
- "parent aio:Automation_Complacency_Bias\n",
- "aio:Automation_Complacency_Bias\n",
- " subject subject_label \\\n",
- "62 aio:Automation_Complacency_Bias Automation Complacency Bias \n",
- "63 aio:Automation_Complacency_Bias Automation Complacency Bias \n",
- "64 aio:Automation_Complacency_Bias Automation Complacency Bias \n",
- "\n",
- " predicate predicate_label object \\\n",
- "62 rdfs:subClassOf None aio:Automation_Complacency_Bias \n",
- "63 rdfs:subClassOf None aio:Bias \n",
- "64 rdfs:subClassOf None aio:Individual_Bias \n",
- "\n",
- " object_label \n",
- "62 Automation Complacency Bias \n",
- "63 Bias \n",
- "64 Individual Bias \n",
- "parent aio:Automation_Complacency_Bias\n",
- "aio:Availability_Heuristic_Bias\n",
- " subject subject_label \\\n",
- "65 aio:Availability_Heuristic_Bias Availability Heuristic Bias \n",
- "66 aio:Availability_Heuristic_Bias Availability Heuristic Bias \n",
- "67 aio:Availability_Heuristic_Bias Availability Heuristic Bias \n",
- "\n",
- " predicate predicate_label object \\\n",
- "65 rdfs:subClassOf None aio:Availability_Heuristic_Bias \n",
- "66 rdfs:subClassOf None aio:Bias \n",
- "67 rdfs:subClassOf None aio:Individual_Bias \n",
- "\n",
- " object_label \n",
- "65 Availability Heuristic Bias \n",
- "66 Bias \n",
- "67 Individual Bias \n",
- "parent aio:Availability_Heuristic_Bias\n",
- "aio:Availability_Heuristic_Bias\n",
- " subject subject_label \\\n",
- "65 aio:Availability_Heuristic_Bias Availability Heuristic Bias \n",
- "66 aio:Availability_Heuristic_Bias Availability Heuristic Bias \n",
- "67 aio:Availability_Heuristic_Bias Availability Heuristic Bias \n",
- "\n",
- " predicate predicate_label object \\\n",
- "65 rdfs:subClassOf None aio:Availability_Heuristic_Bias \n",
- "66 rdfs:subClassOf None aio:Bias \n",
- "67 rdfs:subClassOf None aio:Individual_Bias \n",
- "\n",
- " object_label \n",
- "65 Availability Heuristic Bias \n",
- "66 Bias \n",
- "67 Individual Bias \n",
- "parent aio:Availability_Heuristic_Bias\n",
- "aio:Availability_Heuristic_Bias\n",
- " subject subject_label \\\n",
- "65 aio:Availability_Heuristic_Bias Availability Heuristic Bias \n",
- "66 aio:Availability_Heuristic_Bias Availability Heuristic Bias \n",
- "67 aio:Availability_Heuristic_Bias Availability Heuristic Bias \n",
- "\n",
- " predicate predicate_label object \\\n",
- "65 rdfs:subClassOf None aio:Availability_Heuristic_Bias \n",
- "66 rdfs:subClassOf None aio:Bias \n",
- "67 rdfs:subClassOf None aio:Individual_Bias \n",
- "\n",
- " object_label \n",
- "65 Availability Heuristic Bias \n",
- "66 Bias \n",
- "67 Individual Bias \n",
- "parent aio:Availability_Heuristic_Bias\n",
- "aio:AveragePooling1D_Layer\n",
- " subject subject_label predicate \\\n",
- "68 aio:AveragePooling1D_Layer AveragePooling1D Layer rdfs:subClassOf \n",
- "69 aio:AveragePooling1D_Layer AveragePooling1D Layer rdfs:subClassOf \n",
- "70 aio:AveragePooling1D_Layer AveragePooling1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "68 None aio:AveragePooling1D_Layer AveragePooling1D Layer \n",
- "69 None aio:Layer Layer \n",
- "70 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AveragePooling1D_Layer\n",
- "aio:AveragePooling1D_Layer\n",
- " subject subject_label predicate \\\n",
- "68 aio:AveragePooling1D_Layer AveragePooling1D Layer rdfs:subClassOf \n",
- "69 aio:AveragePooling1D_Layer AveragePooling1D Layer rdfs:subClassOf \n",
- "70 aio:AveragePooling1D_Layer AveragePooling1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "68 None aio:AveragePooling1D_Layer AveragePooling1D Layer \n",
- "69 None aio:Layer Layer \n",
- "70 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AveragePooling1D_Layer\n",
- "aio:AveragePooling1D_Layer\n",
- " subject subject_label predicate \\\n",
- "68 aio:AveragePooling1D_Layer AveragePooling1D Layer rdfs:subClassOf \n",
- "69 aio:AveragePooling1D_Layer AveragePooling1D Layer rdfs:subClassOf \n",
- "70 aio:AveragePooling1D_Layer AveragePooling1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "68 None aio:AveragePooling1D_Layer AveragePooling1D Layer \n",
- "69 None aio:Layer Layer \n",
- "70 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AveragePooling1D_Layer\n",
- "aio:AveragePooling2D_Layer\n",
- " subject subject_label predicate \\\n",
- "71 aio:AveragePooling2D_Layer AveragePooling2D Layer rdfs:subClassOf \n",
- "72 aio:AveragePooling2D_Layer AveragePooling2D Layer rdfs:subClassOf \n",
- "73 aio:AveragePooling2D_Layer AveragePooling2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "71 None aio:AveragePooling2D_Layer AveragePooling2D Layer \n",
- "72 None aio:Layer Layer \n",
- "73 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AveragePooling2D_Layer\n",
- "aio:AveragePooling2D_Layer\n",
- " subject subject_label predicate \\\n",
- "71 aio:AveragePooling2D_Layer AveragePooling2D Layer rdfs:subClassOf \n",
- "72 aio:AveragePooling2D_Layer AveragePooling2D Layer rdfs:subClassOf \n",
- "73 aio:AveragePooling2D_Layer AveragePooling2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "71 None aio:AveragePooling2D_Layer AveragePooling2D Layer \n",
- "72 None aio:Layer Layer \n",
- "73 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AveragePooling2D_Layer\n",
- "aio:AveragePooling2D_Layer\n",
- " subject subject_label predicate \\\n",
- "71 aio:AveragePooling2D_Layer AveragePooling2D Layer rdfs:subClassOf \n",
- "72 aio:AveragePooling2D_Layer AveragePooling2D Layer rdfs:subClassOf \n",
- "73 aio:AveragePooling2D_Layer AveragePooling2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "71 None aio:AveragePooling2D_Layer AveragePooling2D Layer \n",
- "72 None aio:Layer Layer \n",
- "73 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AveragePooling2D_Layer\n",
- "aio:AveragePooling3D_Layer\n",
- " subject subject_label predicate \\\n",
- "74 aio:AveragePooling3D_Layer AveragePooling3D Layer rdfs:subClassOf \n",
- "75 aio:AveragePooling3D_Layer AveragePooling3D Layer rdfs:subClassOf \n",
- "76 aio:AveragePooling3D_Layer AveragePooling3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "74 None aio:AveragePooling3D_Layer AveragePooling3D Layer \n",
- "75 None aio:Layer Layer \n",
- "76 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AveragePooling3D_Layer\n",
- "aio:AveragePooling3D_Layer\n",
- " subject subject_label predicate \\\n",
- "74 aio:AveragePooling3D_Layer AveragePooling3D Layer rdfs:subClassOf \n",
- "75 aio:AveragePooling3D_Layer AveragePooling3D Layer rdfs:subClassOf \n",
- "76 aio:AveragePooling3D_Layer AveragePooling3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "74 None aio:AveragePooling3D_Layer AveragePooling3D Layer \n",
- "75 None aio:Layer Layer \n",
- "76 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AveragePooling3D_Layer\n",
- "aio:AveragePooling3D_Layer\n",
- " subject subject_label predicate \\\n",
- "74 aio:AveragePooling3D_Layer AveragePooling3D Layer rdfs:subClassOf \n",
- "75 aio:AveragePooling3D_Layer AveragePooling3D Layer rdfs:subClassOf \n",
- "76 aio:AveragePooling3D_Layer AveragePooling3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "74 None aio:AveragePooling3D_Layer AveragePooling3D Layer \n",
- "75 None aio:Layer Layer \n",
- "76 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AveragePooling3D_Layer\n",
- "aio:Average_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "77 aio:Average_Layer Average Layer rdfs:subClassOf None \n",
- "78 aio:Average_Layer Average Layer rdfs:subClassOf None \n",
- "79 aio:Average_Layer Average Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "77 aio:Average_Layer Average Layer \n",
- "78 aio:Layer Layer \n",
- "79 aio:Merging_Layer Merging Layer \n",
- "parent aio:Average_Layer\n",
- "aio:Average_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "77 aio:Average_Layer Average Layer rdfs:subClassOf None \n",
- "78 aio:Average_Layer Average Layer rdfs:subClassOf None \n",
- "79 aio:Average_Layer Average Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "77 aio:Average_Layer Average Layer \n",
- "78 aio:Layer Layer \n",
- "79 aio:Merging_Layer Merging Layer \n",
- "parent aio:Average_Layer\n",
- "aio:Average_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "77 aio:Average_Layer Average Layer rdfs:subClassOf None \n",
- "78 aio:Average_Layer Average Layer rdfs:subClassOf None \n",
- "79 aio:Average_Layer Average Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "77 aio:Average_Layer Average Layer \n",
- "78 aio:Layer Layer \n",
- "79 aio:Merging_Layer Merging Layer \n",
- "parent aio:Average_Layer\n",
- "aio:AvgPool1D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "80 aio:AvgPool1D_Layer AvgPool1D Layer rdfs:subClassOf None \n",
- "81 aio:AvgPool1D_Layer AvgPool1D Layer rdfs:subClassOf None \n",
- "82 aio:AvgPool1D_Layer AvgPool1D Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "80 aio:AvgPool1D_Layer AvgPool1D Layer \n",
- "81 aio:Layer Layer \n",
- "82 aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AvgPool1D_Layer\n",
- "aio:AvgPool1D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "80 aio:AvgPool1D_Layer AvgPool1D Layer rdfs:subClassOf None \n",
- "81 aio:AvgPool1D_Layer AvgPool1D Layer rdfs:subClassOf None \n",
- "82 aio:AvgPool1D_Layer AvgPool1D Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "80 aio:AvgPool1D_Layer AvgPool1D Layer \n",
- "81 aio:Layer Layer \n",
- "82 aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AvgPool1D_Layer\n",
- "aio:AvgPool1D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "80 aio:AvgPool1D_Layer AvgPool1D Layer rdfs:subClassOf None \n",
- "81 aio:AvgPool1D_Layer AvgPool1D Layer rdfs:subClassOf None \n",
- "82 aio:AvgPool1D_Layer AvgPool1D Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "80 aio:AvgPool1D_Layer AvgPool1D Layer \n",
- "81 aio:Layer Layer \n",
- "82 aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AvgPool1D_Layer\n",
- "aio:AvgPool2D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "83 aio:AvgPool2D_Layer AvgPool2D Layer rdfs:subClassOf None \n",
- "84 aio:AvgPool2D_Layer AvgPool2D Layer rdfs:subClassOf None \n",
- "85 aio:AvgPool2D_Layer AvgPool2D Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "83 aio:AvgPool2D_Layer AvgPool2D Layer \n",
- "84 aio:Layer Layer \n",
- "85 aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AvgPool2D_Layer\n",
- "aio:AvgPool2D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "83 aio:AvgPool2D_Layer AvgPool2D Layer rdfs:subClassOf None \n",
- "84 aio:AvgPool2D_Layer AvgPool2D Layer rdfs:subClassOf None \n",
- "85 aio:AvgPool2D_Layer AvgPool2D Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "83 aio:AvgPool2D_Layer AvgPool2D Layer \n",
- "84 aio:Layer Layer \n",
- "85 aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AvgPool2D_Layer\n",
- "aio:AvgPool2D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "83 aio:AvgPool2D_Layer AvgPool2D Layer rdfs:subClassOf None \n",
- "84 aio:AvgPool2D_Layer AvgPool2D Layer rdfs:subClassOf None \n",
- "85 aio:AvgPool2D_Layer AvgPool2D Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "83 aio:AvgPool2D_Layer AvgPool2D Layer \n",
- "84 aio:Layer Layer \n",
- "85 aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AvgPool2D_Layer\n",
- "aio:AvgPool3D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "86 aio:AvgPool3D_Layer AvgPool3D Layer rdfs:subClassOf None \n",
- "87 aio:AvgPool3D_Layer AvgPool3D Layer rdfs:subClassOf None \n",
- "88 aio:AvgPool3D_Layer AvgPool3D Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "86 aio:AvgPool3D_Layer AvgPool3D Layer \n",
- "87 aio:Layer Layer \n",
- "88 aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AvgPool3D_Layer\n",
- "aio:AvgPool3D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "86 aio:AvgPool3D_Layer AvgPool3D Layer rdfs:subClassOf None \n",
- "87 aio:AvgPool3D_Layer AvgPool3D Layer rdfs:subClassOf None \n",
- "88 aio:AvgPool3D_Layer AvgPool3D Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "86 aio:AvgPool3D_Layer AvgPool3D Layer \n",
- "87 aio:Layer Layer \n",
- "88 aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AvgPool3D_Layer\n",
- "aio:AvgPool3D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "86 aio:AvgPool3D_Layer AvgPool3D Layer rdfs:subClassOf None \n",
- "87 aio:AvgPool3D_Layer AvgPool3D Layer rdfs:subClassOf None \n",
- "88 aio:AvgPool3D_Layer AvgPool3D Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "86 aio:AvgPool3D_Layer AvgPool3D Layer \n",
- "87 aio:Layer Layer \n",
- "88 aio:Pooling_Layer Pooling Layer \n",
- "parent aio:AvgPool3D_Layer\n",
- "aio:BM\n",
- " subject subject_label predicate predicate_label \\\n",
- "89 aio:BM Boltzmann Machine Network rdfs:subClassOf None \n",
- "90 aio:BM Boltzmann Machine Network rdfs:subClassOf None \n",
- "91 aio:BM Boltzmann Machine Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "89 aio:BM Boltzmann Machine Network \n",
- "90 aio:Network Network \n",
- "91 aio:SCN Symmetrically Connected Network \n",
- "parent aio:BM\n",
- "aio:BM\n",
- " subject subject_label predicate predicate_label \\\n",
- "89 aio:BM Boltzmann Machine Network rdfs:subClassOf None \n",
- "90 aio:BM Boltzmann Machine Network rdfs:subClassOf None \n",
- "91 aio:BM Boltzmann Machine Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "89 aio:BM Boltzmann Machine Network \n",
- "90 aio:Network Network \n",
- "91 aio:SCN Symmetrically Connected Network \n",
- "parent aio:BM\n",
- "aio:BM\n",
- " subject subject_label predicate predicate_label \\\n",
- "89 aio:BM Boltzmann Machine Network rdfs:subClassOf None \n",
- "90 aio:BM Boltzmann Machine Network rdfs:subClassOf None \n",
- "91 aio:BM Boltzmann Machine Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "89 aio:BM Boltzmann Machine Network \n",
- "90 aio:Network Network \n",
- "91 aio:SCN Symmetrically Connected Network \n",
- "parent aio:BM\n",
- "aio:BatchNorm1D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "92 aio:BatchNorm1D_Layer BatchNorm1D Layer rdfs:subClassOf None \n",
- "93 aio:BatchNorm1D_Layer BatchNorm1D Layer rdfs:subClassOf None \n",
- "94 aio:BatchNorm1D_Layer BatchNorm1D Layer rdfs:subClassOf None \n",
- "95 aio:BatchNorm1D_Layer BatchNorm1D Layer rdfs:subClassOf None \n",
- "96 aio:BatchNorm1D_Layer BatchNorm1D Layer rdfs:subClassOf None \n",
- "\n",
- " object \\\n",
- "92 aio:BatchNorm1D_Layer \n",
- "93 aio:BatchNormalization_Layer \n",
- "94 aio:Layer \n",
- "95 aio:Normalization_Layer \n",
- "96 aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "92 BatchNorm1D Layer \n",
- "93 BatchNormalization Layer \n",
- "94 Layer \n",
- "95 Normalization Layer \n",
- "96 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNorm1D_Layer\n",
- "aio:BatchNorm1D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "92 aio:BatchNorm1D_Layer BatchNorm1D Layer rdfs:subClassOf None \n",
- "93 aio:BatchNorm1D_Layer BatchNorm1D Layer rdfs:subClassOf None \n",
- "94 aio:BatchNorm1D_Layer BatchNorm1D Layer rdfs:subClassOf None \n",
- "95 aio:BatchNorm1D_Layer BatchNorm1D Layer rdfs:subClassOf None \n",
- "96 aio:BatchNorm1D_Layer BatchNorm1D Layer rdfs:subClassOf None \n",
- "\n",
- " object \\\n",
- "92 aio:BatchNorm1D_Layer \n",
- "93 aio:BatchNormalization_Layer \n",
- "94 aio:Layer \n",
- "95 aio:Normalization_Layer \n",
- "96 aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "92 BatchNorm1D Layer \n",
- "93 BatchNormalization Layer \n",
- "94 Layer \n",
- "95 Normalization Layer \n",
- "96 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNorm1D_Layer\n",
- "aio:BatchNorm1D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "92 aio:BatchNorm1D_Layer BatchNorm1D Layer rdfs:subClassOf None \n",
- "93 aio:BatchNorm1D_Layer BatchNorm1D Layer rdfs:subClassOf None \n",
- "94 aio:BatchNorm1D_Layer BatchNorm1D Layer rdfs:subClassOf None \n",
- "95 aio:BatchNorm1D_Layer BatchNorm1D Layer rdfs:subClassOf None \n",
- "96 aio:BatchNorm1D_Layer BatchNorm1D Layer rdfs:subClassOf None \n",
- "\n",
- " object \\\n",
- "92 aio:BatchNorm1D_Layer \n",
- "93 aio:BatchNormalization_Layer \n",
- "94 aio:Layer \n",
- "95 aio:Normalization_Layer \n",
- "96 aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "92 BatchNorm1D Layer \n",
- "93 BatchNormalization Layer \n",
- "94 Layer \n",
- "95 Normalization Layer \n",
- "96 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNorm1D_Layer\n",
- "aio:BatchNorm1D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "92 aio:BatchNorm1D_Layer BatchNorm1D Layer rdfs:subClassOf None \n",
- "93 aio:BatchNorm1D_Layer BatchNorm1D Layer rdfs:subClassOf None \n",
- "94 aio:BatchNorm1D_Layer BatchNorm1D Layer rdfs:subClassOf None \n",
- "95 aio:BatchNorm1D_Layer BatchNorm1D Layer rdfs:subClassOf None \n",
- "96 aio:BatchNorm1D_Layer BatchNorm1D Layer rdfs:subClassOf None \n",
- "\n",
- " object \\\n",
- "92 aio:BatchNorm1D_Layer \n",
- "93 aio:BatchNormalization_Layer \n",
- "94 aio:Layer \n",
- "95 aio:Normalization_Layer \n",
- "96 aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "92 BatchNorm1D Layer \n",
- "93 BatchNormalization Layer \n",
- "94 Layer \n",
- "95 Normalization Layer \n",
- "96 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNorm1D_Layer\n",
- "aio:BatchNorm1D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "92 aio:BatchNorm1D_Layer BatchNorm1D Layer rdfs:subClassOf None \n",
- "93 aio:BatchNorm1D_Layer BatchNorm1D Layer rdfs:subClassOf None \n",
- "94 aio:BatchNorm1D_Layer BatchNorm1D Layer rdfs:subClassOf None \n",
- "95 aio:BatchNorm1D_Layer BatchNorm1D Layer rdfs:subClassOf None \n",
- "96 aio:BatchNorm1D_Layer BatchNorm1D Layer rdfs:subClassOf None \n",
- "\n",
- " object \\\n",
- "92 aio:BatchNorm1D_Layer \n",
- "93 aio:BatchNormalization_Layer \n",
- "94 aio:Layer \n",
- "95 aio:Normalization_Layer \n",
- "96 aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "92 BatchNorm1D Layer \n",
- "93 BatchNormalization Layer \n",
- "94 Layer \n",
- "95 Normalization Layer \n",
- "96 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNorm1D_Layer\n",
- "aio:BatchNorm2D_Layer\n",
- " subject subject_label predicate \\\n",
- "97 aio:BatchNorm2D_Layer BatchNorm2D Layer rdfs:subClassOf \n",
- "98 aio:BatchNorm2D_Layer BatchNorm2D Layer rdfs:subClassOf \n",
- "99 aio:BatchNorm2D_Layer BatchNorm2D Layer rdfs:subClassOf \n",
- "100 aio:BatchNorm2D_Layer BatchNorm2D Layer rdfs:subClassOf \n",
- "101 aio:BatchNorm2D_Layer BatchNorm2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "97 None aio:BatchNorm2D_Layer \n",
- "98 None aio:BatchNormalization_Layer \n",
- "99 None aio:Layer \n",
- "100 None aio:Normalization_Layer \n",
- "101 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "97 BatchNorm2D Layer \n",
- "98 BatchNormalization Layer \n",
- "99 Layer \n",
- "100 Normalization Layer \n",
- "101 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNorm2D_Layer\n",
- "aio:BatchNorm2D_Layer\n",
- " subject subject_label predicate \\\n",
- "97 aio:BatchNorm2D_Layer BatchNorm2D Layer rdfs:subClassOf \n",
- "98 aio:BatchNorm2D_Layer BatchNorm2D Layer rdfs:subClassOf \n",
- "99 aio:BatchNorm2D_Layer BatchNorm2D Layer rdfs:subClassOf \n",
- "100 aio:BatchNorm2D_Layer BatchNorm2D Layer rdfs:subClassOf \n",
- "101 aio:BatchNorm2D_Layer BatchNorm2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "97 None aio:BatchNorm2D_Layer \n",
- "98 None aio:BatchNormalization_Layer \n",
- "99 None aio:Layer \n",
- "100 None aio:Normalization_Layer \n",
- "101 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "97 BatchNorm2D Layer \n",
- "98 BatchNormalization Layer \n",
- "99 Layer \n",
- "100 Normalization Layer \n",
- "101 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNorm2D_Layer\n",
- "aio:BatchNorm2D_Layer\n",
- " subject subject_label predicate \\\n",
- "97 aio:BatchNorm2D_Layer BatchNorm2D Layer rdfs:subClassOf \n",
- "98 aio:BatchNorm2D_Layer BatchNorm2D Layer rdfs:subClassOf \n",
- "99 aio:BatchNorm2D_Layer BatchNorm2D Layer rdfs:subClassOf \n",
- "100 aio:BatchNorm2D_Layer BatchNorm2D Layer rdfs:subClassOf \n",
- "101 aio:BatchNorm2D_Layer BatchNorm2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "97 None aio:BatchNorm2D_Layer \n",
- "98 None aio:BatchNormalization_Layer \n",
- "99 None aio:Layer \n",
- "100 None aio:Normalization_Layer \n",
- "101 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "97 BatchNorm2D Layer \n",
- "98 BatchNormalization Layer \n",
- "99 Layer \n",
- "100 Normalization Layer \n",
- "101 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNorm2D_Layer\n",
- "aio:BatchNorm2D_Layer\n",
- " subject subject_label predicate \\\n",
- "97 aio:BatchNorm2D_Layer BatchNorm2D Layer rdfs:subClassOf \n",
- "98 aio:BatchNorm2D_Layer BatchNorm2D Layer rdfs:subClassOf \n",
- "99 aio:BatchNorm2D_Layer BatchNorm2D Layer rdfs:subClassOf \n",
- "100 aio:BatchNorm2D_Layer BatchNorm2D Layer rdfs:subClassOf \n",
- "101 aio:BatchNorm2D_Layer BatchNorm2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "97 None aio:BatchNorm2D_Layer \n",
- "98 None aio:BatchNormalization_Layer \n",
- "99 None aio:Layer \n",
- "100 None aio:Normalization_Layer \n",
- "101 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "97 BatchNorm2D Layer \n",
- "98 BatchNormalization Layer \n",
- "99 Layer \n",
- "100 Normalization Layer \n",
- "101 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNorm2D_Layer\n",
- "aio:BatchNorm2D_Layer\n",
- " subject subject_label predicate \\\n",
- "97 aio:BatchNorm2D_Layer BatchNorm2D Layer rdfs:subClassOf \n",
- "98 aio:BatchNorm2D_Layer BatchNorm2D Layer rdfs:subClassOf \n",
- "99 aio:BatchNorm2D_Layer BatchNorm2D Layer rdfs:subClassOf \n",
- "100 aio:BatchNorm2D_Layer BatchNorm2D Layer rdfs:subClassOf \n",
- "101 aio:BatchNorm2D_Layer BatchNorm2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "97 None aio:BatchNorm2D_Layer \n",
- "98 None aio:BatchNormalization_Layer \n",
- "99 None aio:Layer \n",
- "100 None aio:Normalization_Layer \n",
- "101 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "97 BatchNorm2D Layer \n",
- "98 BatchNormalization Layer \n",
- "99 Layer \n",
- "100 Normalization Layer \n",
- "101 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNorm2D_Layer\n",
- "aio:BatchNorm3D_Layer\n",
- " subject subject_label predicate \\\n",
- "102 aio:BatchNorm3D_Layer BatchNorm3D Layer rdfs:subClassOf \n",
- "103 aio:BatchNorm3D_Layer BatchNorm3D Layer rdfs:subClassOf \n",
- "104 aio:BatchNorm3D_Layer BatchNorm3D Layer rdfs:subClassOf \n",
- "105 aio:BatchNorm3D_Layer BatchNorm3D Layer rdfs:subClassOf \n",
- "106 aio:BatchNorm3D_Layer BatchNorm3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "102 None aio:BatchNorm3D_Layer \n",
- "103 None aio:BatchNormalization_Layer \n",
- "104 None aio:Layer \n",
- "105 None aio:Normalization_Layer \n",
- "106 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "102 BatchNorm3D Layer \n",
- "103 BatchNormalization Layer \n",
- "104 Layer \n",
- "105 Normalization Layer \n",
- "106 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNorm3D_Layer\n",
- "aio:BatchNorm3D_Layer\n",
- " subject subject_label predicate \\\n",
- "102 aio:BatchNorm3D_Layer BatchNorm3D Layer rdfs:subClassOf \n",
- "103 aio:BatchNorm3D_Layer BatchNorm3D Layer rdfs:subClassOf \n",
- "104 aio:BatchNorm3D_Layer BatchNorm3D Layer rdfs:subClassOf \n",
- "105 aio:BatchNorm3D_Layer BatchNorm3D Layer rdfs:subClassOf \n",
- "106 aio:BatchNorm3D_Layer BatchNorm3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "102 None aio:BatchNorm3D_Layer \n",
- "103 None aio:BatchNormalization_Layer \n",
- "104 None aio:Layer \n",
- "105 None aio:Normalization_Layer \n",
- "106 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "102 BatchNorm3D Layer \n",
- "103 BatchNormalization Layer \n",
- "104 Layer \n",
- "105 Normalization Layer \n",
- "106 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNorm3D_Layer\n",
- "aio:BatchNorm3D_Layer\n",
- " subject subject_label predicate \\\n",
- "102 aio:BatchNorm3D_Layer BatchNorm3D Layer rdfs:subClassOf \n",
- "103 aio:BatchNorm3D_Layer BatchNorm3D Layer rdfs:subClassOf \n",
- "104 aio:BatchNorm3D_Layer BatchNorm3D Layer rdfs:subClassOf \n",
- "105 aio:BatchNorm3D_Layer BatchNorm3D Layer rdfs:subClassOf \n",
- "106 aio:BatchNorm3D_Layer BatchNorm3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "102 None aio:BatchNorm3D_Layer \n",
- "103 None aio:BatchNormalization_Layer \n",
- "104 None aio:Layer \n",
- "105 None aio:Normalization_Layer \n",
- "106 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "102 BatchNorm3D Layer \n",
- "103 BatchNormalization Layer \n",
- "104 Layer \n",
- "105 Normalization Layer \n",
- "106 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNorm3D_Layer\n",
- "aio:BatchNorm3D_Layer\n",
- " subject subject_label predicate \\\n",
- "102 aio:BatchNorm3D_Layer BatchNorm3D Layer rdfs:subClassOf \n",
- "103 aio:BatchNorm3D_Layer BatchNorm3D Layer rdfs:subClassOf \n",
- "104 aio:BatchNorm3D_Layer BatchNorm3D Layer rdfs:subClassOf \n",
- "105 aio:BatchNorm3D_Layer BatchNorm3D Layer rdfs:subClassOf \n",
- "106 aio:BatchNorm3D_Layer BatchNorm3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "102 None aio:BatchNorm3D_Layer \n",
- "103 None aio:BatchNormalization_Layer \n",
- "104 None aio:Layer \n",
- "105 None aio:Normalization_Layer \n",
- "106 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "102 BatchNorm3D Layer \n",
- "103 BatchNormalization Layer \n",
- "104 Layer \n",
- "105 Normalization Layer \n",
- "106 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNorm3D_Layer\n",
- "aio:BatchNorm3D_Layer\n",
- " subject subject_label predicate \\\n",
- "102 aio:BatchNorm3D_Layer BatchNorm3D Layer rdfs:subClassOf \n",
- "103 aio:BatchNorm3D_Layer BatchNorm3D Layer rdfs:subClassOf \n",
- "104 aio:BatchNorm3D_Layer BatchNorm3D Layer rdfs:subClassOf \n",
- "105 aio:BatchNorm3D_Layer BatchNorm3D Layer rdfs:subClassOf \n",
- "106 aio:BatchNorm3D_Layer BatchNorm3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "102 None aio:BatchNorm3D_Layer \n",
- "103 None aio:BatchNormalization_Layer \n",
- "104 None aio:Layer \n",
- "105 None aio:Normalization_Layer \n",
- "106 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "102 BatchNorm3D Layer \n",
- "103 BatchNormalization Layer \n",
- "104 Layer \n",
- "105 Normalization Layer \n",
- "106 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNorm3D_Layer\n",
- "aio:BatchNormalization_Layer\n",
- " subject subject_label predicate \\\n",
- "107 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "108 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "109 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "110 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "107 None aio:BatchNormalization_Layer \n",
- "108 None aio:Layer \n",
- "109 None aio:Normalization_Layer \n",
- "110 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "107 BatchNormalization Layer \n",
- "108 Layer \n",
- "109 Normalization Layer \n",
- "110 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:BatchNormalization_Layer\n",
- " subject subject_label predicate \\\n",
- "107 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "108 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "109 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "110 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "107 None aio:BatchNormalization_Layer \n",
- "108 None aio:Layer \n",
- "109 None aio:Normalization_Layer \n",
- "110 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "107 BatchNormalization Layer \n",
- "108 Layer \n",
- "109 Normalization Layer \n",
- "110 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:BatchNormalization_Layer\n",
- " subject subject_label predicate \\\n",
- "107 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "108 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "109 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "110 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "107 None aio:BatchNormalization_Layer \n",
- "108 None aio:Layer \n",
- "109 None aio:Normalization_Layer \n",
- "110 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "107 BatchNormalization Layer \n",
- "108 Layer \n",
- "109 Normalization Layer \n",
- "110 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:BatchNormalization_Layer\n",
- " subject subject_label predicate \\\n",
- "107 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "108 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "109 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "110 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "107 None aio:BatchNormalization_Layer \n",
- "108 None aio:Layer \n",
- "109 None aio:Normalization_Layer \n",
- "110 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "107 BatchNormalization Layer \n",
- "108 Layer \n",
- "109 Normalization Layer \n",
- "110 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:Bayesian_Network\n",
- " subject subject_label predicate predicate_label \\\n",
- "111 aio:Bayesian_Network Bayesian Network rdfs:subClassOf None \n",
- "112 aio:Bayesian_Network Bayesian Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "111 aio:Bayesian_Network Bayesian Network \n",
- "112 aio:Network Network \n",
- "parent aio:Bayesian_Network\n",
- "aio:Bayesian_Network\n",
- " subject subject_label predicate predicate_label \\\n",
- "111 aio:Bayesian_Network Bayesian Network rdfs:subClassOf None \n",
- "112 aio:Bayesian_Network Bayesian Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "111 aio:Bayesian_Network Bayesian Network \n",
- "112 aio:Network Network \n",
- "parent aio:Bayesian_Network\n",
- "aio:Behavioral_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "113 aio:Behavioral_Bias Behavioral Bias rdfs:subClassOf None \n",
- "114 aio:Behavioral_Bias Behavioral Bias rdfs:subClassOf None \n",
- "115 aio:Behavioral_Bias Behavioral Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "113 aio:Behavioral_Bias Behavioral Bias \n",
- "114 aio:Bias Bias \n",
- "115 aio:Individual_Bias Individual Bias \n",
- "parent aio:Behavioral_Bias\n",
- "aio:Behavioral_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "113 aio:Behavioral_Bias Behavioral Bias rdfs:subClassOf None \n",
- "114 aio:Behavioral_Bias Behavioral Bias rdfs:subClassOf None \n",
- "115 aio:Behavioral_Bias Behavioral Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "113 aio:Behavioral_Bias Behavioral Bias \n",
- "114 aio:Bias Bias \n",
- "115 aio:Individual_Bias Individual Bias \n",
- "parent aio:Behavioral_Bias\n",
- "aio:Behavioral_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "113 aio:Behavioral_Bias Behavioral Bias rdfs:subClassOf None \n",
- "114 aio:Behavioral_Bias Behavioral Bias rdfs:subClassOf None \n",
- "115 aio:Behavioral_Bias Behavioral Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "113 aio:Behavioral_Bias Behavioral Bias \n",
- "114 aio:Bias Bias \n",
- "115 aio:Individual_Bias Individual Bias \n",
- "parent aio:Behavioral_Bias\n",
- "aio:Bias\n",
- " subject subject_label predicate predicate_label object \\\n",
- "116 aio:Bias Bias rdfs:subClassOf None aio:Bias \n",
- "\n",
- " object_label \n",
- "116 Bias \n",
- "parent aio:Bias\n",
- "aio:Biclustering\n",
- " subject subject_label predicate predicate_label \\\n",
- "117 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "118 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "119 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "117 aio:Biclustering Biclustering \n",
- "118 aio:Machine_Learning Machine Learning \n",
- "119 aio:Method Method \n",
- "parent aio:Biclustering\n",
- "aio:Biclustering\n",
- " subject subject_label predicate predicate_label \\\n",
- "117 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "118 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "119 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "117 aio:Biclustering Biclustering \n",
- "118 aio:Machine_Learning Machine Learning \n",
- "119 aio:Method Method \n",
- "parent aio:Biclustering\n",
- "aio:Biclustering\n",
- " subject subject_label predicate predicate_label \\\n",
- "117 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "118 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "119 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "117 aio:Biclustering Biclustering \n",
- "118 aio:Machine_Learning Machine Learning \n",
- "119 aio:Method Method \n",
- "parent aio:Biclustering\n",
- "aio:Bidirectional_Layer\n",
- " subject subject_label predicate \\\n",
- "120 aio:Bidirectional_Layer Bidirectional Layer rdfs:subClassOf \n",
- "121 aio:Bidirectional_Layer Bidirectional Layer rdfs:subClassOf \n",
- "122 aio:Bidirectional_Layer Bidirectional Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "120 None aio:Bidirectional_Layer Bidirectional Layer \n",
- "121 None aio:Layer Layer \n",
- "122 None aio:Recurrent_Layer Recurrent Layer \n",
- "parent aio:Bidirectional_Layer\n",
- "aio:Bidirectional_Layer\n",
- " subject subject_label predicate \\\n",
- "120 aio:Bidirectional_Layer Bidirectional Layer rdfs:subClassOf \n",
- "121 aio:Bidirectional_Layer Bidirectional Layer rdfs:subClassOf \n",
- "122 aio:Bidirectional_Layer Bidirectional Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "120 None aio:Bidirectional_Layer Bidirectional Layer \n",
- "121 None aio:Layer Layer \n",
- "122 None aio:Recurrent_Layer Recurrent Layer \n",
- "parent aio:Bidirectional_Layer\n",
- "aio:Bidirectional_Layer\n",
- " subject subject_label predicate \\\n",
- "120 aio:Bidirectional_Layer Bidirectional Layer rdfs:subClassOf \n",
- "121 aio:Bidirectional_Layer Bidirectional Layer rdfs:subClassOf \n",
- "122 aio:Bidirectional_Layer Bidirectional Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "120 None aio:Bidirectional_Layer Bidirectional Layer \n",
- "121 None aio:Layer Layer \n",
- "122 None aio:Recurrent_Layer Recurrent Layer \n",
- "parent aio:Bidirectional_Layer\n",
- "aio:Binary_Classification\n",
- " subject subject_label predicate \\\n",
- "123 aio:Binary_Classification Binary Classification rdfs:subClassOf \n",
- "124 aio:Binary_Classification Binary Classification rdfs:subClassOf \n",
- "125 aio:Binary_Classification Binary Classification rdfs:subClassOf \n",
- "126 aio:Binary_Classification Binary Classification rdfs:subClassOf \n",
- "127 aio:Binary_Classification Binary Classification rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "123 None aio:Binary_Classification Binary Classification \n",
- "124 None aio:Classification Classification \n",
- "125 None aio:Machine_Learning Machine Learning \n",
- "126 None aio:Method Method \n",
- "127 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Binary_Classification\n",
- "aio:Binary_Classification\n",
- " subject subject_label predicate \\\n",
- "123 aio:Binary_Classification Binary Classification rdfs:subClassOf \n",
- "124 aio:Binary_Classification Binary Classification rdfs:subClassOf \n",
- "125 aio:Binary_Classification Binary Classification rdfs:subClassOf \n",
- "126 aio:Binary_Classification Binary Classification rdfs:subClassOf \n",
- "127 aio:Binary_Classification Binary Classification rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "123 None aio:Binary_Classification Binary Classification \n",
- "124 None aio:Classification Classification \n",
- "125 None aio:Machine_Learning Machine Learning \n",
- "126 None aio:Method Method \n",
- "127 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Binary_Classification\n",
- "aio:Binary_Classification\n",
- " subject subject_label predicate \\\n",
- "123 aio:Binary_Classification Binary Classification rdfs:subClassOf \n",
- "124 aio:Binary_Classification Binary Classification rdfs:subClassOf \n",
- "125 aio:Binary_Classification Binary Classification rdfs:subClassOf \n",
- "126 aio:Binary_Classification Binary Classification rdfs:subClassOf \n",
- "127 aio:Binary_Classification Binary Classification rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "123 None aio:Binary_Classification Binary Classification \n",
- "124 None aio:Classification Classification \n",
- "125 None aio:Machine_Learning Machine Learning \n",
- "126 None aio:Method Method \n",
- "127 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Binary_Classification\n",
- "aio:Binary_Classification\n",
- " subject subject_label predicate \\\n",
- "123 aio:Binary_Classification Binary Classification rdfs:subClassOf \n",
- "124 aio:Binary_Classification Binary Classification rdfs:subClassOf \n",
- "125 aio:Binary_Classification Binary Classification rdfs:subClassOf \n",
- "126 aio:Binary_Classification Binary Classification rdfs:subClassOf \n",
- "127 aio:Binary_Classification Binary Classification rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "123 None aio:Binary_Classification Binary Classification \n",
- "124 None aio:Classification Classification \n",
- "125 None aio:Machine_Learning Machine Learning \n",
- "126 None aio:Method Method \n",
- "127 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Binary_Classification\n",
- "aio:Binary_Classification\n",
- " subject subject_label predicate \\\n",
- "123 aio:Binary_Classification Binary Classification rdfs:subClassOf \n",
- "124 aio:Binary_Classification Binary Classification rdfs:subClassOf \n",
- "125 aio:Binary_Classification Binary Classification rdfs:subClassOf \n",
- "126 aio:Binary_Classification Binary Classification rdfs:subClassOf \n",
- "127 aio:Binary_Classification Binary Classification rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "123 None aio:Binary_Classification Binary Classification \n",
- "124 None aio:Classification Classification \n",
- "125 None aio:Machine_Learning Machine Learning \n",
- "126 None aio:Method Method \n",
- "127 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Binary_Classification\n",
- "aio:Categorical_Features_Preprocessing_Layer\n",
- " subject \\\n",
- "128 aio:Categorical_Features_Preprocessing_Layer \n",
- "129 aio:Categorical_Features_Preprocessing_Layer \n",
- "\n",
- " subject_label predicate \\\n",
- "128 Categorical Features Preprocessing Layer rdfs:subClassOf \n",
- "129 Categorical Features Preprocessing Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "128 None aio:Categorical_Features_Preprocessing_Layer \n",
- "129 None aio:Layer \n",
- "\n",
- " object_label \n",
- "128 Categorical Features Preprocessing Layer \n",
- "129 Layer \n",
- "parent aio:Categorical_Features_Preprocessing_Layer\n",
- "aio:Categorical_Features_Preprocessing_Layer\n",
- " subject \\\n",
- "128 aio:Categorical_Features_Preprocessing_Layer \n",
- "129 aio:Categorical_Features_Preprocessing_Layer \n",
- "\n",
- " subject_label predicate \\\n",
- "128 Categorical Features Preprocessing Layer rdfs:subClassOf \n",
- "129 Categorical Features Preprocessing Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "128 None aio:Categorical_Features_Preprocessing_Layer \n",
- "129 None aio:Layer \n",
- "\n",
- " object_label \n",
- "128 Categorical Features Preprocessing Layer \n",
- "129 Layer \n",
- "parent aio:Categorical_Features_Preprocessing_Layer\n",
- "aio:CategoryEncoding_Layer\n",
- " subject subject_label predicate \\\n",
- "130 aio:CategoryEncoding_Layer CategoryEncoding Layer rdfs:subClassOf \n",
- "131 aio:CategoryEncoding_Layer CategoryEncoding Layer rdfs:subClassOf \n",
- "132 aio:CategoryEncoding_Layer CategoryEncoding Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "130 None aio:Categorical_Features_Preprocessing_Layer \n",
- "131 None aio:CategoryEncoding_Layer \n",
- "132 None aio:Layer \n",
- "\n",
- " object_label \n",
- "130 Categorical Features Preprocessing Layer \n",
- "131 CategoryEncoding Layer \n",
- "132 Layer \n",
- "parent aio:Categorical_Features_Preprocessing_Layer\n",
- "aio:Categorical_Features_Preprocessing_Layer\n",
- " subject \\\n",
- "128 aio:Categorical_Features_Preprocessing_Layer \n",
- "129 aio:Categorical_Features_Preprocessing_Layer \n",
- "\n",
- " subject_label predicate \\\n",
- "128 Categorical Features Preprocessing Layer rdfs:subClassOf \n",
- "129 Categorical Features Preprocessing Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "128 None aio:Categorical_Features_Preprocessing_Layer \n",
- "129 None aio:Layer \n",
- "\n",
- " object_label \n",
- "128 Categorical Features Preprocessing Layer \n",
- "129 Layer \n",
- "parent aio:Categorical_Features_Preprocessing_Layer\n",
- "aio:CategoryEncoding_Layer\n",
- " subject subject_label predicate \\\n",
- "130 aio:CategoryEncoding_Layer CategoryEncoding Layer rdfs:subClassOf \n",
- "131 aio:CategoryEncoding_Layer CategoryEncoding Layer rdfs:subClassOf \n",
- "132 aio:CategoryEncoding_Layer CategoryEncoding Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "130 None aio:Categorical_Features_Preprocessing_Layer \n",
- "131 None aio:CategoryEncoding_Layer \n",
- "132 None aio:Layer \n",
- "\n",
- " object_label \n",
- "130 Categorical Features Preprocessing Layer \n",
- "131 CategoryEncoding Layer \n",
- "132 Layer \n",
- "parent aio:Categorical_Features_Preprocessing_Layer\n",
- "aio:Categorical_Features_Preprocessing_Layer\n",
- " subject \\\n",
- "128 aio:Categorical_Features_Preprocessing_Layer \n",
- "129 aio:Categorical_Features_Preprocessing_Layer \n",
- "\n",
- " subject_label predicate \\\n",
- "128 Categorical Features Preprocessing Layer rdfs:subClassOf \n",
- "129 Categorical Features Preprocessing Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "128 None aio:Categorical_Features_Preprocessing_Layer \n",
- "129 None aio:Layer \n",
- "\n",
- " object_label \n",
- "128 Categorical Features Preprocessing Layer \n",
- "129 Layer \n",
- "parent aio:Categorical_Features_Preprocessing_Layer\n",
- "aio:CategoryEncoding_Layer\n",
- " subject subject_label predicate \\\n",
- "130 aio:CategoryEncoding_Layer CategoryEncoding Layer rdfs:subClassOf \n",
- "131 aio:CategoryEncoding_Layer CategoryEncoding Layer rdfs:subClassOf \n",
- "132 aio:CategoryEncoding_Layer CategoryEncoding Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "130 None aio:Categorical_Features_Preprocessing_Layer \n",
- "131 None aio:CategoryEncoding_Layer \n",
- "132 None aio:Layer \n",
- "\n",
- " object_label \n",
- "130 Categorical Features Preprocessing Layer \n",
- "131 CategoryEncoding Layer \n",
- "132 Layer \n",
- "parent aio:Categorical_Features_Preprocessing_Layer\n",
- "aio:Categorical_Features_Preprocessing_Layer\n",
- " subject \\\n",
- "128 aio:Categorical_Features_Preprocessing_Layer \n",
- "129 aio:Categorical_Features_Preprocessing_Layer \n",
- "\n",
- " subject_label predicate \\\n",
- "128 Categorical Features Preprocessing Layer rdfs:subClassOf \n",
- "129 Categorical Features Preprocessing Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "128 None aio:Categorical_Features_Preprocessing_Layer \n",
- "129 None aio:Layer \n",
- "\n",
- " object_label \n",
- "128 Categorical Features Preprocessing Layer \n",
- "129 Layer \n",
- "parent aio:Categorical_Features_Preprocessing_Layer\n",
- "aio:Causal_Graphical_Model\n",
- " subject subject_label predicate \\\n",
- "133 aio:Causal_Graphical_Model Causal Graphical Model rdfs:subClassOf \n",
- "134 aio:Causal_Graphical_Model Causal Graphical Model rdfs:subClassOf \n",
- "135 aio:Causal_Graphical_Model Causal Graphical Model rdfs:subClassOf \n",
- "136 aio:Causal_Graphical_Model Causal Graphical Model rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "133 None aio:Causal_Graphical_Model \n",
- "134 None aio:Machine_Learning \n",
- "135 None aio:Method \n",
- "136 None aio:Probabilistic_Graphical_Model \n",
- "\n",
- " object_label \n",
- "133 Causal Graphical Model \n",
- "134 Machine Learning \n",
- "135 Method \n",
- "136 Probabilistic Graphical Model \n",
- "parent aio:Causal_Graphical_Model\n",
- "aio:Causal_Graphical_Model\n",
- " subject subject_label predicate \\\n",
- "133 aio:Causal_Graphical_Model Causal Graphical Model rdfs:subClassOf \n",
- "134 aio:Causal_Graphical_Model Causal Graphical Model rdfs:subClassOf \n",
- "135 aio:Causal_Graphical_Model Causal Graphical Model rdfs:subClassOf \n",
- "136 aio:Causal_Graphical_Model Causal Graphical Model rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "133 None aio:Causal_Graphical_Model \n",
- "134 None aio:Machine_Learning \n",
- "135 None aio:Method \n",
- "136 None aio:Probabilistic_Graphical_Model \n",
- "\n",
- " object_label \n",
- "133 Causal Graphical Model \n",
- "134 Machine Learning \n",
- "135 Method \n",
- "136 Probabilistic Graphical Model \n",
- "parent aio:Causal_Graphical_Model\n",
- "aio:Causal_Graphical_Model\n",
- " subject subject_label predicate \\\n",
- "133 aio:Causal_Graphical_Model Causal Graphical Model rdfs:subClassOf \n",
- "134 aio:Causal_Graphical_Model Causal Graphical Model rdfs:subClassOf \n",
- "135 aio:Causal_Graphical_Model Causal Graphical Model rdfs:subClassOf \n",
- "136 aio:Causal_Graphical_Model Causal Graphical Model rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "133 None aio:Causal_Graphical_Model \n",
- "134 None aio:Machine_Learning \n",
- "135 None aio:Method \n",
- "136 None aio:Probabilistic_Graphical_Model \n",
- "\n",
- " object_label \n",
- "133 Causal Graphical Model \n",
- "134 Machine Learning \n",
- "135 Method \n",
- "136 Probabilistic Graphical Model \n",
- "parent aio:Causal_Graphical_Model\n",
- "aio:Causal_Graphical_Model\n",
- " subject subject_label predicate \\\n",
- "133 aio:Causal_Graphical_Model Causal Graphical Model rdfs:subClassOf \n",
- "134 aio:Causal_Graphical_Model Causal Graphical Model rdfs:subClassOf \n",
- "135 aio:Causal_Graphical_Model Causal Graphical Model rdfs:subClassOf \n",
- "136 aio:Causal_Graphical_Model Causal Graphical Model rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "133 None aio:Causal_Graphical_Model \n",
- "134 None aio:Machine_Learning \n",
- "135 None aio:Method \n",
- "136 None aio:Probabilistic_Graphical_Model \n",
- "\n",
- " object_label \n",
- "133 Causal Graphical Model \n",
- "134 Machine Learning \n",
- "135 Method \n",
- "136 Probabilistic Graphical Model \n",
- "parent aio:Causal_Graphical_Model\n",
- "aio:CenterCrop_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "137 aio:CenterCrop_Layer CenterCrop Layer rdfs:subClassOf None \n",
- "138 aio:CenterCrop_Layer CenterCrop Layer rdfs:subClassOf None \n",
- "139 aio:CenterCrop_Layer CenterCrop Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "137 aio:CenterCrop_Layer CenterCrop Layer \n",
- "138 aio:Image_Preprocessing_Layer Image Preprocessing Layer \n",
- "139 aio:Layer Layer \n",
- "parent aio:CenterCrop_Layer\n",
- "aio:CenterCrop_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "137 aio:CenterCrop_Layer CenterCrop Layer rdfs:subClassOf None \n",
- "138 aio:CenterCrop_Layer CenterCrop Layer rdfs:subClassOf None \n",
- "139 aio:CenterCrop_Layer CenterCrop Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "137 aio:CenterCrop_Layer CenterCrop Layer \n",
- "138 aio:Image_Preprocessing_Layer Image Preprocessing Layer \n",
- "139 aio:Layer Layer \n",
- "parent aio:CenterCrop_Layer\n",
- "aio:CenterCrop_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "137 aio:CenterCrop_Layer CenterCrop Layer rdfs:subClassOf None \n",
- "138 aio:CenterCrop_Layer CenterCrop Layer rdfs:subClassOf None \n",
- "139 aio:CenterCrop_Layer CenterCrop Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "137 aio:CenterCrop_Layer CenterCrop Layer \n",
- "138 aio:Image_Preprocessing_Layer Image Preprocessing Layer \n",
- "139 aio:Layer Layer \n",
- "parent aio:CenterCrop_Layer\n",
- "aio:Classification\n",
- " subject subject_label predicate predicate_label \\\n",
- "140 aio:Classification Classification rdfs:subClassOf None \n",
- "141 aio:Classification Classification rdfs:subClassOf None \n",
- "142 aio:Classification Classification rdfs:subClassOf None \n",
- "143 aio:Classification Classification rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "140 aio:Classification Classification \n",
- "141 aio:Machine_Learning Machine Learning \n",
- "142 aio:Method Method \n",
- "143 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Classification\n",
- " subject subject_label predicate predicate_label \\\n",
- "140 aio:Classification Classification rdfs:subClassOf None \n",
- "141 aio:Classification Classification rdfs:subClassOf None \n",
- "142 aio:Classification Classification rdfs:subClassOf None \n",
- "143 aio:Classification Classification rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "140 aio:Classification Classification \n",
- "141 aio:Machine_Learning Machine Learning \n",
- "142 aio:Method Method \n",
- "143 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Classification\n",
- " subject subject_label predicate predicate_label \\\n",
- "140 aio:Classification Classification rdfs:subClassOf None \n",
- "141 aio:Classification Classification rdfs:subClassOf None \n",
- "142 aio:Classification Classification rdfs:subClassOf None \n",
- "143 aio:Classification Classification rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "140 aio:Classification Classification \n",
- "141 aio:Machine_Learning Machine Learning \n",
- "142 aio:Method Method \n",
- "143 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Classification\n",
- " subject subject_label predicate predicate_label \\\n",
- "140 aio:Classification Classification rdfs:subClassOf None \n",
- "141 aio:Classification Classification rdfs:subClassOf None \n",
- "142 aio:Classification Classification rdfs:subClassOf None \n",
- "143 aio:Classification Classification rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "140 aio:Classification Classification \n",
- "141 aio:Machine_Learning Machine Learning \n",
- "142 aio:Method Method \n",
- "143 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Clustering\n",
- " subject subject_label predicate predicate_label \\\n",
- "144 aio:Clustering Clustering rdfs:subClassOf None \n",
- "145 aio:Clustering Clustering rdfs:subClassOf None \n",
- "146 aio:Clustering Clustering rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "144 aio:Clustering Clustering \n",
- "145 aio:Machine_Learning Machine Learning \n",
- "146 aio:Method Method \n",
- "parent aio:Clustering\n",
- "aio:Clustering\n",
- " subject subject_label predicate predicate_label \\\n",
- "144 aio:Clustering Clustering rdfs:subClassOf None \n",
- "145 aio:Clustering Clustering rdfs:subClassOf None \n",
- "146 aio:Clustering Clustering rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "144 aio:Clustering Clustering \n",
- "145 aio:Machine_Learning Machine Learning \n",
- "146 aio:Method Method \n",
- "parent aio:Clustering\n",
- "aio:Clustering\n",
- " subject subject_label predicate predicate_label \\\n",
- "144 aio:Clustering Clustering rdfs:subClassOf None \n",
- "145 aio:Clustering Clustering rdfs:subClassOf None \n",
- "146 aio:Clustering Clustering rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "144 aio:Clustering Clustering \n",
- "145 aio:Machine_Learning Machine Learning \n",
- "146 aio:Method Method \n",
- "parent aio:Clustering\n",
- "aio:Cognitive_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "147 aio:Cognitive_Bias Cognitive Bias rdfs:subClassOf None \n",
- "148 aio:Cognitive_Bias Cognitive Bias rdfs:subClassOf None \n",
- "149 aio:Cognitive_Bias Cognitive Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "147 aio:Bias Bias \n",
- "148 aio:Cognitive_Bias Cognitive Bias \n",
- "149 aio:Individual_Bias Individual Bias \n",
- "parent aio:Bias\n",
- "aio:Cognitive_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "147 aio:Cognitive_Bias Cognitive Bias rdfs:subClassOf None \n",
- "148 aio:Cognitive_Bias Cognitive Bias rdfs:subClassOf None \n",
- "149 aio:Cognitive_Bias Cognitive Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "147 aio:Bias Bias \n",
- "148 aio:Cognitive_Bias Cognitive Bias \n",
- "149 aio:Individual_Bias Individual Bias \n",
- "parent aio:Bias\n",
- "aio:Cognitive_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "147 aio:Cognitive_Bias Cognitive Bias rdfs:subClassOf None \n",
- "148 aio:Cognitive_Bias Cognitive Bias rdfs:subClassOf None \n",
- "149 aio:Cognitive_Bias Cognitive Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "147 aio:Bias Bias \n",
- "148 aio:Cognitive_Bias Cognitive Bias \n",
- "149 aio:Individual_Bias Individual Bias \n",
- "parent aio:Bias\n",
- "aio:Computational_Bias\n",
- " subject subject_label predicate \\\n",
- "150 aio:Computational_Bias Computational Bias rdfs:subClassOf \n",
- "151 aio:Computational_Bias Computational Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "150 None aio:Bias Bias \n",
- "151 None aio:Computational_Bias Computational Bias \n",
- "parent aio:Bias\n",
- "aio:Computational_Bias\n",
- " subject subject_label predicate \\\n",
- "150 aio:Computational_Bias Computational Bias rdfs:subClassOf \n",
- "151 aio:Computational_Bias Computational Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "150 None aio:Bias Bias \n",
- "151 None aio:Computational_Bias Computational Bias \n",
- "parent aio:Bias\n",
- "aio:Concatenate_Layer\n",
- " subject subject_label predicate \\\n",
- "152 aio:Concatenate_Layer Concatenate Layer rdfs:subClassOf \n",
- "153 aio:Concatenate_Layer Concatenate Layer rdfs:subClassOf \n",
- "154 aio:Concatenate_Layer Concatenate Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "152 None aio:Concatenate_Layer Concatenate Layer \n",
- "153 None aio:Layer Layer \n",
- "154 None aio:Merging_Layer Merging Layer \n",
- "parent aio:Concatenate_Layer\n",
- "aio:Concatenate_Layer\n",
- " subject subject_label predicate \\\n",
- "152 aio:Concatenate_Layer Concatenate Layer rdfs:subClassOf \n",
- "153 aio:Concatenate_Layer Concatenate Layer rdfs:subClassOf \n",
- "154 aio:Concatenate_Layer Concatenate Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "152 None aio:Concatenate_Layer Concatenate Layer \n",
- "153 None aio:Layer Layer \n",
- "154 None aio:Merging_Layer Merging Layer \n",
- "parent aio:Concatenate_Layer\n",
- "aio:Concatenate_Layer\n",
- " subject subject_label predicate \\\n",
- "152 aio:Concatenate_Layer Concatenate Layer rdfs:subClassOf \n",
- "153 aio:Concatenate_Layer Concatenate Layer rdfs:subClassOf \n",
- "154 aio:Concatenate_Layer Concatenate Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "152 None aio:Concatenate_Layer Concatenate Layer \n",
- "153 None aio:Layer Layer \n",
- "154 None aio:Merging_Layer Merging Layer \n",
- "parent aio:Concatenate_Layer\n",
- "aio:Concept_Drift_Bias\n",
- " subject subject_label predicate \\\n",
- "155 aio:Concept_Drift_Bias Concept Drift Bias rdfs:subClassOf \n",
- "156 aio:Concept_Drift_Bias Concept Drift Bias rdfs:subClassOf \n",
- "157 aio:Concept_Drift_Bias Concept Drift Bias rdfs:subClassOf \n",
- "158 aio:Concept_Drift_Bias Concept Drift Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "155 None aio:Bias \n",
- "156 None aio:Computational_Bias \n",
- "157 None aio:Concept_Drift_Bias \n",
- "158 None aio:Use_And_Interpretation_Bias \n",
- "\n",
- " object_label \n",
- "155 Bias \n",
- "156 Computational Bias \n",
- "157 Concept Drift Bias \n",
- "158 Use And Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:Concept_Drift_Bias\n",
- " subject subject_label predicate \\\n",
- "155 aio:Concept_Drift_Bias Concept Drift Bias rdfs:subClassOf \n",
- "156 aio:Concept_Drift_Bias Concept Drift Bias rdfs:subClassOf \n",
- "157 aio:Concept_Drift_Bias Concept Drift Bias rdfs:subClassOf \n",
- "158 aio:Concept_Drift_Bias Concept Drift Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "155 None aio:Bias \n",
- "156 None aio:Computational_Bias \n",
- "157 None aio:Concept_Drift_Bias \n",
- "158 None aio:Use_And_Interpretation_Bias \n",
- "\n",
- " object_label \n",
- "155 Bias \n",
- "156 Computational Bias \n",
- "157 Concept Drift Bias \n",
- "158 Use And Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:Concept_Drift_Bias\n",
- " subject subject_label predicate \\\n",
- "155 aio:Concept_Drift_Bias Concept Drift Bias rdfs:subClassOf \n",
- "156 aio:Concept_Drift_Bias Concept Drift Bias rdfs:subClassOf \n",
- "157 aio:Concept_Drift_Bias Concept Drift Bias rdfs:subClassOf \n",
- "158 aio:Concept_Drift_Bias Concept Drift Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "155 None aio:Bias \n",
- "156 None aio:Computational_Bias \n",
- "157 None aio:Concept_Drift_Bias \n",
- "158 None aio:Use_And_Interpretation_Bias \n",
- "\n",
- " object_label \n",
- "155 Bias \n",
- "156 Computational Bias \n",
- "157 Concept Drift Bias \n",
- "158 Use And Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:Concept_Drift_Bias\n",
- " subject subject_label predicate \\\n",
- "155 aio:Concept_Drift_Bias Concept Drift Bias rdfs:subClassOf \n",
- "156 aio:Concept_Drift_Bias Concept Drift Bias rdfs:subClassOf \n",
- "157 aio:Concept_Drift_Bias Concept Drift Bias rdfs:subClassOf \n",
- "158 aio:Concept_Drift_Bias Concept Drift Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "155 None aio:Bias \n",
- "156 None aio:Computational_Bias \n",
- "157 None aio:Concept_Drift_Bias \n",
- "158 None aio:Use_And_Interpretation_Bias \n",
- "\n",
- " object_label \n",
- "155 Bias \n",
- "156 Computational Bias \n",
- "157 Concept Drift Bias \n",
- "158 Use And Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:Confirmation_Bias\n",
- " subject subject_label predicate \\\n",
- "159 aio:Confirmation_Bias Confirmation Bias rdfs:subClassOf \n",
- "160 aio:Confirmation_Bias Confirmation Bias rdfs:subClassOf \n",
- "161 aio:Confirmation_Bias Confirmation Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "159 None aio:Bias Bias \n",
- "160 None aio:Confirmation_Bias Confirmation Bias \n",
- "161 None aio:Individual_Bias Individual Bias \n",
- "parent aio:Bias\n",
- "aio:Confirmation_Bias\n",
- " subject subject_label predicate \\\n",
- "159 aio:Confirmation_Bias Confirmation Bias rdfs:subClassOf \n",
- "160 aio:Confirmation_Bias Confirmation Bias rdfs:subClassOf \n",
- "161 aio:Confirmation_Bias Confirmation Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "159 None aio:Bias Bias \n",
- "160 None aio:Confirmation_Bias Confirmation Bias \n",
- "161 None aio:Individual_Bias Individual Bias \n",
- "parent aio:Bias\n",
- "aio:Confirmation_Bias\n",
- " subject subject_label predicate \\\n",
- "159 aio:Confirmation_Bias Confirmation Bias rdfs:subClassOf \n",
- "160 aio:Confirmation_Bias Confirmation Bias rdfs:subClassOf \n",
- "161 aio:Confirmation_Bias Confirmation Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "159 None aio:Bias Bias \n",
- "160 None aio:Confirmation_Bias Confirmation Bias \n",
- "161 None aio:Individual_Bias Individual Bias \n",
- "parent aio:Bias\n",
- "aio:Consumer_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "162 aio:Consumer_Bias Consumer Bias rdfs:subClassOf None \n",
- "163 aio:Consumer_Bias Consumer Bias rdfs:subClassOf None \n",
- "164 aio:Consumer_Bias Consumer Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "162 aio:Bias Bias \n",
- "163 aio:Consumer_Bias Consumer Bias \n",
- "164 aio:Individual_Bias Individual Bias \n",
- "parent aio:Bias\n",
- "aio:Consumer_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "162 aio:Consumer_Bias Consumer Bias rdfs:subClassOf None \n",
- "163 aio:Consumer_Bias Consumer Bias rdfs:subClassOf None \n",
- "164 aio:Consumer_Bias Consumer Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "162 aio:Bias Bias \n",
- "163 aio:Consumer_Bias Consumer Bias \n",
- "164 aio:Individual_Bias Individual Bias \n",
- "parent aio:Bias\n",
- "aio:Consumer_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "162 aio:Consumer_Bias Consumer Bias rdfs:subClassOf None \n",
- "163 aio:Consumer_Bias Consumer Bias rdfs:subClassOf None \n",
- "164 aio:Consumer_Bias Consumer Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "162 aio:Bias Bias \n",
- "163 aio:Consumer_Bias Consumer Bias \n",
- "164 aio:Individual_Bias Individual Bias \n",
- "parent aio:Bias\n",
- "aio:Content_Production_Bias\n",
- " subject subject_label predicate \\\n",
- "165 aio:Content_Production_Bias Content Production Bias rdfs:subClassOf \n",
- "166 aio:Content_Production_Bias Content Production Bias rdfs:subClassOf \n",
- "167 aio:Content_Production_Bias Content Production Bias rdfs:subClassOf \n",
- "168 aio:Content_Production_Bias Content Production Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "165 None aio:Bias \n",
- "166 None aio:Computational_Bias \n",
- "167 None aio:Content_Production_Bias \n",
- "168 None aio:Use_And_Interpretation_Bias \n",
- "\n",
- " object_label \n",
- "165 Bias \n",
- "166 Computational Bias \n",
- "167 Content Production Bias \n",
- "168 Use And Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:Content_Production_Bias\n",
- " subject subject_label predicate \\\n",
- "165 aio:Content_Production_Bias Content Production Bias rdfs:subClassOf \n",
- "166 aio:Content_Production_Bias Content Production Bias rdfs:subClassOf \n",
- "167 aio:Content_Production_Bias Content Production Bias rdfs:subClassOf \n",
- "168 aio:Content_Production_Bias Content Production Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "165 None aio:Bias \n",
- "166 None aio:Computational_Bias \n",
- "167 None aio:Content_Production_Bias \n",
- "168 None aio:Use_And_Interpretation_Bias \n",
- "\n",
- " object_label \n",
- "165 Bias \n",
- "166 Computational Bias \n",
- "167 Content Production Bias \n",
- "168 Use And Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:Content_Production_Bias\n",
- " subject subject_label predicate \\\n",
- "165 aio:Content_Production_Bias Content Production Bias rdfs:subClassOf \n",
- "166 aio:Content_Production_Bias Content Production Bias rdfs:subClassOf \n",
- "167 aio:Content_Production_Bias Content Production Bias rdfs:subClassOf \n",
- "168 aio:Content_Production_Bias Content Production Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "165 None aio:Bias \n",
- "166 None aio:Computational_Bias \n",
- "167 None aio:Content_Production_Bias \n",
- "168 None aio:Use_And_Interpretation_Bias \n",
- "\n",
- " object_label \n",
- "165 Bias \n",
- "166 Computational Bias \n",
- "167 Content Production Bias \n",
- "168 Use And Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:Content_Production_Bias\n",
- " subject subject_label predicate \\\n",
- "165 aio:Content_Production_Bias Content Production Bias rdfs:subClassOf \n",
- "166 aio:Content_Production_Bias Content Production Bias rdfs:subClassOf \n",
- "167 aio:Content_Production_Bias Content Production Bias rdfs:subClassOf \n",
- "168 aio:Content_Production_Bias Content Production Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "165 None aio:Bias \n",
- "166 None aio:Computational_Bias \n",
- "167 None aio:Content_Production_Bias \n",
- "168 None aio:Use_And_Interpretation_Bias \n",
- "\n",
- " object_label \n",
- "165 Bias \n",
- "166 Computational Bias \n",
- "167 Content Production Bias \n",
- "168 Use And Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:Continual_Learning\n",
- " subject subject_label predicate \\\n",
- "169 aio:Continual_Learning Continual Learning rdfs:subClassOf \n",
- "170 aio:Continual_Learning Continual Learning rdfs:subClassOf \n",
- "171 aio:Continual_Learning Continual Learning rdfs:subClassOf \n",
- "172 aio:Continual_Learning Continual Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "169 None aio:ANN Artificial Neural Network \n",
- "170 None aio:Continual_Learning Continual Learning \n",
- "171 None aio:DNN Deep Neural Network \n",
- "172 None aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Continual_Learning\n",
- " subject subject_label predicate \\\n",
- "169 aio:Continual_Learning Continual Learning rdfs:subClassOf \n",
- "170 aio:Continual_Learning Continual Learning rdfs:subClassOf \n",
- "171 aio:Continual_Learning Continual Learning rdfs:subClassOf \n",
- "172 aio:Continual_Learning Continual Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "169 None aio:ANN Artificial Neural Network \n",
- "170 None aio:Continual_Learning Continual Learning \n",
- "171 None aio:DNN Deep Neural Network \n",
- "172 None aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Continual_Learning\n",
- " subject subject_label predicate \\\n",
- "169 aio:Continual_Learning Continual Learning rdfs:subClassOf \n",
- "170 aio:Continual_Learning Continual Learning rdfs:subClassOf \n",
- "171 aio:Continual_Learning Continual Learning rdfs:subClassOf \n",
- "172 aio:Continual_Learning Continual Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "169 None aio:ANN Artificial Neural Network \n",
- "170 None aio:Continual_Learning Continual Learning \n",
- "171 None aio:DNN Deep Neural Network \n",
- "172 None aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Continual_Learning\n",
- " subject subject_label predicate \\\n",
- "169 aio:Continual_Learning Continual Learning rdfs:subClassOf \n",
- "170 aio:Continual_Learning Continual Learning rdfs:subClassOf \n",
- "171 aio:Continual_Learning Continual Learning rdfs:subClassOf \n",
- "172 aio:Continual_Learning Continual Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "169 None aio:ANN Artificial Neural Network \n",
- "170 None aio:Continual_Learning Continual Learning \n",
- "171 None aio:DNN Deep Neural Network \n",
- "172 None aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Contrastive_Learning\n",
- " subject subject_label predicate \\\n",
- "173 aio:Contrastive_Learning Contrastive Learning rdfs:subClassOf \n",
- "174 aio:Contrastive_Learning Contrastive Learning rdfs:subClassOf \n",
- "175 aio:Contrastive_Learning Contrastive Learning rdfs:subClassOf \n",
- "176 aio:Contrastive_Learning Contrastive Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "173 None aio:ANN Artificial Neural Network \n",
- "174 None aio:Contrastive_Learning Contrastive Learning \n",
- "175 None aio:DNN Deep Neural Network \n",
- "176 None aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Contrastive_Learning\n",
- " subject subject_label predicate \\\n",
- "173 aio:Contrastive_Learning Contrastive Learning rdfs:subClassOf \n",
- "174 aio:Contrastive_Learning Contrastive Learning rdfs:subClassOf \n",
- "175 aio:Contrastive_Learning Contrastive Learning rdfs:subClassOf \n",
- "176 aio:Contrastive_Learning Contrastive Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "173 None aio:ANN Artificial Neural Network \n",
- "174 None aio:Contrastive_Learning Contrastive Learning \n",
- "175 None aio:DNN Deep Neural Network \n",
- "176 None aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Contrastive_Learning\n",
- " subject subject_label predicate \\\n",
- "173 aio:Contrastive_Learning Contrastive Learning rdfs:subClassOf \n",
- "174 aio:Contrastive_Learning Contrastive Learning rdfs:subClassOf \n",
- "175 aio:Contrastive_Learning Contrastive Learning rdfs:subClassOf \n",
- "176 aio:Contrastive_Learning Contrastive Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "173 None aio:ANN Artificial Neural Network \n",
- "174 None aio:Contrastive_Learning Contrastive Learning \n",
- "175 None aio:DNN Deep Neural Network \n",
- "176 None aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Contrastive_Learning\n",
- " subject subject_label predicate \\\n",
- "173 aio:Contrastive_Learning Contrastive Learning rdfs:subClassOf \n",
- "174 aio:Contrastive_Learning Contrastive Learning rdfs:subClassOf \n",
- "175 aio:Contrastive_Learning Contrastive Learning rdfs:subClassOf \n",
- "176 aio:Contrastive_Learning Contrastive Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "173 None aio:ANN Artificial Neural Network \n",
- "174 None aio:Contrastive_Learning Contrastive Learning \n",
- "175 None aio:DNN Deep Neural Network \n",
- "176 None aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ConvLSTM1D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "177 aio:ConvLSTM1D_Layer ConvLSTM1D Layer rdfs:subClassOf None \n",
- "178 aio:ConvLSTM1D_Layer ConvLSTM1D Layer rdfs:subClassOf None \n",
- "179 aio:ConvLSTM1D_Layer ConvLSTM1D Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "177 aio:ConvLSTM1D_Layer ConvLSTM1D Layer \n",
- "178 aio:Convolutional_Layer Convolutional Layer \n",
- "179 aio:Layer Layer \n",
- "parent aio:ConvLSTM1D_Layer\n",
- "aio:ConvLSTM1D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "177 aio:ConvLSTM1D_Layer ConvLSTM1D Layer rdfs:subClassOf None \n",
- "178 aio:ConvLSTM1D_Layer ConvLSTM1D Layer rdfs:subClassOf None \n",
- "179 aio:ConvLSTM1D_Layer ConvLSTM1D Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "177 aio:ConvLSTM1D_Layer ConvLSTM1D Layer \n",
- "178 aio:Convolutional_Layer Convolutional Layer \n",
- "179 aio:Layer Layer \n",
- "parent aio:ConvLSTM1D_Layer\n",
- "aio:ConvLSTM1D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "177 aio:ConvLSTM1D_Layer ConvLSTM1D Layer rdfs:subClassOf None \n",
- "178 aio:ConvLSTM1D_Layer ConvLSTM1D Layer rdfs:subClassOf None \n",
- "179 aio:ConvLSTM1D_Layer ConvLSTM1D Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "177 aio:ConvLSTM1D_Layer ConvLSTM1D Layer \n",
- "178 aio:Convolutional_Layer Convolutional Layer \n",
- "179 aio:Layer Layer \n",
- "parent aio:ConvLSTM1D_Layer\n",
- "aio:ConvLSTM2D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "180 aio:ConvLSTM2D_Layer ConvLSTM2D Layer rdfs:subClassOf None \n",
- "181 aio:ConvLSTM2D_Layer ConvLSTM2D Layer rdfs:subClassOf None \n",
- "182 aio:ConvLSTM2D_Layer ConvLSTM2D Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "180 aio:ConvLSTM2D_Layer ConvLSTM2D Layer \n",
- "181 aio:Convolutional_Layer Convolutional Layer \n",
- "182 aio:Layer Layer \n",
- "parent aio:ConvLSTM2D_Layer\n",
- "aio:ConvLSTM2D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "180 aio:ConvLSTM2D_Layer ConvLSTM2D Layer rdfs:subClassOf None \n",
- "181 aio:ConvLSTM2D_Layer ConvLSTM2D Layer rdfs:subClassOf None \n",
- "182 aio:ConvLSTM2D_Layer ConvLSTM2D Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "180 aio:ConvLSTM2D_Layer ConvLSTM2D Layer \n",
- "181 aio:Convolutional_Layer Convolutional Layer \n",
- "182 aio:Layer Layer \n",
- "parent aio:ConvLSTM2D_Layer\n",
- "aio:ConvLSTM2D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "180 aio:ConvLSTM2D_Layer ConvLSTM2D Layer rdfs:subClassOf None \n",
- "181 aio:ConvLSTM2D_Layer ConvLSTM2D Layer rdfs:subClassOf None \n",
- "182 aio:ConvLSTM2D_Layer ConvLSTM2D Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "180 aio:ConvLSTM2D_Layer ConvLSTM2D Layer \n",
- "181 aio:Convolutional_Layer Convolutional Layer \n",
- "182 aio:Layer Layer \n",
- "parent aio:ConvLSTM2D_Layer\n",
- "aio:ConvLSTM3D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "183 aio:ConvLSTM3D_Layer ConvLSTM3D Layer rdfs:subClassOf None \n",
- "184 aio:ConvLSTM3D_Layer ConvLSTM3D Layer rdfs:subClassOf None \n",
- "185 aio:ConvLSTM3D_Layer ConvLSTM3D Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "183 aio:ConvLSTM3D_Layer ConvLSTM3D Layer \n",
- "184 aio:Convolutional_Layer Convolutional Layer \n",
- "185 aio:Layer Layer \n",
- "parent aio:ConvLSTM3D_Layer\n",
- "aio:ConvLSTM3D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "183 aio:ConvLSTM3D_Layer ConvLSTM3D Layer rdfs:subClassOf None \n",
- "184 aio:ConvLSTM3D_Layer ConvLSTM3D Layer rdfs:subClassOf None \n",
- "185 aio:ConvLSTM3D_Layer ConvLSTM3D Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "183 aio:ConvLSTM3D_Layer ConvLSTM3D Layer \n",
- "184 aio:Convolutional_Layer Convolutional Layer \n",
- "185 aio:Layer Layer \n",
- "parent aio:ConvLSTM3D_Layer\n",
- "aio:ConvLSTM3D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "183 aio:ConvLSTM3D_Layer ConvLSTM3D Layer rdfs:subClassOf None \n",
- "184 aio:ConvLSTM3D_Layer ConvLSTM3D Layer rdfs:subClassOf None \n",
- "185 aio:ConvLSTM3D_Layer ConvLSTM3D Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "183 aio:ConvLSTM3D_Layer ConvLSTM3D Layer \n",
- "184 aio:Convolutional_Layer Convolutional Layer \n",
- "185 aio:Layer Layer \n",
- "parent aio:ConvLSTM3D_Layer\n",
- "aio:Convolution1DTranspose_Layer\n",
- " subject subject_label \\\n",
- "186 aio:Convolution1DTranspose_Layer Convolution1DTranspose Layer \n",
- "187 aio:Convolution1DTranspose_Layer Convolution1DTranspose Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "186 rdfs:subClassOf None aio:Convolution1DTranspose_Layer \n",
- "187 rdfs:subClassOf None aio:Layer \n",
- "\n",
- " object_label \n",
- "186 Convolution1DTranspose Layer \n",
- "187 Layer \n",
- "parent aio:Convolution1DTranspose_Layer\n",
- "aio:Convolution1DTranspose_Layer\n",
- " subject subject_label \\\n",
- "186 aio:Convolution1DTranspose_Layer Convolution1DTranspose Layer \n",
- "187 aio:Convolution1DTranspose_Layer Convolution1DTranspose Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "186 rdfs:subClassOf None aio:Convolution1DTranspose_Layer \n",
- "187 rdfs:subClassOf None aio:Layer \n",
- "\n",
- " object_label \n",
- "186 Convolution1DTranspose Layer \n",
- "187 Layer \n",
- "parent aio:Convolution1DTranspose_Layer\n",
- "aio:Convolution1D_Layer\n",
- " subject subject_label predicate \\\n",
- "188 aio:Convolution1D_Layer Convolution1D Layer rdfs:subClassOf \n",
- "189 aio:Convolution1D_Layer Convolution1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "188 None aio:Convolution1D_Layer Convolution1D Layer \n",
- "189 None aio:Layer Layer \n",
- "parent aio:Convolution1D_Layer\n",
- "aio:Convolution1D_Layer\n",
- " subject subject_label predicate \\\n",
- "188 aio:Convolution1D_Layer Convolution1D Layer rdfs:subClassOf \n",
- "189 aio:Convolution1D_Layer Convolution1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "188 None aio:Convolution1D_Layer Convolution1D Layer \n",
- "189 None aio:Layer Layer \n",
- "parent aio:Convolution1D_Layer\n",
- "aio:Convolution2DTranspose_Layer\n",
- " subject subject_label \\\n",
- "190 aio:Convolution2DTranspose_Layer Convolution2DTranspose Layer \n",
- "191 aio:Convolution2DTranspose_Layer Convolution2DTranspose Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "190 rdfs:subClassOf None aio:Convolution2DTranspose_Layer \n",
- "191 rdfs:subClassOf None aio:Layer \n",
- "\n",
- " object_label \n",
- "190 Convolution2DTranspose Layer \n",
- "191 Layer \n",
- "parent aio:Convolution2DTranspose_Layer\n",
- "aio:Convolution2DTranspose_Layer\n",
- " subject subject_label \\\n",
- "190 aio:Convolution2DTranspose_Layer Convolution2DTranspose Layer \n",
- "191 aio:Convolution2DTranspose_Layer Convolution2DTranspose Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "190 rdfs:subClassOf None aio:Convolution2DTranspose_Layer \n",
- "191 rdfs:subClassOf None aio:Layer \n",
- "\n",
- " object_label \n",
- "190 Convolution2DTranspose Layer \n",
- "191 Layer \n",
- "parent aio:Convolution2DTranspose_Layer\n",
- "aio:Convolution2D_Layer\n",
- " subject subject_label predicate \\\n",
- "192 aio:Convolution2D_Layer Convolution2D Layer rdfs:subClassOf \n",
- "193 aio:Convolution2D_Layer Convolution2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "192 None aio:Convolution2D_Layer Convolution2D Layer \n",
- "193 None aio:Layer Layer \n",
- "parent aio:Convolution2D_Layer\n",
- "aio:Convolution2D_Layer\n",
- " subject subject_label predicate \\\n",
- "192 aio:Convolution2D_Layer Convolution2D Layer rdfs:subClassOf \n",
- "193 aio:Convolution2D_Layer Convolution2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "192 None aio:Convolution2D_Layer Convolution2D Layer \n",
- "193 None aio:Layer Layer \n",
- "parent aio:Convolution2D_Layer\n",
- "aio:Convolution3DTranspose_Layer\n",
- " subject subject_label \\\n",
- "194 aio:Convolution3DTranspose_Layer Convolution3DTranspose Layer \n",
- "195 aio:Convolution3DTranspose_Layer Convolution3DTranspose Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "194 rdfs:subClassOf None aio:Convolution3DTranspose_Layer \n",
- "195 rdfs:subClassOf None aio:Layer \n",
- "\n",
- " object_label \n",
- "194 Convolution3DTranspose Layer \n",
- "195 Layer \n",
- "parent aio:Convolution3DTranspose_Layer\n",
- "aio:Convolution3DTranspose_Layer\n",
- " subject subject_label \\\n",
- "194 aio:Convolution3DTranspose_Layer Convolution3DTranspose Layer \n",
- "195 aio:Convolution3DTranspose_Layer Convolution3DTranspose Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "194 rdfs:subClassOf None aio:Convolution3DTranspose_Layer \n",
- "195 rdfs:subClassOf None aio:Layer \n",
- "\n",
- " object_label \n",
- "194 Convolution3DTranspose Layer \n",
- "195 Layer \n",
- "parent aio:Convolution3DTranspose_Layer\n",
- "aio:Convolution3D_Layer\n",
- " subject subject_label predicate \\\n",
- "196 aio:Convolution3D_Layer Convolution3D Layer rdfs:subClassOf \n",
- "197 aio:Convolution3D_Layer Convolution3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "196 None aio:Convolution3D_Layer Convolution3D Layer \n",
- "197 None aio:Layer Layer \n",
- "parent aio:Convolution3D_Layer\n",
- "aio:Convolution3D_Layer\n",
- " subject subject_label predicate \\\n",
- "196 aio:Convolution3D_Layer Convolution3D Layer rdfs:subClassOf \n",
- "197 aio:Convolution3D_Layer Convolution3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "196 None aio:Convolution3D_Layer Convolution3D Layer \n",
- "197 None aio:Layer Layer \n",
- "parent aio:Convolution3D_Layer\n",
- "aio:Convolutional_Layer\n",
- " subject subject_label predicate \\\n",
- "198 aio:Convolutional_Layer Convolutional Layer rdfs:subClassOf \n",
- "199 aio:Convolutional_Layer Convolutional Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "198 None aio:Convolutional_Layer Convolutional Layer \n",
- "199 None aio:Layer Layer \n",
- "parent aio:Convolutional_Layer\n",
- "aio:Convolutional_Layer\n",
- " subject subject_label predicate \\\n",
- "198 aio:Convolutional_Layer Convolutional Layer rdfs:subClassOf \n",
- "199 aio:Convolutional_Layer Convolutional Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "198 None aio:Convolutional_Layer Convolutional Layer \n",
- "199 None aio:Layer Layer \n",
- "parent aio:Convolutional_Layer\n",
- "aio:Cropping1D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "200 aio:Cropping1D_Layer Cropping1D Layer rdfs:subClassOf None \n",
- "201 aio:Cropping1D_Layer Cropping1D Layer rdfs:subClassOf None \n",
- "202 aio:Cropping1D_Layer Cropping1D Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "200 aio:Cropping1D_Layer Cropping1D Layer \n",
- "201 aio:Layer Layer \n",
- "202 aio:Reshaping_Layer Reshaping Layer \n",
- "parent aio:Cropping1D_Layer\n",
- "aio:Cropping1D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "200 aio:Cropping1D_Layer Cropping1D Layer rdfs:subClassOf None \n",
- "201 aio:Cropping1D_Layer Cropping1D Layer rdfs:subClassOf None \n",
- "202 aio:Cropping1D_Layer Cropping1D Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "200 aio:Cropping1D_Layer Cropping1D Layer \n",
- "201 aio:Layer Layer \n",
- "202 aio:Reshaping_Layer Reshaping Layer \n",
- "parent aio:Cropping1D_Layer\n",
- "aio:Cropping1D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "200 aio:Cropping1D_Layer Cropping1D Layer rdfs:subClassOf None \n",
- "201 aio:Cropping1D_Layer Cropping1D Layer rdfs:subClassOf None \n",
- "202 aio:Cropping1D_Layer Cropping1D Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "200 aio:Cropping1D_Layer Cropping1D Layer \n",
- "201 aio:Layer Layer \n",
- "202 aio:Reshaping_Layer Reshaping Layer \n",
- "parent aio:Cropping1D_Layer\n",
- "aio:Cropping2D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "203 aio:Cropping2D_Layer Cropping2D Layer rdfs:subClassOf None \n",
- "204 aio:Cropping2D_Layer Cropping2D Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "203 aio:Cropping2D_Layer Cropping2D Layer \n",
- "204 aio:Layer Layer \n",
- "parent aio:Cropping2D_Layer\n",
- "aio:Cropping2D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "203 aio:Cropping2D_Layer Cropping2D Layer rdfs:subClassOf None \n",
- "204 aio:Cropping2D_Layer Cropping2D Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "203 aio:Cropping2D_Layer Cropping2D Layer \n",
- "204 aio:Layer Layer \n",
- "parent aio:Cropping2D_Layer\n",
- "aio:Cropping3D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "205 aio:Cropping3D_Layer Cropping3D Layer rdfs:subClassOf None \n",
- "206 aio:Cropping3D_Layer Cropping3D Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "205 aio:Cropping3D_Layer Cropping3D Layer \n",
- "206 aio:Layer Layer \n",
- "parent aio:Cropping3D_Layer\n",
- "aio:Cropping3D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "205 aio:Cropping3D_Layer Cropping3D Layer rdfs:subClassOf None \n",
- "206 aio:Cropping3D_Layer Cropping3D Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "205 aio:Cropping3D_Layer Cropping3D Layer \n",
- "206 aio:Layer Layer \n",
- "parent aio:Cropping3D_Layer\n",
- "aio:DAE\n",
- " subject subject_label predicate predicate_label \\\n",
- "207 aio:DAE Denoising Auto Encoder rdfs:subClassOf None \n",
- "208 aio:DAE Denoising Auto Encoder rdfs:subClassOf None \n",
- "209 aio:DAE Denoising Auto Encoder rdfs:subClassOf None \n",
- "210 aio:DAE Denoising Auto Encoder rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "207 aio:AE Auto Encoder Network \n",
- "208 aio:DAE Denoising Auto Encoder \n",
- "209 aio:Network Network \n",
- "210 aio:UPN Unsupervised Pretrained Network \n",
- "parent aio:AE\n",
- "aio:AE\n",
- " subject subject_label predicate predicate_label object \\\n",
- "0 aio:AE Auto Encoder Network rdfs:subClassOf None aio:AE \n",
- "1 aio:AE Auto Encoder Network rdfs:subClassOf None aio:Network \n",
- "2 aio:AE Auto Encoder Network rdfs:subClassOf None aio:UPN \n",
- "\n",
- " object_label \n",
- "0 Auto Encoder Network \n",
- "1 Network \n",
- "2 Unsupervised Pretrained Network \n",
- "parent aio:AE\n",
- "aio:DAE\n",
- " subject subject_label predicate predicate_label \\\n",
- "207 aio:DAE Denoising Auto Encoder rdfs:subClassOf None \n",
- "208 aio:DAE Denoising Auto Encoder rdfs:subClassOf None \n",
- "209 aio:DAE Denoising Auto Encoder rdfs:subClassOf None \n",
- "210 aio:DAE Denoising Auto Encoder rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "207 aio:AE Auto Encoder Network \n",
- "208 aio:DAE Denoising Auto Encoder \n",
- "209 aio:Network Network \n",
- "210 aio:UPN Unsupervised Pretrained Network \n",
- "parent aio:AE\n",
- "aio:AE\n",
- " subject subject_label predicate predicate_label object \\\n",
- "0 aio:AE Auto Encoder Network rdfs:subClassOf None aio:AE \n",
- "1 aio:AE Auto Encoder Network rdfs:subClassOf None aio:Network \n",
- "2 aio:AE Auto Encoder Network rdfs:subClassOf None aio:UPN \n",
- "\n",
- " object_label \n",
- "0 Auto Encoder Network \n",
- "1 Network \n",
- "2 Unsupervised Pretrained Network \n",
- "parent aio:AE\n",
- "aio:DAE\n",
- " subject subject_label predicate predicate_label \\\n",
- "207 aio:DAE Denoising Auto Encoder rdfs:subClassOf None \n",
- "208 aio:DAE Denoising Auto Encoder rdfs:subClassOf None \n",
- "209 aio:DAE Denoising Auto Encoder rdfs:subClassOf None \n",
- "210 aio:DAE Denoising Auto Encoder rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "207 aio:AE Auto Encoder Network \n",
- "208 aio:DAE Denoising Auto Encoder \n",
- "209 aio:Network Network \n",
- "210 aio:UPN Unsupervised Pretrained Network \n",
- "parent aio:AE\n",
- "aio:AE\n",
- " subject subject_label predicate predicate_label object \\\n",
- "0 aio:AE Auto Encoder Network rdfs:subClassOf None aio:AE \n",
- "1 aio:AE Auto Encoder Network rdfs:subClassOf None aio:Network \n",
- "2 aio:AE Auto Encoder Network rdfs:subClassOf None aio:UPN \n",
- "\n",
- " object_label \n",
- "0 Auto Encoder Network \n",
- "1 Network \n",
- "2 Unsupervised Pretrained Network \n",
- "parent aio:AE\n",
- "aio:DAE\n",
- " subject subject_label predicate predicate_label \\\n",
- "207 aio:DAE Denoising Auto Encoder rdfs:subClassOf None \n",
- "208 aio:DAE Denoising Auto Encoder rdfs:subClassOf None \n",
- "209 aio:DAE Denoising Auto Encoder rdfs:subClassOf None \n",
- "210 aio:DAE Denoising Auto Encoder rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "207 aio:AE Auto Encoder Network \n",
- "208 aio:DAE Denoising Auto Encoder \n",
- "209 aio:Network Network \n",
- "210 aio:UPN Unsupervised Pretrained Network \n",
- "parent aio:AE\n",
- "aio:AE\n",
- " subject subject_label predicate predicate_label object \\\n",
- "0 aio:AE Auto Encoder Network rdfs:subClassOf None aio:AE \n",
- "1 aio:AE Auto Encoder Network rdfs:subClassOf None aio:Network \n",
- "2 aio:AE Auto Encoder Network rdfs:subClassOf None aio:UPN \n",
- "\n",
- " object_label \n",
- "0 Auto Encoder Network \n",
- "1 Network \n",
- "2 Unsupervised Pretrained Network \n",
- "parent aio:AE\n",
- "aio:DBN\n",
- " subject subject_label predicate predicate_label \\\n",
- "211 aio:DBN Deep Belief Network rdfs:subClassOf None \n",
- "212 aio:DBN Deep Belief Network rdfs:subClassOf None \n",
- "213 aio:DBN Deep Belief Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "211 aio:DBN Deep Belief Network \n",
- "212 aio:Network Network \n",
- "213 aio:UPN Unsupervised Pretrained Network \n",
- "parent aio:DBN\n",
- "aio:DBN\n",
- " subject subject_label predicate predicate_label \\\n",
- "211 aio:DBN Deep Belief Network rdfs:subClassOf None \n",
- "212 aio:DBN Deep Belief Network rdfs:subClassOf None \n",
- "213 aio:DBN Deep Belief Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "211 aio:DBN Deep Belief Network \n",
- "212 aio:Network Network \n",
- "213 aio:UPN Unsupervised Pretrained Network \n",
- "parent aio:DBN\n",
- "aio:DBN\n",
- " subject subject_label predicate predicate_label \\\n",
- "211 aio:DBN Deep Belief Network rdfs:subClassOf None \n",
- "212 aio:DBN Deep Belief Network rdfs:subClassOf None \n",
- "213 aio:DBN Deep Belief Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "211 aio:DBN Deep Belief Network \n",
- "212 aio:Network Network \n",
- "213 aio:UPN Unsupervised Pretrained Network \n",
- "parent aio:DBN\n",
- "aio:DCIGN\n",
- " subject subject_label predicate \\\n",
- "214 aio:DCIGN Deep Convolutional Inverse Graphics Network rdfs:subClassOf \n",
- "215 aio:DCIGN Deep Convolutional Inverse Graphics Network rdfs:subClassOf \n",
- "216 aio:DCIGN Deep Convolutional Inverse Graphics Network rdfs:subClassOf \n",
- "217 aio:DCIGN Deep Convolutional Inverse Graphics Network rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "214 None aio:AE Auto Encoder Network \n",
- "215 None aio:DCIGN Deep Convolutional Inverse Graphics Network \n",
- "216 None aio:Network Network \n",
- "217 None aio:UPN Unsupervised Pretrained Network \n",
- "parent aio:AE\n",
- "aio:AE\n",
- " subject subject_label predicate predicate_label object \\\n",
- "0 aio:AE Auto Encoder Network rdfs:subClassOf None aio:AE \n",
- "1 aio:AE Auto Encoder Network rdfs:subClassOf None aio:Network \n",
- "2 aio:AE Auto Encoder Network rdfs:subClassOf None aio:UPN \n",
- "\n",
- " object_label \n",
- "0 Auto Encoder Network \n",
- "1 Network \n",
- "2 Unsupervised Pretrained Network \n",
- "parent aio:AE\n",
- "aio:DCIGN\n",
- " subject subject_label predicate \\\n",
- "214 aio:DCIGN Deep Convolutional Inverse Graphics Network rdfs:subClassOf \n",
- "215 aio:DCIGN Deep Convolutional Inverse Graphics Network rdfs:subClassOf \n",
- "216 aio:DCIGN Deep Convolutional Inverse Graphics Network rdfs:subClassOf \n",
- "217 aio:DCIGN Deep Convolutional Inverse Graphics Network rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "214 None aio:AE Auto Encoder Network \n",
- "215 None aio:DCIGN Deep Convolutional Inverse Graphics Network \n",
- "216 None aio:Network Network \n",
- "217 None aio:UPN Unsupervised Pretrained Network \n",
- "parent aio:AE\n",
- "aio:AE\n",
- " subject subject_label predicate predicate_label object \\\n",
- "0 aio:AE Auto Encoder Network rdfs:subClassOf None aio:AE \n",
- "1 aio:AE Auto Encoder Network rdfs:subClassOf None aio:Network \n",
- "2 aio:AE Auto Encoder Network rdfs:subClassOf None aio:UPN \n",
- "\n",
- " object_label \n",
- "0 Auto Encoder Network \n",
- "1 Network \n",
- "2 Unsupervised Pretrained Network \n",
- "parent aio:AE\n",
- "aio:DCIGN\n",
- " subject subject_label predicate \\\n",
- "214 aio:DCIGN Deep Convolutional Inverse Graphics Network rdfs:subClassOf \n",
- "215 aio:DCIGN Deep Convolutional Inverse Graphics Network rdfs:subClassOf \n",
- "216 aio:DCIGN Deep Convolutional Inverse Graphics Network rdfs:subClassOf \n",
- "217 aio:DCIGN Deep Convolutional Inverse Graphics Network rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "214 None aio:AE Auto Encoder Network \n",
- "215 None aio:DCIGN Deep Convolutional Inverse Graphics Network \n",
- "216 None aio:Network Network \n",
- "217 None aio:UPN Unsupervised Pretrained Network \n",
- "parent aio:AE\n",
- "aio:AE\n",
- " subject subject_label predicate predicate_label object \\\n",
- "0 aio:AE Auto Encoder Network rdfs:subClassOf None aio:AE \n",
- "1 aio:AE Auto Encoder Network rdfs:subClassOf None aio:Network \n",
- "2 aio:AE Auto Encoder Network rdfs:subClassOf None aio:UPN \n",
- "\n",
- " object_label \n",
- "0 Auto Encoder Network \n",
- "1 Network \n",
- "2 Unsupervised Pretrained Network \n",
- "parent aio:AE\n",
- "aio:DCIGN\n",
- " subject subject_label predicate \\\n",
- "214 aio:DCIGN Deep Convolutional Inverse Graphics Network rdfs:subClassOf \n",
- "215 aio:DCIGN Deep Convolutional Inverse Graphics Network rdfs:subClassOf \n",
- "216 aio:DCIGN Deep Convolutional Inverse Graphics Network rdfs:subClassOf \n",
- "217 aio:DCIGN Deep Convolutional Inverse Graphics Network rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "214 None aio:AE Auto Encoder Network \n",
- "215 None aio:DCIGN Deep Convolutional Inverse Graphics Network \n",
- "216 None aio:Network Network \n",
- "217 None aio:UPN Unsupervised Pretrained Network \n",
- "parent aio:AE\n",
- "aio:AE\n",
- " subject subject_label predicate predicate_label object \\\n",
- "0 aio:AE Auto Encoder Network rdfs:subClassOf None aio:AE \n",
- "1 aio:AE Auto Encoder Network rdfs:subClassOf None aio:Network \n",
- "2 aio:AE Auto Encoder Network rdfs:subClassOf None aio:UPN \n",
- "\n",
- " object_label \n",
- "0 Auto Encoder Network \n",
- "1 Network \n",
- "2 Unsupervised Pretrained Network \n",
- "parent aio:AE\n",
- "aio:DCN\n",
- " subject subject_label predicate predicate_label \\\n",
- "218 aio:DCN Deep Convolutional Network rdfs:subClassOf None \n",
- "219 aio:DCN Deep Convolutional Network rdfs:subClassOf None \n",
- "220 aio:DCN Deep Convolutional Network rdfs:subClassOf None \n",
- "221 aio:DCN Deep Convolutional Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "218 aio:ANN Artificial Neural Network \n",
- "219 aio:DCN Deep Convolutional Network \n",
- "220 aio:DNN Deep Neural Network \n",
- "221 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:DCN\n",
- " subject subject_label predicate predicate_label \\\n",
- "218 aio:DCN Deep Convolutional Network rdfs:subClassOf None \n",
- "219 aio:DCN Deep Convolutional Network rdfs:subClassOf None \n",
- "220 aio:DCN Deep Convolutional Network rdfs:subClassOf None \n",
- "221 aio:DCN Deep Convolutional Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "218 aio:ANN Artificial Neural Network \n",
- "219 aio:DCN Deep Convolutional Network \n",
- "220 aio:DNN Deep Neural Network \n",
- "221 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:DCN\n",
- " subject subject_label predicate predicate_label \\\n",
- "218 aio:DCN Deep Convolutional Network rdfs:subClassOf None \n",
- "219 aio:DCN Deep Convolutional Network rdfs:subClassOf None \n",
- "220 aio:DCN Deep Convolutional Network rdfs:subClassOf None \n",
- "221 aio:DCN Deep Convolutional Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "218 aio:ANN Artificial Neural Network \n",
- "219 aio:DCN Deep Convolutional Network \n",
- "220 aio:DNN Deep Neural Network \n",
- "221 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:DCN\n",
- " subject subject_label predicate predicate_label \\\n",
- "218 aio:DCN Deep Convolutional Network rdfs:subClassOf None \n",
- "219 aio:DCN Deep Convolutional Network rdfs:subClassOf None \n",
- "220 aio:DCN Deep Convolutional Network rdfs:subClassOf None \n",
- "221 aio:DCN Deep Convolutional Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "218 aio:ANN Artificial Neural Network \n",
- "219 aio:DCN Deep Convolutional Network \n",
- "220 aio:DNN Deep Neural Network \n",
- "221 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:DFF\n",
- " subject subject_label predicate predicate_label object \\\n",
- "222 aio:DFF Deep FeedFoward rdfs:subClassOf None aio:ANN \n",
- "223 aio:DFF Deep FeedFoward rdfs:subClassOf None aio:DFF \n",
- "224 aio:DFF Deep FeedFoward rdfs:subClassOf None aio:DNN \n",
- "225 aio:DFF Deep FeedFoward rdfs:subClassOf None aio:Network \n",
- "\n",
- " object_label \n",
- "222 Artificial Neural Network \n",
- "223 Deep FeedFoward \n",
- "224 Deep Neural Network \n",
- "225 Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:DFF\n",
- " subject subject_label predicate predicate_label object \\\n",
- "222 aio:DFF Deep FeedFoward rdfs:subClassOf None aio:ANN \n",
- "223 aio:DFF Deep FeedFoward rdfs:subClassOf None aio:DFF \n",
- "224 aio:DFF Deep FeedFoward rdfs:subClassOf None aio:DNN \n",
- "225 aio:DFF Deep FeedFoward rdfs:subClassOf None aio:Network \n",
- "\n",
- " object_label \n",
- "222 Artificial Neural Network \n",
- "223 Deep FeedFoward \n",
- "224 Deep Neural Network \n",
- "225 Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:DFF\n",
- " subject subject_label predicate predicate_label object \\\n",
- "222 aio:DFF Deep FeedFoward rdfs:subClassOf None aio:ANN \n",
- "223 aio:DFF Deep FeedFoward rdfs:subClassOf None aio:DFF \n",
- "224 aio:DFF Deep FeedFoward rdfs:subClassOf None aio:DNN \n",
- "225 aio:DFF Deep FeedFoward rdfs:subClassOf None aio:Network \n",
- "\n",
- " object_label \n",
- "222 Artificial Neural Network \n",
- "223 Deep FeedFoward \n",
- "224 Deep Neural Network \n",
- "225 Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:DFF\n",
- " subject subject_label predicate predicate_label object \\\n",
- "222 aio:DFF Deep FeedFoward rdfs:subClassOf None aio:ANN \n",
- "223 aio:DFF Deep FeedFoward rdfs:subClassOf None aio:DFF \n",
- "224 aio:DFF Deep FeedFoward rdfs:subClassOf None aio:DNN \n",
- "225 aio:DFF Deep FeedFoward rdfs:subClassOf None aio:Network \n",
- "\n",
- " object_label \n",
- "222 Artificial Neural Network \n",
- "223 Deep FeedFoward \n",
- "224 Deep Neural Network \n",
- "225 Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:DN\n",
- " subject subject_label predicate predicate_label \\\n",
- "226 aio:DN Deconvolutional Network rdfs:subClassOf None \n",
- "227 aio:DN Deconvolutional Network rdfs:subClassOf None \n",
- "228 aio:DN Deconvolutional Network rdfs:subClassOf None \n",
- "229 aio:DN Deconvolutional Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "226 aio:ANN Artificial Neural Network \n",
- "227 aio:DN Deconvolutional Network \n",
- "228 aio:DNN Deep Neural Network \n",
- "229 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:DN\n",
- " subject subject_label predicate predicate_label \\\n",
- "226 aio:DN Deconvolutional Network rdfs:subClassOf None \n",
- "227 aio:DN Deconvolutional Network rdfs:subClassOf None \n",
- "228 aio:DN Deconvolutional Network rdfs:subClassOf None \n",
- "229 aio:DN Deconvolutional Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "226 aio:ANN Artificial Neural Network \n",
- "227 aio:DN Deconvolutional Network \n",
- "228 aio:DNN Deep Neural Network \n",
- "229 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:DN\n",
- " subject subject_label predicate predicate_label \\\n",
- "226 aio:DN Deconvolutional Network rdfs:subClassOf None \n",
- "227 aio:DN Deconvolutional Network rdfs:subClassOf None \n",
- "228 aio:DN Deconvolutional Network rdfs:subClassOf None \n",
- "229 aio:DN Deconvolutional Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "226 aio:ANN Artificial Neural Network \n",
- "227 aio:DN Deconvolutional Network \n",
- "228 aio:DNN Deep Neural Network \n",
- "229 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:DN\n",
- " subject subject_label predicate predicate_label \\\n",
- "226 aio:DN Deconvolutional Network rdfs:subClassOf None \n",
- "227 aio:DN Deconvolutional Network rdfs:subClassOf None \n",
- "228 aio:DN Deconvolutional Network rdfs:subClassOf None \n",
- "229 aio:DN Deconvolutional Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "226 aio:ANN Artificial Neural Network \n",
- "227 aio:DN Deconvolutional Network \n",
- "228 aio:DNN Deep Neural Network \n",
- "229 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:DNN\n",
- " subject subject_label predicate predicate_label \\\n",
- "230 aio:DNN Deep Neural Network rdfs:subClassOf None \n",
- "231 aio:DNN Deep Neural Network rdfs:subClassOf None \n",
- "232 aio:DNN Deep Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "230 aio:ANN Artificial Neural Network \n",
- "231 aio:DNN Deep Neural Network \n",
- "232 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:DNN\n",
- " subject subject_label predicate predicate_label \\\n",
- "230 aio:DNN Deep Neural Network rdfs:subClassOf None \n",
- "231 aio:DNN Deep Neural Network rdfs:subClassOf None \n",
- "232 aio:DNN Deep Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "230 aio:ANN Artificial Neural Network \n",
- "231 aio:DNN Deep Neural Network \n",
- "232 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:DNN\n",
- " subject subject_label predicate predicate_label \\\n",
- "230 aio:DNN Deep Neural Network rdfs:subClassOf None \n",
- "231 aio:DNN Deep Neural Network rdfs:subClassOf None \n",
- "232 aio:DNN Deep Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "230 aio:ANN Artificial Neural Network \n",
- "231 aio:DNN Deep Neural Network \n",
- "232 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Data_Dredging_Bias\n",
- " subject subject_label predicate \\\n",
- "233 aio:Data_Dredging_Bias Data Dredging Bias rdfs:subClassOf \n",
- "234 aio:Data_Dredging_Bias Data Dredging Bias rdfs:subClassOf \n",
- "235 aio:Data_Dredging_Bias Data Dredging Bias rdfs:subClassOf \n",
- "236 aio:Data_Dredging_Bias Data Dredging Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "233 None aio:Bias \n",
- "234 None aio:Computational_Bias \n",
- "235 None aio:Data_Dredging_Bias \n",
- "236 None aio:Use_And_Interpretation_Bias \n",
- "\n",
- " object_label \n",
- "233 Bias \n",
- "234 Computational Bias \n",
- "235 Data Dredging Bias \n",
- "236 Use And Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:Data_Dredging_Bias\n",
- " subject subject_label predicate \\\n",
- "233 aio:Data_Dredging_Bias Data Dredging Bias rdfs:subClassOf \n",
- "234 aio:Data_Dredging_Bias Data Dredging Bias rdfs:subClassOf \n",
- "235 aio:Data_Dredging_Bias Data Dredging Bias rdfs:subClassOf \n",
- "236 aio:Data_Dredging_Bias Data Dredging Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "233 None aio:Bias \n",
- "234 None aio:Computational_Bias \n",
- "235 None aio:Data_Dredging_Bias \n",
- "236 None aio:Use_And_Interpretation_Bias \n",
- "\n",
- " object_label \n",
- "233 Bias \n",
- "234 Computational Bias \n",
- "235 Data Dredging Bias \n",
- "236 Use And Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:Data_Dredging_Bias\n",
- " subject subject_label predicate \\\n",
- "233 aio:Data_Dredging_Bias Data Dredging Bias rdfs:subClassOf \n",
- "234 aio:Data_Dredging_Bias Data Dredging Bias rdfs:subClassOf \n",
- "235 aio:Data_Dredging_Bias Data Dredging Bias rdfs:subClassOf \n",
- "236 aio:Data_Dredging_Bias Data Dredging Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "233 None aio:Bias \n",
- "234 None aio:Computational_Bias \n",
- "235 None aio:Data_Dredging_Bias \n",
- "236 None aio:Use_And_Interpretation_Bias \n",
- "\n",
- " object_label \n",
- "233 Bias \n",
- "234 Computational Bias \n",
- "235 Data Dredging Bias \n",
- "236 Use And Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:Data_Dredging_Bias\n",
- " subject subject_label predicate \\\n",
- "233 aio:Data_Dredging_Bias Data Dredging Bias rdfs:subClassOf \n",
- "234 aio:Data_Dredging_Bias Data Dredging Bias rdfs:subClassOf \n",
- "235 aio:Data_Dredging_Bias Data Dredging Bias rdfs:subClassOf \n",
- "236 aio:Data_Dredging_Bias Data Dredging Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "233 None aio:Bias \n",
- "234 None aio:Computational_Bias \n",
- "235 None aio:Data_Dredging_Bias \n",
- "236 None aio:Use_And_Interpretation_Bias \n",
- "\n",
- " object_label \n",
- "233 Bias \n",
- "234 Computational Bias \n",
- "235 Data Dredging Bias \n",
- "236 Use And Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:Data_Generation_Bias\n",
- " subject subject_label predicate \\\n",
- "237 aio:Data_Generation_Bias Data Generation Bias rdfs:subClassOf \n",
- "238 aio:Data_Generation_Bias Data Generation Bias rdfs:subClassOf \n",
- "239 aio:Data_Generation_Bias Data Generation Bias rdfs:subClassOf \n",
- "240 aio:Data_Generation_Bias Data Generation Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "237 None aio:Bias \n",
- "238 None aio:Computational_Bias \n",
- "239 None aio:Data_Generation_Bias \n",
- "240 None aio:Selection_And_Sampling_Bias \n",
- "\n",
- " object_label \n",
- "237 Bias \n",
- "238 Computational Bias \n",
- "239 Data Generation Bias \n",
- "240 Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Data_Generation_Bias\n",
- " subject subject_label predicate \\\n",
- "237 aio:Data_Generation_Bias Data Generation Bias rdfs:subClassOf \n",
- "238 aio:Data_Generation_Bias Data Generation Bias rdfs:subClassOf \n",
- "239 aio:Data_Generation_Bias Data Generation Bias rdfs:subClassOf \n",
- "240 aio:Data_Generation_Bias Data Generation Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "237 None aio:Bias \n",
- "238 None aio:Computational_Bias \n",
- "239 None aio:Data_Generation_Bias \n",
- "240 None aio:Selection_And_Sampling_Bias \n",
- "\n",
- " object_label \n",
- "237 Bias \n",
- "238 Computational Bias \n",
- "239 Data Generation Bias \n",
- "240 Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Data_Generation_Bias\n",
- " subject subject_label predicate \\\n",
- "237 aio:Data_Generation_Bias Data Generation Bias rdfs:subClassOf \n",
- "238 aio:Data_Generation_Bias Data Generation Bias rdfs:subClassOf \n",
- "239 aio:Data_Generation_Bias Data Generation Bias rdfs:subClassOf \n",
- "240 aio:Data_Generation_Bias Data Generation Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "237 None aio:Bias \n",
- "238 None aio:Computational_Bias \n",
- "239 None aio:Data_Generation_Bias \n",
- "240 None aio:Selection_And_Sampling_Bias \n",
- "\n",
- " object_label \n",
- "237 Bias \n",
- "238 Computational Bias \n",
- "239 Data Generation Bias \n",
- "240 Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Data_Generation_Bias\n",
- " subject subject_label predicate \\\n",
- "237 aio:Data_Generation_Bias Data Generation Bias rdfs:subClassOf \n",
- "238 aio:Data_Generation_Bias Data Generation Bias rdfs:subClassOf \n",
- "239 aio:Data_Generation_Bias Data Generation Bias rdfs:subClassOf \n",
- "240 aio:Data_Generation_Bias Data Generation Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "237 None aio:Bias \n",
- "238 None aio:Computational_Bias \n",
- "239 None aio:Data_Generation_Bias \n",
- "240 None aio:Selection_And_Sampling_Bias \n",
- "\n",
- " object_label \n",
- "237 Bias \n",
- "238 Computational Bias \n",
- "239 Data Generation Bias \n",
- "240 Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Data_Imputation\n",
- " subject subject_label predicate predicate_label \\\n",
- "241 aio:Data_Imputation Data Imputation rdfs:subClassOf None \n",
- "242 aio:Data_Imputation Data Imputation rdfs:subClassOf None \n",
- "243 aio:Data_Imputation Data Imputation rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "241 aio:Data_Imputation Data Imputation \n",
- "242 aio:Machine_Learning Machine Learning \n",
- "243 aio:Method Method \n",
- "parent aio:Data_Imputation\n",
- "aio:Data_Imputation\n",
- " subject subject_label predicate predicate_label \\\n",
- "241 aio:Data_Imputation Data Imputation rdfs:subClassOf None \n",
- "242 aio:Data_Imputation Data Imputation rdfs:subClassOf None \n",
- "243 aio:Data_Imputation Data Imputation rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "241 aio:Data_Imputation Data Imputation \n",
- "242 aio:Machine_Learning Machine Learning \n",
- "243 aio:Method Method \n",
- "parent aio:Data_Imputation\n",
- "aio:Data_Imputation\n",
- " subject subject_label predicate predicate_label \\\n",
- "241 aio:Data_Imputation Data Imputation rdfs:subClassOf None \n",
- "242 aio:Data_Imputation Data Imputation rdfs:subClassOf None \n",
- "243 aio:Data_Imputation Data Imputation rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "241 aio:Data_Imputation Data Imputation \n",
- "242 aio:Machine_Learning Machine Learning \n",
- "243 aio:Method Method \n",
- "parent aio:Data_Imputation\n",
- "aio:Decision_Tree\n",
- " subject subject_label predicate predicate_label \\\n",
- "244 aio:Decision_Tree Decision Tree rdfs:subClassOf None \n",
- "245 aio:Decision_Tree Decision Tree rdfs:subClassOf None \n",
- "246 aio:Decision_Tree Decision Tree rdfs:subClassOf None \n",
- "247 aio:Decision_Tree Decision Tree rdfs:subClassOf None \n",
- "248 aio:Decision_Tree Decision Tree rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "244 aio:Classification Classification \n",
- "245 aio:Decision_Tree Decision Tree \n",
- "246 aio:Machine_Learning Machine Learning \n",
- "247 aio:Method Method \n",
- "248 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Classification\n",
- " subject subject_label predicate predicate_label \\\n",
- "140 aio:Classification Classification rdfs:subClassOf None \n",
- "141 aio:Classification Classification rdfs:subClassOf None \n",
- "142 aio:Classification Classification rdfs:subClassOf None \n",
- "143 aio:Classification Classification rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "140 aio:Classification Classification \n",
- "141 aio:Machine_Learning Machine Learning \n",
- "142 aio:Method Method \n",
- "143 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Decision_Tree\n",
- " subject subject_label predicate predicate_label \\\n",
- "244 aio:Decision_Tree Decision Tree rdfs:subClassOf None \n",
- "245 aio:Decision_Tree Decision Tree rdfs:subClassOf None \n",
- "246 aio:Decision_Tree Decision Tree rdfs:subClassOf None \n",
- "247 aio:Decision_Tree Decision Tree rdfs:subClassOf None \n",
- "248 aio:Decision_Tree Decision Tree rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "244 aio:Classification Classification \n",
- "245 aio:Decision_Tree Decision Tree \n",
- "246 aio:Machine_Learning Machine Learning \n",
- "247 aio:Method Method \n",
- "248 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Classification\n",
- " subject subject_label predicate predicate_label \\\n",
- "140 aio:Classification Classification rdfs:subClassOf None \n",
- "141 aio:Classification Classification rdfs:subClassOf None \n",
- "142 aio:Classification Classification rdfs:subClassOf None \n",
- "143 aio:Classification Classification rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "140 aio:Classification Classification \n",
- "141 aio:Machine_Learning Machine Learning \n",
- "142 aio:Method Method \n",
- "143 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Decision_Tree\n",
- " subject subject_label predicate predicate_label \\\n",
- "244 aio:Decision_Tree Decision Tree rdfs:subClassOf None \n",
- "245 aio:Decision_Tree Decision Tree rdfs:subClassOf None \n",
- "246 aio:Decision_Tree Decision Tree rdfs:subClassOf None \n",
- "247 aio:Decision_Tree Decision Tree rdfs:subClassOf None \n",
- "248 aio:Decision_Tree Decision Tree rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "244 aio:Classification Classification \n",
- "245 aio:Decision_Tree Decision Tree \n",
- "246 aio:Machine_Learning Machine Learning \n",
- "247 aio:Method Method \n",
- "248 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Classification\n",
- " subject subject_label predicate predicate_label \\\n",
- "140 aio:Classification Classification rdfs:subClassOf None \n",
- "141 aio:Classification Classification rdfs:subClassOf None \n",
- "142 aio:Classification Classification rdfs:subClassOf None \n",
- "143 aio:Classification Classification rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "140 aio:Classification Classification \n",
- "141 aio:Machine_Learning Machine Learning \n",
- "142 aio:Method Method \n",
- "143 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Decision_Tree\n",
- " subject subject_label predicate predicate_label \\\n",
- "244 aio:Decision_Tree Decision Tree rdfs:subClassOf None \n",
- "245 aio:Decision_Tree Decision Tree rdfs:subClassOf None \n",
- "246 aio:Decision_Tree Decision Tree rdfs:subClassOf None \n",
- "247 aio:Decision_Tree Decision Tree rdfs:subClassOf None \n",
- "248 aio:Decision_Tree Decision Tree rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "244 aio:Classification Classification \n",
- "245 aio:Decision_Tree Decision Tree \n",
- "246 aio:Machine_Learning Machine Learning \n",
- "247 aio:Method Method \n",
- "248 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Classification\n",
- " subject subject_label predicate predicate_label \\\n",
- "140 aio:Classification Classification rdfs:subClassOf None \n",
- "141 aio:Classification Classification rdfs:subClassOf None \n",
- "142 aio:Classification Classification rdfs:subClassOf None \n",
- "143 aio:Classification Classification rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "140 aio:Classification Classification \n",
- "141 aio:Machine_Learning Machine Learning \n",
- "142 aio:Method Method \n",
- "143 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Decision_Tree\n",
- " subject subject_label predicate predicate_label \\\n",
- "244 aio:Decision_Tree Decision Tree rdfs:subClassOf None \n",
- "245 aio:Decision_Tree Decision Tree rdfs:subClassOf None \n",
- "246 aio:Decision_Tree Decision Tree rdfs:subClassOf None \n",
- "247 aio:Decision_Tree Decision Tree rdfs:subClassOf None \n",
- "248 aio:Decision_Tree Decision Tree rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "244 aio:Classification Classification \n",
- "245 aio:Decision_Tree Decision Tree \n",
- "246 aio:Machine_Learning Machine Learning \n",
- "247 aio:Method Method \n",
- "248 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Classification\n",
- " subject subject_label predicate predicate_label \\\n",
- "140 aio:Classification Classification rdfs:subClassOf None \n",
- "141 aio:Classification Classification rdfs:subClassOf None \n",
- "142 aio:Classification Classification rdfs:subClassOf None \n",
- "143 aio:Classification Classification rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "140 aio:Classification Classification \n",
- "141 aio:Machine_Learning Machine Learning \n",
- "142 aio:Method Method \n",
- "143 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Deep_Active_Learning\n",
- " subject subject_label predicate \\\n",
- "249 aio:Deep_Active_Learning Deep Active Learning rdfs:subClassOf \n",
- "250 aio:Deep_Active_Learning Deep Active Learning rdfs:subClassOf \n",
- "251 aio:Deep_Active_Learning Deep Active Learning rdfs:subClassOf \n",
- "252 aio:Deep_Active_Learning Deep Active Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "249 None aio:ANN Artificial Neural Network \n",
- "250 None aio:DNN Deep Neural Network \n",
- "251 None aio:Deep_Active_Learning Deep Active Learning \n",
- "252 None aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Deep_Active_Learning\n",
- " subject subject_label predicate \\\n",
- "249 aio:Deep_Active_Learning Deep Active Learning rdfs:subClassOf \n",
- "250 aio:Deep_Active_Learning Deep Active Learning rdfs:subClassOf \n",
- "251 aio:Deep_Active_Learning Deep Active Learning rdfs:subClassOf \n",
- "252 aio:Deep_Active_Learning Deep Active Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "249 None aio:ANN Artificial Neural Network \n",
- "250 None aio:DNN Deep Neural Network \n",
- "251 None aio:Deep_Active_Learning Deep Active Learning \n",
- "252 None aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Deep_Active_Learning\n",
- " subject subject_label predicate \\\n",
- "249 aio:Deep_Active_Learning Deep Active Learning rdfs:subClassOf \n",
- "250 aio:Deep_Active_Learning Deep Active Learning rdfs:subClassOf \n",
- "251 aio:Deep_Active_Learning Deep Active Learning rdfs:subClassOf \n",
- "252 aio:Deep_Active_Learning Deep Active Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "249 None aio:ANN Artificial Neural Network \n",
- "250 None aio:DNN Deep Neural Network \n",
- "251 None aio:Deep_Active_Learning Deep Active Learning \n",
- "252 None aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Deep_Active_Learning\n",
- " subject subject_label predicate \\\n",
- "249 aio:Deep_Active_Learning Deep Active Learning rdfs:subClassOf \n",
- "250 aio:Deep_Active_Learning Deep Active Learning rdfs:subClassOf \n",
- "251 aio:Deep_Active_Learning Deep Active Learning rdfs:subClassOf \n",
- "252 aio:Deep_Active_Learning Deep Active Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "249 None aio:ANN Artificial Neural Network \n",
- "250 None aio:DNN Deep Neural Network \n",
- "251 None aio:Deep_Active_Learning Deep Active Learning \n",
- "252 None aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Deep_Transfer_Learning\n",
- " subject subject_label predicate \\\n",
- "253 aio:Deep_Transfer_Learning Deep Transfer Learning rdfs:subClassOf \n",
- "254 aio:Deep_Transfer_Learning Deep Transfer Learning rdfs:subClassOf \n",
- "255 aio:Deep_Transfer_Learning Deep Transfer Learning rdfs:subClassOf \n",
- "256 aio:Deep_Transfer_Learning Deep Transfer Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "253 None aio:ANN Artificial Neural Network \n",
- "254 None aio:DNN Deep Neural Network \n",
- "255 None aio:Deep_Transfer_Learning Deep Transfer Learning \n",
- "256 None aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Deep_Transfer_Learning\n",
- " subject subject_label predicate \\\n",
- "253 aio:Deep_Transfer_Learning Deep Transfer Learning rdfs:subClassOf \n",
- "254 aio:Deep_Transfer_Learning Deep Transfer Learning rdfs:subClassOf \n",
- "255 aio:Deep_Transfer_Learning Deep Transfer Learning rdfs:subClassOf \n",
- "256 aio:Deep_Transfer_Learning Deep Transfer Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "253 None aio:ANN Artificial Neural Network \n",
- "254 None aio:DNN Deep Neural Network \n",
- "255 None aio:Deep_Transfer_Learning Deep Transfer Learning \n",
- "256 None aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Deep_Transfer_Learning\n",
- " subject subject_label predicate \\\n",
- "253 aio:Deep_Transfer_Learning Deep Transfer Learning rdfs:subClassOf \n",
- "254 aio:Deep_Transfer_Learning Deep Transfer Learning rdfs:subClassOf \n",
- "255 aio:Deep_Transfer_Learning Deep Transfer Learning rdfs:subClassOf \n",
- "256 aio:Deep_Transfer_Learning Deep Transfer Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "253 None aio:ANN Artificial Neural Network \n",
- "254 None aio:DNN Deep Neural Network \n",
- "255 None aio:Deep_Transfer_Learning Deep Transfer Learning \n",
- "256 None aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Deep_Transfer_Learning\n",
- " subject subject_label predicate \\\n",
- "253 aio:Deep_Transfer_Learning Deep Transfer Learning rdfs:subClassOf \n",
- "254 aio:Deep_Transfer_Learning Deep Transfer Learning rdfs:subClassOf \n",
- "255 aio:Deep_Transfer_Learning Deep Transfer Learning rdfs:subClassOf \n",
- "256 aio:Deep_Transfer_Learning Deep Transfer Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "253 None aio:ANN Artificial Neural Network \n",
- "254 None aio:DNN Deep Neural Network \n",
- "255 None aio:Deep_Transfer_Learning Deep Transfer Learning \n",
- "256 None aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:DenseFeatures_Layer\n",
- " subject subject_label predicate \\\n",
- "257 aio:DenseFeatures_Layer DenseFeatures Layer rdfs:subClassOf \n",
- "258 aio:DenseFeatures_Layer DenseFeatures Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "257 None aio:DenseFeatures_Layer DenseFeatures Layer \n",
- "258 None aio:Layer Layer \n",
- "parent aio:DenseFeatures_Layer\n",
- "aio:DenseFeatures_Layer\n",
- " subject subject_label predicate \\\n",
- "257 aio:DenseFeatures_Layer DenseFeatures Layer rdfs:subClassOf \n",
- "258 aio:DenseFeatures_Layer DenseFeatures Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "257 None aio:DenseFeatures_Layer DenseFeatures Layer \n",
- "258 None aio:Layer Layer \n",
- "parent aio:DenseFeatures_Layer\n",
- "aio:Dense_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "259 aio:Dense_Layer Dense Layer rdfs:subClassOf None \n",
- "260 aio:Dense_Layer Dense Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "259 aio:Dense_Layer Dense Layer \n",
- "260 aio:Layer Layer \n",
- "parent aio:Dense_Layer\n",
- "aio:Dense_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "259 aio:Dense_Layer Dense Layer rdfs:subClassOf None \n",
- "260 aio:Dense_Layer Dense Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "259 aio:Dense_Layer Dense Layer \n",
- "260 aio:Layer Layer \n",
- "parent aio:Dense_Layer\n",
- "aio:Deployment_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "261 aio:Deployment_Bias Deployment Bias rdfs:subClassOf None \n",
- "262 aio:Deployment_Bias Deployment Bias rdfs:subClassOf None \n",
- "263 aio:Deployment_Bias Deployment Bias rdfs:subClassOf None \n",
- "264 aio:Deployment_Bias Deployment Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "261 aio:Bias Bias \n",
- "262 aio:Deployment_Bias Deployment Bias \n",
- "263 aio:Group_Bias Group Bias \n",
- "264 aio:Human_Bias Human Bias \n",
- "parent aio:Bias\n",
- "aio:Deployment_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "261 aio:Deployment_Bias Deployment Bias rdfs:subClassOf None \n",
- "262 aio:Deployment_Bias Deployment Bias rdfs:subClassOf None \n",
- "263 aio:Deployment_Bias Deployment Bias rdfs:subClassOf None \n",
- "264 aio:Deployment_Bias Deployment Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "261 aio:Bias Bias \n",
- "262 aio:Deployment_Bias Deployment Bias \n",
- "263 aio:Group_Bias Group Bias \n",
- "264 aio:Human_Bias Human Bias \n",
- "parent aio:Bias\n",
- "aio:Deployment_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "261 aio:Deployment_Bias Deployment Bias rdfs:subClassOf None \n",
- "262 aio:Deployment_Bias Deployment Bias rdfs:subClassOf None \n",
- "263 aio:Deployment_Bias Deployment Bias rdfs:subClassOf None \n",
- "264 aio:Deployment_Bias Deployment Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "261 aio:Bias Bias \n",
- "262 aio:Deployment_Bias Deployment Bias \n",
- "263 aio:Group_Bias Group Bias \n",
- "264 aio:Human_Bias Human Bias \n",
- "parent aio:Bias\n",
- "aio:Deployment_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "261 aio:Deployment_Bias Deployment Bias rdfs:subClassOf None \n",
- "262 aio:Deployment_Bias Deployment Bias rdfs:subClassOf None \n",
- "263 aio:Deployment_Bias Deployment Bias rdfs:subClassOf None \n",
- "264 aio:Deployment_Bias Deployment Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "261 aio:Bias Bias \n",
- "262 aio:Deployment_Bias Deployment Bias \n",
- "263 aio:Group_Bias Group Bias \n",
- "264 aio:Human_Bias Human Bias \n",
- "parent aio:Bias\n",
- "aio:DepthwiseConv1D_Layer\n",
- " subject subject_label predicate \\\n",
- "265 aio:DepthwiseConv1D_Layer DepthwiseConv1D Layer rdfs:subClassOf \n",
- "266 aio:DepthwiseConv1D_Layer DepthwiseConv1D Layer rdfs:subClassOf \n",
- "267 aio:DepthwiseConv1D_Layer DepthwiseConv1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "265 None aio:Convolutional_Layer Convolutional Layer \n",
- "266 None aio:DepthwiseConv1D_Layer DepthwiseConv1D Layer \n",
- "267 None aio:Layer Layer \n",
- "parent aio:Convolutional_Layer\n",
- "aio:Convolutional_Layer\n",
- " subject subject_label predicate \\\n",
- "198 aio:Convolutional_Layer Convolutional Layer rdfs:subClassOf \n",
- "199 aio:Convolutional_Layer Convolutional Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "198 None aio:Convolutional_Layer Convolutional Layer \n",
- "199 None aio:Layer Layer \n",
- "parent aio:Convolutional_Layer\n",
- "aio:DepthwiseConv1D_Layer\n",
- " subject subject_label predicate \\\n",
- "265 aio:DepthwiseConv1D_Layer DepthwiseConv1D Layer rdfs:subClassOf \n",
- "266 aio:DepthwiseConv1D_Layer DepthwiseConv1D Layer rdfs:subClassOf \n",
- "267 aio:DepthwiseConv1D_Layer DepthwiseConv1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "265 None aio:Convolutional_Layer Convolutional Layer \n",
- "266 None aio:DepthwiseConv1D_Layer DepthwiseConv1D Layer \n",
- "267 None aio:Layer Layer \n",
- "parent aio:Convolutional_Layer\n",
- "aio:Convolutional_Layer\n",
- " subject subject_label predicate \\\n",
- "198 aio:Convolutional_Layer Convolutional Layer rdfs:subClassOf \n",
- "199 aio:Convolutional_Layer Convolutional Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "198 None aio:Convolutional_Layer Convolutional Layer \n",
- "199 None aio:Layer Layer \n",
- "parent aio:Convolutional_Layer\n",
- "aio:DepthwiseConv1D_Layer\n",
- " subject subject_label predicate \\\n",
- "265 aio:DepthwiseConv1D_Layer DepthwiseConv1D Layer rdfs:subClassOf \n",
- "266 aio:DepthwiseConv1D_Layer DepthwiseConv1D Layer rdfs:subClassOf \n",
- "267 aio:DepthwiseConv1D_Layer DepthwiseConv1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "265 None aio:Convolutional_Layer Convolutional Layer \n",
- "266 None aio:DepthwiseConv1D_Layer DepthwiseConv1D Layer \n",
- "267 None aio:Layer Layer \n",
- "parent aio:Convolutional_Layer\n",
- "aio:Convolutional_Layer\n",
- " subject subject_label predicate \\\n",
- "198 aio:Convolutional_Layer Convolutional Layer rdfs:subClassOf \n",
- "199 aio:Convolutional_Layer Convolutional Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "198 None aio:Convolutional_Layer Convolutional Layer \n",
- "199 None aio:Layer Layer \n",
- "parent aio:Convolutional_Layer\n",
- "aio:DepthwiseConv2D_Layer\n",
- " subject subject_label predicate \\\n",
- "268 aio:DepthwiseConv2D_Layer DepthwiseConv2D Layer rdfs:subClassOf \n",
- "269 aio:DepthwiseConv2D_Layer DepthwiseConv2D Layer rdfs:subClassOf \n",
- "270 aio:DepthwiseConv2D_Layer DepthwiseConv2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "268 None aio:Convolutional_Layer Convolutional Layer \n",
- "269 None aio:DepthwiseConv2D_Layer DepthwiseConv2D Layer \n",
- "270 None aio:Layer Layer \n",
- "parent aio:Convolutional_Layer\n",
- "aio:Convolutional_Layer\n",
- " subject subject_label predicate \\\n",
- "198 aio:Convolutional_Layer Convolutional Layer rdfs:subClassOf \n",
- "199 aio:Convolutional_Layer Convolutional Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "198 None aio:Convolutional_Layer Convolutional Layer \n",
- "199 None aio:Layer Layer \n",
- "parent aio:Convolutional_Layer\n",
- "aio:DepthwiseConv2D_Layer\n",
- " subject subject_label predicate \\\n",
- "268 aio:DepthwiseConv2D_Layer DepthwiseConv2D Layer rdfs:subClassOf \n",
- "269 aio:DepthwiseConv2D_Layer DepthwiseConv2D Layer rdfs:subClassOf \n",
- "270 aio:DepthwiseConv2D_Layer DepthwiseConv2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "268 None aio:Convolutional_Layer Convolutional Layer \n",
- "269 None aio:DepthwiseConv2D_Layer DepthwiseConv2D Layer \n",
- "270 None aio:Layer Layer \n",
- "parent aio:Convolutional_Layer\n",
- "aio:Convolutional_Layer\n",
- " subject subject_label predicate \\\n",
- "198 aio:Convolutional_Layer Convolutional Layer rdfs:subClassOf \n",
- "199 aio:Convolutional_Layer Convolutional Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "198 None aio:Convolutional_Layer Convolutional Layer \n",
- "199 None aio:Layer Layer \n",
- "parent aio:Convolutional_Layer\n",
- "aio:DepthwiseConv2D_Layer\n",
- " subject subject_label predicate \\\n",
- "268 aio:DepthwiseConv2D_Layer DepthwiseConv2D Layer rdfs:subClassOf \n",
- "269 aio:DepthwiseConv2D_Layer DepthwiseConv2D Layer rdfs:subClassOf \n",
- "270 aio:DepthwiseConv2D_Layer DepthwiseConv2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "268 None aio:Convolutional_Layer Convolutional Layer \n",
- "269 None aio:DepthwiseConv2D_Layer DepthwiseConv2D Layer \n",
- "270 None aio:Layer Layer \n",
- "parent aio:Convolutional_Layer\n",
- "aio:Convolutional_Layer\n",
- " subject subject_label predicate \\\n",
- "198 aio:Convolutional_Layer Convolutional Layer rdfs:subClassOf \n",
- "199 aio:Convolutional_Layer Convolutional Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "198 None aio:Convolutional_Layer Convolutional Layer \n",
- "199 None aio:Layer Layer \n",
- "parent aio:Convolutional_Layer\n",
- "aio:Detection_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "271 aio:Detection_Bias Detection Bias rdfs:subClassOf None \n",
- "272 aio:Detection_Bias Detection Bias rdfs:subClassOf None \n",
- "273 aio:Detection_Bias Detection Bias rdfs:subClassOf None \n",
- "274 aio:Detection_Bias Detection Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "271 aio:Bias Bias \n",
- "272 aio:Computational_Bias Computational Bias \n",
- "273 aio:Detection_Bias Detection Bias \n",
- "274 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Detection_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "271 aio:Detection_Bias Detection Bias rdfs:subClassOf None \n",
- "272 aio:Detection_Bias Detection Bias rdfs:subClassOf None \n",
- "273 aio:Detection_Bias Detection Bias rdfs:subClassOf None \n",
- "274 aio:Detection_Bias Detection Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "271 aio:Bias Bias \n",
- "272 aio:Computational_Bias Computational Bias \n",
- "273 aio:Detection_Bias Detection Bias \n",
- "274 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Detection_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "271 aio:Detection_Bias Detection Bias rdfs:subClassOf None \n",
- "272 aio:Detection_Bias Detection Bias rdfs:subClassOf None \n",
- "273 aio:Detection_Bias Detection Bias rdfs:subClassOf None \n",
- "274 aio:Detection_Bias Detection Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "271 aio:Bias Bias \n",
- "272 aio:Computational_Bias Computational Bias \n",
- "273 aio:Detection_Bias Detection Bias \n",
- "274 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Detection_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "271 aio:Detection_Bias Detection Bias rdfs:subClassOf None \n",
- "272 aio:Detection_Bias Detection Bias rdfs:subClassOf None \n",
- "273 aio:Detection_Bias Detection Bias rdfs:subClassOf None \n",
- "274 aio:Detection_Bias Detection Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "271 aio:Bias Bias \n",
- "272 aio:Computational_Bias Computational Bias \n",
- "273 aio:Detection_Bias Detection Bias \n",
- "274 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Dimensionality_Reduction\n",
- " subject subject_label predicate \\\n",
- "275 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "276 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "277 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "278 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "275 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "276 None aio:Machine_Learning Machine Learning \n",
- "277 None aio:Method Method \n",
- "278 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Dimensionality_Reduction\n",
- " subject subject_label predicate \\\n",
- "275 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "276 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "277 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "278 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "275 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "276 None aio:Machine_Learning Machine Learning \n",
- "277 None aio:Method Method \n",
- "278 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Dimensionality_Reduction\n",
- " subject subject_label predicate \\\n",
- "275 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "276 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "277 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "278 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "275 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "276 None aio:Machine_Learning Machine Learning \n",
- "277 None aio:Method Method \n",
- "278 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Dimensionality_Reduction\n",
- " subject subject_label predicate \\\n",
- "275 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "276 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "277 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "278 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "275 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "276 None aio:Machine_Learning Machine Learning \n",
- "277 None aio:Method Method \n",
- "278 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Discretization_Layer\n",
- " subject subject_label predicate \\\n",
- "279 aio:Discretization_Layer Discretization Layer rdfs:subClassOf \n",
- "280 aio:Discretization_Layer Discretization Layer rdfs:subClassOf \n",
- "281 aio:Discretization_Layer Discretization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "279 None aio:Discretization_Layer \n",
- "280 None aio:Layer \n",
- "281 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "279 Discretization Layer \n",
- "280 Layer \n",
- "281 Numerical Features Preprocessing Layer \n",
- "parent aio:Discretization_Layer\n",
- "aio:Discretization_Layer\n",
- " subject subject_label predicate \\\n",
- "279 aio:Discretization_Layer Discretization Layer rdfs:subClassOf \n",
- "280 aio:Discretization_Layer Discretization Layer rdfs:subClassOf \n",
- "281 aio:Discretization_Layer Discretization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "279 None aio:Discretization_Layer \n",
- "280 None aio:Layer \n",
- "281 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "279 Discretization Layer \n",
- "280 Layer \n",
- "281 Numerical Features Preprocessing Layer \n",
- "parent aio:Discretization_Layer\n",
- "aio:Discretization_Layer\n",
- " subject subject_label predicate \\\n",
- "279 aio:Discretization_Layer Discretization Layer rdfs:subClassOf \n",
- "280 aio:Discretization_Layer Discretization Layer rdfs:subClassOf \n",
- "281 aio:Discretization_Layer Discretization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "279 None aio:Discretization_Layer \n",
- "280 None aio:Layer \n",
- "281 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "279 Discretization Layer \n",
- "280 Layer \n",
- "281 Numerical Features Preprocessing Layer \n",
- "parent aio:Discretization_Layer\n",
- "aio:Dot_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "282 aio:Dot_Layer Dot Layer rdfs:subClassOf None \n",
- "283 aio:Dot_Layer Dot Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "282 aio:Dot_Layer Dot Layer \n",
- "283 aio:Layer Layer \n",
- "parent aio:Dot_Layer\n",
- "aio:Dot_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "282 aio:Dot_Layer Dot Layer rdfs:subClassOf None \n",
- "283 aio:Dot_Layer Dot Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "282 aio:Dot_Layer Dot Layer \n",
- "283 aio:Layer Layer \n",
- "parent aio:Dot_Layer\n",
- "aio:Dropout_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "284 aio:Dropout_Layer Dropout Layer rdfs:subClassOf None \n",
- "285 aio:Dropout_Layer Dropout Layer rdfs:subClassOf None \n",
- "286 aio:Dropout_Layer Dropout Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "284 aio:Dropout_Layer Dropout Layer \n",
- "285 aio:Layer Layer \n",
- "286 aio:Regularization_Layer Regularization Layer \n",
- "parent aio:Dropout_Layer\n",
- "aio:Dropout_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "284 aio:Dropout_Layer Dropout Layer rdfs:subClassOf None \n",
- "285 aio:Dropout_Layer Dropout Layer rdfs:subClassOf None \n",
- "286 aio:Dropout_Layer Dropout Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "284 aio:Dropout_Layer Dropout Layer \n",
- "285 aio:Layer Layer \n",
- "286 aio:Regularization_Layer Regularization Layer \n",
- "parent aio:Dropout_Layer\n",
- "aio:Dropout_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "284 aio:Dropout_Layer Dropout Layer rdfs:subClassOf None \n",
- "285 aio:Dropout_Layer Dropout Layer rdfs:subClassOf None \n",
- "286 aio:Dropout_Layer Dropout Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "284 aio:Dropout_Layer Dropout Layer \n",
- "285 aio:Layer Layer \n",
- "286 aio:Regularization_Layer Regularization Layer \n",
- "parent aio:Dropout_Layer\n",
- "aio:Dunning-Kruger_Effect_Bias\n",
- " subject subject_label \\\n",
- "287 aio:Dunning-Kruger_Effect_Bias Dunning-Kruger Effect Bias \n",
- "288 aio:Dunning-Kruger_Effect_Bias Dunning-Kruger Effect Bias \n",
- "289 aio:Dunning-Kruger_Effect_Bias Dunning-Kruger Effect Bias \n",
- "290 aio:Dunning-Kruger_Effect_Bias Dunning-Kruger Effect Bias \n",
- "\n",
- " predicate predicate_label object \\\n",
- "287 rdfs:subClassOf None aio:Bias \n",
- "288 rdfs:subClassOf None aio:Cognitive_Bias \n",
- "289 rdfs:subClassOf None aio:Dunning-Kruger_Effect_Bias \n",
- "290 rdfs:subClassOf None aio:Individual_Bias \n",
- "\n",
- " object_label \n",
- "287 Bias \n",
- "288 Cognitive Bias \n",
- "289 Dunning-Kruger Effect Bias \n",
- "290 Individual Bias \n",
- "parent aio:Bias\n",
- "aio:Dunning-Kruger_Effect_Bias\n",
- " subject subject_label \\\n",
- "287 aio:Dunning-Kruger_Effect_Bias Dunning-Kruger Effect Bias \n",
- "288 aio:Dunning-Kruger_Effect_Bias Dunning-Kruger Effect Bias \n",
- "289 aio:Dunning-Kruger_Effect_Bias Dunning-Kruger Effect Bias \n",
- "290 aio:Dunning-Kruger_Effect_Bias Dunning-Kruger Effect Bias \n",
- "\n",
- " predicate predicate_label object \\\n",
- "287 rdfs:subClassOf None aio:Bias \n",
- "288 rdfs:subClassOf None aio:Cognitive_Bias \n",
- "289 rdfs:subClassOf None aio:Dunning-Kruger_Effect_Bias \n",
- "290 rdfs:subClassOf None aio:Individual_Bias \n",
- "\n",
- " object_label \n",
- "287 Bias \n",
- "288 Cognitive Bias \n",
- "289 Dunning-Kruger Effect Bias \n",
- "290 Individual Bias \n",
- "parent aio:Bias\n",
- "aio:Dunning-Kruger_Effect_Bias\n",
- " subject subject_label \\\n",
- "287 aio:Dunning-Kruger_Effect_Bias Dunning-Kruger Effect Bias \n",
- "288 aio:Dunning-Kruger_Effect_Bias Dunning-Kruger Effect Bias \n",
- "289 aio:Dunning-Kruger_Effect_Bias Dunning-Kruger Effect Bias \n",
- "290 aio:Dunning-Kruger_Effect_Bias Dunning-Kruger Effect Bias \n",
- "\n",
- " predicate predicate_label object \\\n",
- "287 rdfs:subClassOf None aio:Bias \n",
- "288 rdfs:subClassOf None aio:Cognitive_Bias \n",
- "289 rdfs:subClassOf None aio:Dunning-Kruger_Effect_Bias \n",
- "290 rdfs:subClassOf None aio:Individual_Bias \n",
- "\n",
- " object_label \n",
- "287 Bias \n",
- "288 Cognitive Bias \n",
- "289 Dunning-Kruger Effect Bias \n",
- "290 Individual Bias \n",
- "parent aio:Bias\n",
- "aio:Dunning-Kruger_Effect_Bias\n",
- " subject subject_label \\\n",
- "287 aio:Dunning-Kruger_Effect_Bias Dunning-Kruger Effect Bias \n",
- "288 aio:Dunning-Kruger_Effect_Bias Dunning-Kruger Effect Bias \n",
- "289 aio:Dunning-Kruger_Effect_Bias Dunning-Kruger Effect Bias \n",
- "290 aio:Dunning-Kruger_Effect_Bias Dunning-Kruger Effect Bias \n",
- "\n",
- " predicate predicate_label object \\\n",
- "287 rdfs:subClassOf None aio:Bias \n",
- "288 rdfs:subClassOf None aio:Cognitive_Bias \n",
- "289 rdfs:subClassOf None aio:Dunning-Kruger_Effect_Bias \n",
- "290 rdfs:subClassOf None aio:Individual_Bias \n",
- "\n",
- " object_label \n",
- "287 Bias \n",
- "288 Cognitive Bias \n",
- "289 Dunning-Kruger Effect Bias \n",
- "290 Individual Bias \n",
- "parent aio:Bias\n",
- "aio:ELM\n",
- " subject subject_label predicate predicate_label \\\n",
- "291 aio:ELM Extreme Learning Machine rdfs:subClassOf None \n",
- "292 aio:ELM Extreme Learning Machine rdfs:subClassOf None \n",
- "293 aio:ELM Extreme Learning Machine rdfs:subClassOf None \n",
- "294 aio:ELM Extreme Learning Machine rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "291 aio:ANN Artificial Neural Network \n",
- "292 aio:ELM Extreme Learning Machine \n",
- "293 aio:FBN Feedback Network \n",
- "294 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ELM\n",
- " subject subject_label predicate predicate_label \\\n",
- "291 aio:ELM Extreme Learning Machine rdfs:subClassOf None \n",
- "292 aio:ELM Extreme Learning Machine rdfs:subClassOf None \n",
- "293 aio:ELM Extreme Learning Machine rdfs:subClassOf None \n",
- "294 aio:ELM Extreme Learning Machine rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "291 aio:ANN Artificial Neural Network \n",
- "292 aio:ELM Extreme Learning Machine \n",
- "293 aio:FBN Feedback Network \n",
- "294 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ELM\n",
- " subject subject_label predicate predicate_label \\\n",
- "291 aio:ELM Extreme Learning Machine rdfs:subClassOf None \n",
- "292 aio:ELM Extreme Learning Machine rdfs:subClassOf None \n",
- "293 aio:ELM Extreme Learning Machine rdfs:subClassOf None \n",
- "294 aio:ELM Extreme Learning Machine rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "291 aio:ANN Artificial Neural Network \n",
- "292 aio:ELM Extreme Learning Machine \n",
- "293 aio:FBN Feedback Network \n",
- "294 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ELM\n",
- " subject subject_label predicate predicate_label \\\n",
- "291 aio:ELM Extreme Learning Machine rdfs:subClassOf None \n",
- "292 aio:ELM Extreme Learning Machine rdfs:subClassOf None \n",
- "293 aio:ELM Extreme Learning Machine rdfs:subClassOf None \n",
- "294 aio:ELM Extreme Learning Machine rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "291 aio:ANN Artificial Neural Network \n",
- "292 aio:ELM Extreme Learning Machine \n",
- "293 aio:FBN Feedback Network \n",
- "294 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ELU_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "295 aio:ELU_Layer ELU Layer rdfs:subClassOf None \n",
- "296 aio:ELU_Layer ELU Layer rdfs:subClassOf None \n",
- "297 aio:ELU_Layer ELU Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "295 aio:Activation_Layer Activation Layer \n",
- "296 aio:ELU_Layer ELU Layer \n",
- "297 aio:Layer Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:Activation_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "7 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "8 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "7 aio:Activation_Layer Activation Layer \n",
- "8 aio:Layer Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:ELU_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "295 aio:ELU_Layer ELU Layer rdfs:subClassOf None \n",
- "296 aio:ELU_Layer ELU Layer rdfs:subClassOf None \n",
- "297 aio:ELU_Layer ELU Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "295 aio:Activation_Layer Activation Layer \n",
- "296 aio:ELU_Layer ELU Layer \n",
- "297 aio:Layer Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:Activation_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "7 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "8 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "7 aio:Activation_Layer Activation Layer \n",
- "8 aio:Layer Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:ELU_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "295 aio:ELU_Layer ELU Layer rdfs:subClassOf None \n",
- "296 aio:ELU_Layer ELU Layer rdfs:subClassOf None \n",
- "297 aio:ELU_Layer ELU Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "295 aio:Activation_Layer Activation Layer \n",
- "296 aio:ELU_Layer ELU Layer \n",
- "297 aio:Layer Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:Activation_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "7 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "8 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "7 aio:Activation_Layer Activation Layer \n",
- "8 aio:Layer Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:ESN\n",
- " subject subject_label predicate predicate_label \\\n",
- "298 aio:ESN Echo State Network rdfs:subClassOf None \n",
- "299 aio:ESN Echo State Network rdfs:subClassOf None \n",
- "300 aio:ESN Echo State Network rdfs:subClassOf None \n",
- "301 aio:ESN Echo State Network rdfs:subClassOf None \n",
- "302 aio:ESN Echo State Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "298 aio:ANN Artificial Neural Network \n",
- "299 aio:DNN Deep Neural Network \n",
- "300 aio:ESN Echo State Network \n",
- "301 aio:Network Network \n",
- "302 aio:RecNN Recurrent Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ESN\n",
- " subject subject_label predicate predicate_label \\\n",
- "298 aio:ESN Echo State Network rdfs:subClassOf None \n",
- "299 aio:ESN Echo State Network rdfs:subClassOf None \n",
- "300 aio:ESN Echo State Network rdfs:subClassOf None \n",
- "301 aio:ESN Echo State Network rdfs:subClassOf None \n",
- "302 aio:ESN Echo State Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "298 aio:ANN Artificial Neural Network \n",
- "299 aio:DNN Deep Neural Network \n",
- "300 aio:ESN Echo State Network \n",
- "301 aio:Network Network \n",
- "302 aio:RecNN Recurrent Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ESN\n",
- " subject subject_label predicate predicate_label \\\n",
- "298 aio:ESN Echo State Network rdfs:subClassOf None \n",
- "299 aio:ESN Echo State Network rdfs:subClassOf None \n",
- "300 aio:ESN Echo State Network rdfs:subClassOf None \n",
- "301 aio:ESN Echo State Network rdfs:subClassOf None \n",
- "302 aio:ESN Echo State Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "298 aio:ANN Artificial Neural Network \n",
- "299 aio:DNN Deep Neural Network \n",
- "300 aio:ESN Echo State Network \n",
- "301 aio:Network Network \n",
- "302 aio:RecNN Recurrent Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ESN\n",
- " subject subject_label predicate predicate_label \\\n",
- "298 aio:ESN Echo State Network rdfs:subClassOf None \n",
- "299 aio:ESN Echo State Network rdfs:subClassOf None \n",
- "300 aio:ESN Echo State Network rdfs:subClassOf None \n",
- "301 aio:ESN Echo State Network rdfs:subClassOf None \n",
- "302 aio:ESN Echo State Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "298 aio:ANN Artificial Neural Network \n",
- "299 aio:DNN Deep Neural Network \n",
- "300 aio:ESN Echo State Network \n",
- "301 aio:Network Network \n",
- "302 aio:RecNN Recurrent Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ESN\n",
- " subject subject_label predicate predicate_label \\\n",
- "298 aio:ESN Echo State Network rdfs:subClassOf None \n",
- "299 aio:ESN Echo State Network rdfs:subClassOf None \n",
- "300 aio:ESN Echo State Network rdfs:subClassOf None \n",
- "301 aio:ESN Echo State Network rdfs:subClassOf None \n",
- "302 aio:ESN Echo State Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "298 aio:ANN Artificial Neural Network \n",
- "299 aio:DNN Deep Neural Network \n",
- "300 aio:ESN Echo State Network \n",
- "301 aio:Network Network \n",
- "302 aio:RecNN Recurrent Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Ecological_Fallacy_Bias\n",
- " subject subject_label predicate \\\n",
- "303 aio:Ecological_Fallacy_Bias Ecological Fallacy Bias rdfs:subClassOf \n",
- "304 aio:Ecological_Fallacy_Bias Ecological Fallacy Bias rdfs:subClassOf \n",
- "305 aio:Ecological_Fallacy_Bias Ecological Fallacy Bias rdfs:subClassOf \n",
- "306 aio:Ecological_Fallacy_Bias Ecological Fallacy Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "303 None aio:Bias \n",
- "304 None aio:Computational_Bias \n",
- "305 None aio:Ecological_Fallacy_Bias \n",
- "306 None aio:Selection_And_Sampling_Bias \n",
- "\n",
- " object_label \n",
- "303 Bias \n",
- "304 Computational Bias \n",
- "305 Ecological Fallacy Bias \n",
- "306 Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Ecological_Fallacy_Bias\n",
- " subject subject_label predicate \\\n",
- "303 aio:Ecological_Fallacy_Bias Ecological Fallacy Bias rdfs:subClassOf \n",
- "304 aio:Ecological_Fallacy_Bias Ecological Fallacy Bias rdfs:subClassOf \n",
- "305 aio:Ecological_Fallacy_Bias Ecological Fallacy Bias rdfs:subClassOf \n",
- "306 aio:Ecological_Fallacy_Bias Ecological Fallacy Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "303 None aio:Bias \n",
- "304 None aio:Computational_Bias \n",
- "305 None aio:Ecological_Fallacy_Bias \n",
- "306 None aio:Selection_And_Sampling_Bias \n",
- "\n",
- " object_label \n",
- "303 Bias \n",
- "304 Computational Bias \n",
- "305 Ecological Fallacy Bias \n",
- "306 Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Ecological_Fallacy_Bias\n",
- " subject subject_label predicate \\\n",
- "303 aio:Ecological_Fallacy_Bias Ecological Fallacy Bias rdfs:subClassOf \n",
- "304 aio:Ecological_Fallacy_Bias Ecological Fallacy Bias rdfs:subClassOf \n",
- "305 aio:Ecological_Fallacy_Bias Ecological Fallacy Bias rdfs:subClassOf \n",
- "306 aio:Ecological_Fallacy_Bias Ecological Fallacy Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "303 None aio:Bias \n",
- "304 None aio:Computational_Bias \n",
- "305 None aio:Ecological_Fallacy_Bias \n",
- "306 None aio:Selection_And_Sampling_Bias \n",
- "\n",
- " object_label \n",
- "303 Bias \n",
- "304 Computational Bias \n",
- "305 Ecological Fallacy Bias \n",
- "306 Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Ecological_Fallacy_Bias\n",
- " subject subject_label predicate \\\n",
- "303 aio:Ecological_Fallacy_Bias Ecological Fallacy Bias rdfs:subClassOf \n",
- "304 aio:Ecological_Fallacy_Bias Ecological Fallacy Bias rdfs:subClassOf \n",
- "305 aio:Ecological_Fallacy_Bias Ecological Fallacy Bias rdfs:subClassOf \n",
- "306 aio:Ecological_Fallacy_Bias Ecological Fallacy Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "303 None aio:Bias \n",
- "304 None aio:Computational_Bias \n",
- "305 None aio:Ecological_Fallacy_Bias \n",
- "306 None aio:Selection_And_Sampling_Bias \n",
- "\n",
- " object_label \n",
- "303 Bias \n",
- "304 Computational Bias \n",
- "305 Ecological Fallacy Bias \n",
- "306 Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Embedding_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "307 aio:Embedding_Layer Embedding Layer rdfs:subClassOf None \n",
- "308 aio:Embedding_Layer Embedding Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "307 aio:Embedding_Layer Embedding Layer \n",
- "308 aio:Layer Layer \n",
- "parent aio:Embedding_Layer\n",
- "aio:Embedding_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "307 aio:Embedding_Layer Embedding Layer rdfs:subClassOf None \n",
- "308 aio:Embedding_Layer Embedding Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "307 aio:Embedding_Layer Embedding Layer \n",
- "308 aio:Layer Layer \n",
- "parent aio:Embedding_Layer\n",
- "aio:Emergent_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "309 aio:Emergent_Bias Emergent Bias rdfs:subClassOf None \n",
- "310 aio:Emergent_Bias Emergent Bias rdfs:subClassOf None \n",
- "311 aio:Emergent_Bias Emergent Bias rdfs:subClassOf None \n",
- "312 aio:Emergent_Bias Emergent Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "309 aio:Bias Bias \n",
- "310 aio:Computational_Bias Computational Bias \n",
- "311 aio:Emergent_Bias Emergent Bias \n",
- "312 aio:Use_And_Interpretation_Bias Use And Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:Emergent_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "309 aio:Emergent_Bias Emergent Bias rdfs:subClassOf None \n",
- "310 aio:Emergent_Bias Emergent Bias rdfs:subClassOf None \n",
- "311 aio:Emergent_Bias Emergent Bias rdfs:subClassOf None \n",
- "312 aio:Emergent_Bias Emergent Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "309 aio:Bias Bias \n",
- "310 aio:Computational_Bias Computational Bias \n",
- "311 aio:Emergent_Bias Emergent Bias \n",
- "312 aio:Use_And_Interpretation_Bias Use And Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:Emergent_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "309 aio:Emergent_Bias Emergent Bias rdfs:subClassOf None \n",
- "310 aio:Emergent_Bias Emergent Bias rdfs:subClassOf None \n",
- "311 aio:Emergent_Bias Emergent Bias rdfs:subClassOf None \n",
- "312 aio:Emergent_Bias Emergent Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "309 aio:Bias Bias \n",
- "310 aio:Computational_Bias Computational Bias \n",
- "311 aio:Emergent_Bias Emergent Bias \n",
- "312 aio:Use_And_Interpretation_Bias Use And Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:Emergent_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "309 aio:Emergent_Bias Emergent Bias rdfs:subClassOf None \n",
- "310 aio:Emergent_Bias Emergent Bias rdfs:subClassOf None \n",
- "311 aio:Emergent_Bias Emergent Bias rdfs:subClassOf None \n",
- "312 aio:Emergent_Bias Emergent Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "309 aio:Bias Bias \n",
- "310 aio:Computational_Bias Computational Bias \n",
- "311 aio:Emergent_Bias Emergent Bias \n",
- "312 aio:Use_And_Interpretation_Bias Use And Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:Ensemble_Learning\n",
- " subject subject_label predicate \\\n",
- "313 aio:Ensemble_Learning Ensemble Learning rdfs:subClassOf \n",
- "314 aio:Ensemble_Learning Ensemble Learning rdfs:subClassOf \n",
- "315 aio:Ensemble_Learning Ensemble Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "313 None aio:Ensemble_Learning Ensemble Learning \n",
- "314 None aio:Machine_Learning Machine Learning \n",
- "315 None aio:Method Method \n",
- "parent aio:Ensemble_Learning\n",
- "aio:Ensemble_Learning\n",
- " subject subject_label predicate \\\n",
- "313 aio:Ensemble_Learning Ensemble Learning rdfs:subClassOf \n",
- "314 aio:Ensemble_Learning Ensemble Learning rdfs:subClassOf \n",
- "315 aio:Ensemble_Learning Ensemble Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "313 None aio:Ensemble_Learning Ensemble Learning \n",
- "314 None aio:Machine_Learning Machine Learning \n",
- "315 None aio:Method Method \n",
- "parent aio:Ensemble_Learning\n",
- "aio:Ensemble_Learning\n",
- " subject subject_label predicate \\\n",
- "313 aio:Ensemble_Learning Ensemble Learning rdfs:subClassOf \n",
- "314 aio:Ensemble_Learning Ensemble Learning rdfs:subClassOf \n",
- "315 aio:Ensemble_Learning Ensemble Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "313 None aio:Ensemble_Learning Ensemble Learning \n",
- "314 None aio:Machine_Learning Machine Learning \n",
- "315 None aio:Method Method \n",
- "parent aio:Ensemble_Learning\n",
- "aio:Error_Propagation_Bias\n",
- " subject subject_label predicate \\\n",
- "316 aio:Error_Propagation_Bias Error Propagation Bias rdfs:subClassOf \n",
- "317 aio:Error_Propagation_Bias Error Propagation Bias rdfs:subClassOf \n",
- "318 aio:Error_Propagation_Bias Error Propagation Bias rdfs:subClassOf \n",
- "319 aio:Error_Propagation_Bias Error Propagation Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "316 None aio:Bias Bias \n",
- "317 None aio:Computational_Bias Computational Bias \n",
- "318 None aio:Error_Propagation_Bias Error Propagation Bias \n",
- "319 None aio:Processing_Bias Processing Bias \n",
- "parent aio:Bias\n",
- "aio:Error_Propagation_Bias\n",
- " subject subject_label predicate \\\n",
- "316 aio:Error_Propagation_Bias Error Propagation Bias rdfs:subClassOf \n",
- "317 aio:Error_Propagation_Bias Error Propagation Bias rdfs:subClassOf \n",
- "318 aio:Error_Propagation_Bias Error Propagation Bias rdfs:subClassOf \n",
- "319 aio:Error_Propagation_Bias Error Propagation Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "316 None aio:Bias Bias \n",
- "317 None aio:Computational_Bias Computational Bias \n",
- "318 None aio:Error_Propagation_Bias Error Propagation Bias \n",
- "319 None aio:Processing_Bias Processing Bias \n",
- "parent aio:Bias\n",
- "aio:Error_Propagation_Bias\n",
- " subject subject_label predicate \\\n",
- "316 aio:Error_Propagation_Bias Error Propagation Bias rdfs:subClassOf \n",
- "317 aio:Error_Propagation_Bias Error Propagation Bias rdfs:subClassOf \n",
- "318 aio:Error_Propagation_Bias Error Propagation Bias rdfs:subClassOf \n",
- "319 aio:Error_Propagation_Bias Error Propagation Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "316 None aio:Bias Bias \n",
- "317 None aio:Computational_Bias Computational Bias \n",
- "318 None aio:Error_Propagation_Bias Error Propagation Bias \n",
- "319 None aio:Processing_Bias Processing Bias \n",
- "parent aio:Bias\n",
- "aio:Error_Propagation_Bias\n",
- " subject subject_label predicate \\\n",
- "316 aio:Error_Propagation_Bias Error Propagation Bias rdfs:subClassOf \n",
- "317 aio:Error_Propagation_Bias Error Propagation Bias rdfs:subClassOf \n",
- "318 aio:Error_Propagation_Bias Error Propagation Bias rdfs:subClassOf \n",
- "319 aio:Error_Propagation_Bias Error Propagation Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "316 None aio:Bias Bias \n",
- "317 None aio:Computational_Bias Computational Bias \n",
- "318 None aio:Error_Propagation_Bias Error Propagation Bias \n",
- "319 None aio:Processing_Bias Processing Bias \n",
- "parent aio:Bias\n",
- "aio:Evaluation_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "320 aio:Evaluation_Bias Evaluation Bias rdfs:subClassOf None \n",
- "321 aio:Evaluation_Bias Evaluation Bias rdfs:subClassOf None \n",
- "322 aio:Evaluation_Bias Evaluation Bias rdfs:subClassOf None \n",
- "323 aio:Evaluation_Bias Evaluation Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "320 aio:Bias Bias \n",
- "321 aio:Computational_Bias Computational Bias \n",
- "322 aio:Evaluation_Bias Evaluation Bias \n",
- "323 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Evaluation_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "320 aio:Evaluation_Bias Evaluation Bias rdfs:subClassOf None \n",
- "321 aio:Evaluation_Bias Evaluation Bias rdfs:subClassOf None \n",
- "322 aio:Evaluation_Bias Evaluation Bias rdfs:subClassOf None \n",
- "323 aio:Evaluation_Bias Evaluation Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "320 aio:Bias Bias \n",
- "321 aio:Computational_Bias Computational Bias \n",
- "322 aio:Evaluation_Bias Evaluation Bias \n",
- "323 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Evaluation_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "320 aio:Evaluation_Bias Evaluation Bias rdfs:subClassOf None \n",
- "321 aio:Evaluation_Bias Evaluation Bias rdfs:subClassOf None \n",
- "322 aio:Evaluation_Bias Evaluation Bias rdfs:subClassOf None \n",
- "323 aio:Evaluation_Bias Evaluation Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "320 aio:Bias Bias \n",
- "321 aio:Computational_Bias Computational Bias \n",
- "322 aio:Evaluation_Bias Evaluation Bias \n",
- "323 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Evaluation_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "320 aio:Evaluation_Bias Evaluation Bias rdfs:subClassOf None \n",
- "321 aio:Evaluation_Bias Evaluation Bias rdfs:subClassOf None \n",
- "322 aio:Evaluation_Bias Evaluation Bias rdfs:subClassOf None \n",
- "323 aio:Evaluation_Bias Evaluation Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "320 aio:Bias Bias \n",
- "321 aio:Computational_Bias Computational Bias \n",
- "322 aio:Evaluation_Bias Evaluation Bias \n",
- "323 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Exclusion_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "324 aio:Exclusion_Bias Exclusion Bias rdfs:subClassOf None \n",
- "325 aio:Exclusion_Bias Exclusion Bias rdfs:subClassOf None \n",
- "326 aio:Exclusion_Bias Exclusion Bias rdfs:subClassOf None \n",
- "327 aio:Exclusion_Bias Exclusion Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "324 aio:Bias Bias \n",
- "325 aio:Computational_Bias Computational Bias \n",
- "326 aio:Exclusion_Bias Exclusion Bias \n",
- "327 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Exclusion_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "324 aio:Exclusion_Bias Exclusion Bias rdfs:subClassOf None \n",
- "325 aio:Exclusion_Bias Exclusion Bias rdfs:subClassOf None \n",
- "326 aio:Exclusion_Bias Exclusion Bias rdfs:subClassOf None \n",
- "327 aio:Exclusion_Bias Exclusion Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "324 aio:Bias Bias \n",
- "325 aio:Computational_Bias Computational Bias \n",
- "326 aio:Exclusion_Bias Exclusion Bias \n",
- "327 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Exclusion_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "324 aio:Exclusion_Bias Exclusion Bias rdfs:subClassOf None \n",
- "325 aio:Exclusion_Bias Exclusion Bias rdfs:subClassOf None \n",
- "326 aio:Exclusion_Bias Exclusion Bias rdfs:subClassOf None \n",
- "327 aio:Exclusion_Bias Exclusion Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "324 aio:Bias Bias \n",
- "325 aio:Computational_Bias Computational Bias \n",
- "326 aio:Exclusion_Bias Exclusion Bias \n",
- "327 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Exclusion_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "324 aio:Exclusion_Bias Exclusion Bias rdfs:subClassOf None \n",
- "325 aio:Exclusion_Bias Exclusion Bias rdfs:subClassOf None \n",
- "326 aio:Exclusion_Bias Exclusion Bias rdfs:subClassOf None \n",
- "327 aio:Exclusion_Bias Exclusion Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "324 aio:Bias Bias \n",
- "325 aio:Computational_Bias Computational Bias \n",
- "326 aio:Exclusion_Bias Exclusion Bias \n",
- "327 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Exponential_Function\n",
- " subject subject_label predicate \\\n",
- "328 aio:Exponential_Function Exponential Function rdfs:subClassOf \n",
- "329 aio:Exponential_Function Exponential Function rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "328 None aio:Exponential_Function Exponential Function \n",
- "329 None aio:Function None \n",
- "parent aio:Exponential_Function\n",
- "aio:Exponential_Function\n",
- " subject subject_label predicate \\\n",
- "328 aio:Exponential_Function Exponential Function rdfs:subClassOf \n",
- "329 aio:Exponential_Function Exponential Function rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "328 None aio:Exponential_Function Exponential Function \n",
- "329 None aio:Function None \n",
- "parent aio:Exponential_Function\n",
- "aio:FBN\n",
- " subject subject_label predicate predicate_label object \\\n",
- "330 aio:FBN Feedback Network rdfs:subClassOf None aio:ANN \n",
- "331 aio:FBN Feedback Network rdfs:subClassOf None aio:FBN \n",
- "332 aio:FBN Feedback Network rdfs:subClassOf None aio:Network \n",
- "\n",
- " object_label \n",
- "330 Artificial Neural Network \n",
- "331 Feedback Network \n",
- "332 Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:FBN\n",
- " subject subject_label predicate predicate_label object \\\n",
- "330 aio:FBN Feedback Network rdfs:subClassOf None aio:ANN \n",
- "331 aio:FBN Feedback Network rdfs:subClassOf None aio:FBN \n",
- "332 aio:FBN Feedback Network rdfs:subClassOf None aio:Network \n",
- "\n",
- " object_label \n",
- "330 Artificial Neural Network \n",
- "331 Feedback Network \n",
- "332 Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:FBN\n",
- " subject subject_label predicate predicate_label object \\\n",
- "330 aio:FBN Feedback Network rdfs:subClassOf None aio:ANN \n",
- "331 aio:FBN Feedback Network rdfs:subClassOf None aio:FBN \n",
- "332 aio:FBN Feedback Network rdfs:subClassOf None aio:Network \n",
- "\n",
- " object_label \n",
- "330 Artificial Neural Network \n",
- "331 Feedback Network \n",
- "332 Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Federated_Learning\n",
- " subject subject_label predicate \\\n",
- "333 aio:Federated_Learning Federated Learning rdfs:subClassOf \n",
- "334 aio:Federated_Learning Federated Learning rdfs:subClassOf \n",
- "335 aio:Federated_Learning Federated Learning rdfs:subClassOf \n",
- "336 aio:Federated_Learning Federated Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "333 None aio:ANN Artificial Neural Network \n",
- "334 None aio:DNN Deep Neural Network \n",
- "335 None aio:Federated_Learning Federated Learning \n",
- "336 None aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Federated_Learning\n",
- " subject subject_label predicate \\\n",
- "333 aio:Federated_Learning Federated Learning rdfs:subClassOf \n",
- "334 aio:Federated_Learning Federated Learning rdfs:subClassOf \n",
- "335 aio:Federated_Learning Federated Learning rdfs:subClassOf \n",
- "336 aio:Federated_Learning Federated Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "333 None aio:ANN Artificial Neural Network \n",
- "334 None aio:DNN Deep Neural Network \n",
- "335 None aio:Federated_Learning Federated Learning \n",
- "336 None aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Federated_Learning\n",
- " subject subject_label predicate \\\n",
- "333 aio:Federated_Learning Federated Learning rdfs:subClassOf \n",
- "334 aio:Federated_Learning Federated Learning rdfs:subClassOf \n",
- "335 aio:Federated_Learning Federated Learning rdfs:subClassOf \n",
- "336 aio:Federated_Learning Federated Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "333 None aio:ANN Artificial Neural Network \n",
- "334 None aio:DNN Deep Neural Network \n",
- "335 None aio:Federated_Learning Federated Learning \n",
- "336 None aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Federated_Learning\n",
- " subject subject_label predicate \\\n",
- "333 aio:Federated_Learning Federated Learning rdfs:subClassOf \n",
- "334 aio:Federated_Learning Federated Learning rdfs:subClassOf \n",
- "335 aio:Federated_Learning Federated Learning rdfs:subClassOf \n",
- "336 aio:Federated_Learning Federated Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "333 None aio:ANN Artificial Neural Network \n",
- "334 None aio:DNN Deep Neural Network \n",
- "335 None aio:Federated_Learning Federated Learning \n",
- "336 None aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Feedback_Loop_Bias\n",
- " subject subject_label predicate \\\n",
- "337 aio:Feedback_Loop_Bias Feedback Loop Bias rdfs:subClassOf \n",
- "338 aio:Feedback_Loop_Bias Feedback Loop Bias rdfs:subClassOf \n",
- "339 aio:Feedback_Loop_Bias Feedback Loop Bias rdfs:subClassOf \n",
- "340 aio:Feedback_Loop_Bias Feedback Loop Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "337 None aio:Bias \n",
- "338 None aio:Computational_Bias \n",
- "339 None aio:Feedback_Loop_Bias \n",
- "340 None aio:Use_And_Interpretation_Bias \n",
- "\n",
- " object_label \n",
- "337 Bias \n",
- "338 Computational Bias \n",
- "339 Feedback Loop Bias \n",
- "340 Use And Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:Feedback_Loop_Bias\n",
- " subject subject_label predicate \\\n",
- "337 aio:Feedback_Loop_Bias Feedback Loop Bias rdfs:subClassOf \n",
- "338 aio:Feedback_Loop_Bias Feedback Loop Bias rdfs:subClassOf \n",
- "339 aio:Feedback_Loop_Bias Feedback Loop Bias rdfs:subClassOf \n",
- "340 aio:Feedback_Loop_Bias Feedback Loop Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "337 None aio:Bias \n",
- "338 None aio:Computational_Bias \n",
- "339 None aio:Feedback_Loop_Bias \n",
- "340 None aio:Use_And_Interpretation_Bias \n",
- "\n",
- " object_label \n",
- "337 Bias \n",
- "338 Computational Bias \n",
- "339 Feedback Loop Bias \n",
- "340 Use And Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:Feedback_Loop_Bias\n",
- " subject subject_label predicate \\\n",
- "337 aio:Feedback_Loop_Bias Feedback Loop Bias rdfs:subClassOf \n",
- "338 aio:Feedback_Loop_Bias Feedback Loop Bias rdfs:subClassOf \n",
- "339 aio:Feedback_Loop_Bias Feedback Loop Bias rdfs:subClassOf \n",
- "340 aio:Feedback_Loop_Bias Feedback Loop Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "337 None aio:Bias \n",
- "338 None aio:Computational_Bias \n",
- "339 None aio:Feedback_Loop_Bias \n",
- "340 None aio:Use_And_Interpretation_Bias \n",
- "\n",
- " object_label \n",
- "337 Bias \n",
- "338 Computational Bias \n",
- "339 Feedback Loop Bias \n",
- "340 Use And Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:Feedback_Loop_Bias\n",
- " subject subject_label predicate \\\n",
- "337 aio:Feedback_Loop_Bias Feedback Loop Bias rdfs:subClassOf \n",
- "338 aio:Feedback_Loop_Bias Feedback Loop Bias rdfs:subClassOf \n",
- "339 aio:Feedback_Loop_Bias Feedback Loop Bias rdfs:subClassOf \n",
- "340 aio:Feedback_Loop_Bias Feedback Loop Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "337 None aio:Bias \n",
- "338 None aio:Computational_Bias \n",
- "339 None aio:Feedback_Loop_Bias \n",
- "340 None aio:Use_And_Interpretation_Bias \n",
- "\n",
- " object_label \n",
- "337 Bias \n",
- "338 Computational Bias \n",
- "339 Feedback Loop Bias \n",
- "340 Use And Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:Fixed_Effects_Model\n",
- " subject subject_label predicate \\\n",
- "341 aio:Fixed_Effects_Model Fixed Effects Model rdfs:subClassOf \n",
- "342 aio:Fixed_Effects_Model Fixed Effects Model rdfs:subClassOf \n",
- "343 aio:Fixed_Effects_Model Fixed Effects Model rdfs:subClassOf \n",
- "344 aio:Fixed_Effects_Model Fixed Effects Model rdfs:subClassOf \n",
- "345 aio:Fixed_Effects_Model Fixed Effects Model rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "341 None aio:Fixed_Effects_Model Fixed Effects Model \n",
- "342 None aio:Machine_Learning Machine Learning \n",
- "343 None aio:Method Method \n",
- "344 None aio:Regression_Analysis Regression Analysis \n",
- "345 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Fixed_Effects_Model\n",
- "aio:Fixed_Effects_Model\n",
- " subject subject_label predicate \\\n",
- "341 aio:Fixed_Effects_Model Fixed Effects Model rdfs:subClassOf \n",
- "342 aio:Fixed_Effects_Model Fixed Effects Model rdfs:subClassOf \n",
- "343 aio:Fixed_Effects_Model Fixed Effects Model rdfs:subClassOf \n",
- "344 aio:Fixed_Effects_Model Fixed Effects Model rdfs:subClassOf \n",
- "345 aio:Fixed_Effects_Model Fixed Effects Model rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "341 None aio:Fixed_Effects_Model Fixed Effects Model \n",
- "342 None aio:Machine_Learning Machine Learning \n",
- "343 None aio:Method Method \n",
- "344 None aio:Regression_Analysis Regression Analysis \n",
- "345 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Fixed_Effects_Model\n",
- "aio:Fixed_Effects_Model\n",
- " subject subject_label predicate \\\n",
- "341 aio:Fixed_Effects_Model Fixed Effects Model rdfs:subClassOf \n",
- "342 aio:Fixed_Effects_Model Fixed Effects Model rdfs:subClassOf \n",
- "343 aio:Fixed_Effects_Model Fixed Effects Model rdfs:subClassOf \n",
- "344 aio:Fixed_Effects_Model Fixed Effects Model rdfs:subClassOf \n",
- "345 aio:Fixed_Effects_Model Fixed Effects Model rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "341 None aio:Fixed_Effects_Model Fixed Effects Model \n",
- "342 None aio:Machine_Learning Machine Learning \n",
- "343 None aio:Method Method \n",
- "344 None aio:Regression_Analysis Regression Analysis \n",
- "345 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Fixed_Effects_Model\n",
- "aio:Fixed_Effects_Model\n",
- " subject subject_label predicate \\\n",
- "341 aio:Fixed_Effects_Model Fixed Effects Model rdfs:subClassOf \n",
- "342 aio:Fixed_Effects_Model Fixed Effects Model rdfs:subClassOf \n",
- "343 aio:Fixed_Effects_Model Fixed Effects Model rdfs:subClassOf \n",
- "344 aio:Fixed_Effects_Model Fixed Effects Model rdfs:subClassOf \n",
- "345 aio:Fixed_Effects_Model Fixed Effects Model rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "341 None aio:Fixed_Effects_Model Fixed Effects Model \n",
- "342 None aio:Machine_Learning Machine Learning \n",
- "343 None aio:Method Method \n",
- "344 None aio:Regression_Analysis Regression Analysis \n",
- "345 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Fixed_Effects_Model\n",
- "aio:Fixed_Effects_Model\n",
- " subject subject_label predicate \\\n",
- "341 aio:Fixed_Effects_Model Fixed Effects Model rdfs:subClassOf \n",
- "342 aio:Fixed_Effects_Model Fixed Effects Model rdfs:subClassOf \n",
- "343 aio:Fixed_Effects_Model Fixed Effects Model rdfs:subClassOf \n",
- "344 aio:Fixed_Effects_Model Fixed Effects Model rdfs:subClassOf \n",
- "345 aio:Fixed_Effects_Model Fixed Effects Model rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "341 None aio:Fixed_Effects_Model Fixed Effects Model \n",
- "342 None aio:Machine_Learning Machine Learning \n",
- "343 None aio:Method Method \n",
- "344 None aio:Regression_Analysis Regression Analysis \n",
- "345 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Fixed_Effects_Model\n",
- "aio:Flatten_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "346 aio:Flatten_Layer Flatten Layer rdfs:subClassOf None \n",
- "347 aio:Flatten_Layer Flatten Layer rdfs:subClassOf None \n",
- "348 aio:Flatten_Layer Flatten Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "346 aio:Flatten_Layer Flatten Layer \n",
- "347 aio:Layer Layer \n",
- "348 aio:Reshaping_Layer Reshaping Layer \n",
- "parent aio:Flatten_Layer\n",
- "aio:Flatten_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "346 aio:Flatten_Layer Flatten Layer rdfs:subClassOf None \n",
- "347 aio:Flatten_Layer Flatten Layer rdfs:subClassOf None \n",
- "348 aio:Flatten_Layer Flatten Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "346 aio:Flatten_Layer Flatten Layer \n",
- "347 aio:Layer Layer \n",
- "348 aio:Reshaping_Layer Reshaping Layer \n",
- "parent aio:Flatten_Layer\n",
- "aio:Flatten_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "346 aio:Flatten_Layer Flatten Layer rdfs:subClassOf None \n",
- "347 aio:Flatten_Layer Flatten Layer rdfs:subClassOf None \n",
- "348 aio:Flatten_Layer Flatten Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "346 aio:Flatten_Layer Flatten Layer \n",
- "347 aio:Layer Layer \n",
- "348 aio:Reshaping_Layer Reshaping Layer \n",
- "parent aio:Flatten_Layer\n",
- "aio:FractionalMaxPool2D_Layer\n",
- " subject subject_label \\\n",
- "349 aio:FractionalMaxPool2D_Layer FractionalMaxPool2D Layer \n",
- "350 aio:FractionalMaxPool2D_Layer FractionalMaxPool2D Layer \n",
- "351 aio:FractionalMaxPool2D_Layer FractionalMaxPool2D Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "349 rdfs:subClassOf None aio:FractionalMaxPool2D_Layer \n",
- "350 rdfs:subClassOf None aio:Layer \n",
- "351 rdfs:subClassOf None aio:Pooling_Layer \n",
- "\n",
- " object_label \n",
- "349 FractionalMaxPool2D Layer \n",
- "350 Layer \n",
- "351 Pooling Layer \n",
- "parent aio:FractionalMaxPool2D_Layer\n",
- "aio:FractionalMaxPool2D_Layer\n",
- " subject subject_label \\\n",
- "349 aio:FractionalMaxPool2D_Layer FractionalMaxPool2D Layer \n",
- "350 aio:FractionalMaxPool2D_Layer FractionalMaxPool2D Layer \n",
- "351 aio:FractionalMaxPool2D_Layer FractionalMaxPool2D Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "349 rdfs:subClassOf None aio:FractionalMaxPool2D_Layer \n",
- "350 rdfs:subClassOf None aio:Layer \n",
- "351 rdfs:subClassOf None aio:Pooling_Layer \n",
- "\n",
- " object_label \n",
- "349 FractionalMaxPool2D Layer \n",
- "350 Layer \n",
- "351 Pooling Layer \n",
- "parent aio:FractionalMaxPool2D_Layer\n",
- "aio:FractionalMaxPool2D_Layer\n",
- " subject subject_label \\\n",
- "349 aio:FractionalMaxPool2D_Layer FractionalMaxPool2D Layer \n",
- "350 aio:FractionalMaxPool2D_Layer FractionalMaxPool2D Layer \n",
- "351 aio:FractionalMaxPool2D_Layer FractionalMaxPool2D Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "349 rdfs:subClassOf None aio:FractionalMaxPool2D_Layer \n",
- "350 rdfs:subClassOf None aio:Layer \n",
- "351 rdfs:subClassOf None aio:Pooling_Layer \n",
- "\n",
- " object_label \n",
- "349 FractionalMaxPool2D Layer \n",
- "350 Layer \n",
- "351 Pooling Layer \n",
- "parent aio:FractionalMaxPool2D_Layer\n",
- "aio:FractionalMaxPool3D_Layer\n",
- " subject subject_label \\\n",
- "352 aio:FractionalMaxPool3D_Layer FractionalMaxPool3D Layer \n",
- "353 aio:FractionalMaxPool3D_Layer FractionalMaxPool3D Layer \n",
- "354 aio:FractionalMaxPool3D_Layer FractionalMaxPool3D Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "352 rdfs:subClassOf None aio:FractionalMaxPool3D_Layer \n",
- "353 rdfs:subClassOf None aio:Layer \n",
- "354 rdfs:subClassOf None aio:Pooling_Layer \n",
- "\n",
- " object_label \n",
- "352 FractionalMaxPool3D Layer \n",
- "353 Layer \n",
- "354 Pooling Layer \n",
- "parent aio:FractionalMaxPool3D_Layer\n",
- "aio:FractionalMaxPool3D_Layer\n",
- " subject subject_label \\\n",
- "352 aio:FractionalMaxPool3D_Layer FractionalMaxPool3D Layer \n",
- "353 aio:FractionalMaxPool3D_Layer FractionalMaxPool3D Layer \n",
- "354 aio:FractionalMaxPool3D_Layer FractionalMaxPool3D Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "352 rdfs:subClassOf None aio:FractionalMaxPool3D_Layer \n",
- "353 rdfs:subClassOf None aio:Layer \n",
- "354 rdfs:subClassOf None aio:Pooling_Layer \n",
- "\n",
- " object_label \n",
- "352 FractionalMaxPool3D Layer \n",
- "353 Layer \n",
- "354 Pooling Layer \n",
- "parent aio:FractionalMaxPool3D_Layer\n",
- "aio:FractionalMaxPool3D_Layer\n",
- " subject subject_label \\\n",
- "352 aio:FractionalMaxPool3D_Layer FractionalMaxPool3D Layer \n",
- "353 aio:FractionalMaxPool3D_Layer FractionalMaxPool3D Layer \n",
- "354 aio:FractionalMaxPool3D_Layer FractionalMaxPool3D Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "352 rdfs:subClassOf None aio:FractionalMaxPool3D_Layer \n",
- "353 rdfs:subClassOf None aio:Layer \n",
- "354 rdfs:subClassOf None aio:Pooling_Layer \n",
- "\n",
- " object_label \n",
- "352 FractionalMaxPool3D Layer \n",
- "353 Layer \n",
- "354 Pooling Layer \n",
- "parent aio:FractionalMaxPool3D_Layer\n",
- "aio:Funding_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "355 aio:Funding_Bias Funding Bias rdfs:subClassOf None \n",
- "356 aio:Funding_Bias Funding Bias rdfs:subClassOf None \n",
- "357 aio:Funding_Bias Funding Bias rdfs:subClassOf None \n",
- "358 aio:Funding_Bias Funding Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "355 aio:Bias Bias \n",
- "356 aio:Funding_Bias Funding Bias \n",
- "357 aio:Group_Bias Group Bias \n",
- "358 aio:Human_Bias Human Bias \n",
- "parent aio:Bias\n",
- "aio:Funding_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "355 aio:Funding_Bias Funding Bias rdfs:subClassOf None \n",
- "356 aio:Funding_Bias Funding Bias rdfs:subClassOf None \n",
- "357 aio:Funding_Bias Funding Bias rdfs:subClassOf None \n",
- "358 aio:Funding_Bias Funding Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "355 aio:Bias Bias \n",
- "356 aio:Funding_Bias Funding Bias \n",
- "357 aio:Group_Bias Group Bias \n",
- "358 aio:Human_Bias Human Bias \n",
- "parent aio:Bias\n",
- "aio:Funding_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "355 aio:Funding_Bias Funding Bias rdfs:subClassOf None \n",
- "356 aio:Funding_Bias Funding Bias rdfs:subClassOf None \n",
- "357 aio:Funding_Bias Funding Bias rdfs:subClassOf None \n",
- "358 aio:Funding_Bias Funding Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "355 aio:Bias Bias \n",
- "356 aio:Funding_Bias Funding Bias \n",
- "357 aio:Group_Bias Group Bias \n",
- "358 aio:Human_Bias Human Bias \n",
- "parent aio:Bias\n",
- "aio:Funding_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "355 aio:Funding_Bias Funding Bias rdfs:subClassOf None \n",
- "356 aio:Funding_Bias Funding Bias rdfs:subClassOf None \n",
- "357 aio:Funding_Bias Funding Bias rdfs:subClassOf None \n",
- "358 aio:Funding_Bias Funding Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "355 aio:Bias Bias \n",
- "356 aio:Funding_Bias Funding Bias \n",
- "357 aio:Group_Bias Group Bias \n",
- "358 aio:Human_Bias Human Bias \n",
- "parent aio:Bias\n",
- "aio:GAN\n",
- " subject subject_label predicate predicate_label \\\n",
- "359 aio:GAN Generative Adversarial Network rdfs:subClassOf None \n",
- "360 aio:GAN Generative Adversarial Network rdfs:subClassOf None \n",
- "361 aio:GAN Generative Adversarial Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "359 aio:GAN Generative Adversarial Network \n",
- "360 aio:Network Network \n",
- "361 aio:UPN Unsupervised Pretrained Network \n",
- "parent aio:GAN\n",
- "aio:GAN\n",
- " subject subject_label predicate predicate_label \\\n",
- "359 aio:GAN Generative Adversarial Network rdfs:subClassOf None \n",
- "360 aio:GAN Generative Adversarial Network rdfs:subClassOf None \n",
- "361 aio:GAN Generative Adversarial Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "359 aio:GAN Generative Adversarial Network \n",
- "360 aio:Network Network \n",
- "361 aio:UPN Unsupervised Pretrained Network \n",
- "parent aio:GAN\n",
- "aio:GAN\n",
- " subject subject_label predicate predicate_label \\\n",
- "359 aio:GAN Generative Adversarial Network rdfs:subClassOf None \n",
- "360 aio:GAN Generative Adversarial Network rdfs:subClassOf None \n",
- "361 aio:GAN Generative Adversarial Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "359 aio:GAN Generative Adversarial Network \n",
- "360 aio:Network Network \n",
- "361 aio:UPN Unsupervised Pretrained Network \n",
- "parent aio:GAN\n",
- "aio:GCN\n",
- " subject subject_label predicate predicate_label \\\n",
- "362 aio:GCN Graph Convolutional Network rdfs:subClassOf None \n",
- "363 aio:GCN Graph Convolutional Network rdfs:subClassOf None \n",
- "364 aio:GCN Graph Convolutional Network rdfs:subClassOf None \n",
- "365 aio:GCN Graph Convolutional Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "362 aio:ANN Artificial Neural Network \n",
- "363 aio:DNN Deep Neural Network \n",
- "364 aio:GCN Graph Convolutional Network \n",
- "365 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:GCN\n",
- " subject subject_label predicate predicate_label \\\n",
- "362 aio:GCN Graph Convolutional Network rdfs:subClassOf None \n",
- "363 aio:GCN Graph Convolutional Network rdfs:subClassOf None \n",
- "364 aio:GCN Graph Convolutional Network rdfs:subClassOf None \n",
- "365 aio:GCN Graph Convolutional Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "362 aio:ANN Artificial Neural Network \n",
- "363 aio:DNN Deep Neural Network \n",
- "364 aio:GCN Graph Convolutional Network \n",
- "365 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:GCN\n",
- " subject subject_label predicate predicate_label \\\n",
- "362 aio:GCN Graph Convolutional Network rdfs:subClassOf None \n",
- "363 aio:GCN Graph Convolutional Network rdfs:subClassOf None \n",
- "364 aio:GCN Graph Convolutional Network rdfs:subClassOf None \n",
- "365 aio:GCN Graph Convolutional Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "362 aio:ANN Artificial Neural Network \n",
- "363 aio:DNN Deep Neural Network \n",
- "364 aio:GCN Graph Convolutional Network \n",
- "365 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:GCN\n",
- " subject subject_label predicate predicate_label \\\n",
- "362 aio:GCN Graph Convolutional Network rdfs:subClassOf None \n",
- "363 aio:GCN Graph Convolutional Network rdfs:subClassOf None \n",
- "364 aio:GCN Graph Convolutional Network rdfs:subClassOf None \n",
- "365 aio:GCN Graph Convolutional Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "362 aio:ANN Artificial Neural Network \n",
- "363 aio:DNN Deep Neural Network \n",
- "364 aio:GCN Graph Convolutional Network \n",
- "365 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:GPCN\n",
- " subject subject_label predicate \\\n",
- "366 aio:GPCN Graph Convolutional Policy Network rdfs:subClassOf \n",
- "367 aio:GPCN Graph Convolutional Policy Network rdfs:subClassOf \n",
- "368 aio:GPCN Graph Convolutional Policy Network rdfs:subClassOf \n",
- "369 aio:GPCN Graph Convolutional Policy Network rdfs:subClassOf \n",
- "370 aio:GPCN Graph Convolutional Policy Network rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "366 None aio:ANN Artificial Neural Network \n",
- "367 None aio:DNN Deep Neural Network \n",
- "368 None aio:GCN Graph Convolutional Network \n",
- "369 None aio:GPCN Graph Convolutional Policy Network \n",
- "370 None aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:GPCN\n",
- " subject subject_label predicate \\\n",
- "366 aio:GPCN Graph Convolutional Policy Network rdfs:subClassOf \n",
- "367 aio:GPCN Graph Convolutional Policy Network rdfs:subClassOf \n",
- "368 aio:GPCN Graph Convolutional Policy Network rdfs:subClassOf \n",
- "369 aio:GPCN Graph Convolutional Policy Network rdfs:subClassOf \n",
- "370 aio:GPCN Graph Convolutional Policy Network rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "366 None aio:ANN Artificial Neural Network \n",
- "367 None aio:DNN Deep Neural Network \n",
- "368 None aio:GCN Graph Convolutional Network \n",
- "369 None aio:GPCN Graph Convolutional Policy Network \n",
- "370 None aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:GPCN\n",
- " subject subject_label predicate \\\n",
- "366 aio:GPCN Graph Convolutional Policy Network rdfs:subClassOf \n",
- "367 aio:GPCN Graph Convolutional Policy Network rdfs:subClassOf \n",
- "368 aio:GPCN Graph Convolutional Policy Network rdfs:subClassOf \n",
- "369 aio:GPCN Graph Convolutional Policy Network rdfs:subClassOf \n",
- "370 aio:GPCN Graph Convolutional Policy Network rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "366 None aio:ANN Artificial Neural Network \n",
- "367 None aio:DNN Deep Neural Network \n",
- "368 None aio:GCN Graph Convolutional Network \n",
- "369 None aio:GPCN Graph Convolutional Policy Network \n",
- "370 None aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:GPCN\n",
- " subject subject_label predicate \\\n",
- "366 aio:GPCN Graph Convolutional Policy Network rdfs:subClassOf \n",
- "367 aio:GPCN Graph Convolutional Policy Network rdfs:subClassOf \n",
- "368 aio:GPCN Graph Convolutional Policy Network rdfs:subClassOf \n",
- "369 aio:GPCN Graph Convolutional Policy Network rdfs:subClassOf \n",
- "370 aio:GPCN Graph Convolutional Policy Network rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "366 None aio:ANN Artificial Neural Network \n",
- "367 None aio:DNN Deep Neural Network \n",
- "368 None aio:GCN Graph Convolutional Network \n",
- "369 None aio:GPCN Graph Convolutional Policy Network \n",
- "370 None aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:GPCN\n",
- " subject subject_label predicate \\\n",
- "366 aio:GPCN Graph Convolutional Policy Network rdfs:subClassOf \n",
- "367 aio:GPCN Graph Convolutional Policy Network rdfs:subClassOf \n",
- "368 aio:GPCN Graph Convolutional Policy Network rdfs:subClassOf \n",
- "369 aio:GPCN Graph Convolutional Policy Network rdfs:subClassOf \n",
- "370 aio:GPCN Graph Convolutional Policy Network rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "366 None aio:ANN Artificial Neural Network \n",
- "367 None aio:DNN Deep Neural Network \n",
- "368 None aio:GCN Graph Convolutional Network \n",
- "369 None aio:GPCN Graph Convolutional Policy Network \n",
- "370 None aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:GRU\n",
- " subject subject_label predicate predicate_label \\\n",
- "371 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "372 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "373 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "374 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "375 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "376 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "371 aio:ANN Artificial Neural Network \n",
- "372 aio:DNN Deep Neural Network \n",
- "373 aio:GRU Gated Recurrent Unit \n",
- "374 aio:LSTM Long Short Term Memory \n",
- "375 aio:Network Network \n",
- "376 aio:RecNN Recurrent Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:GRU\n",
- " subject subject_label predicate predicate_label \\\n",
- "371 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "372 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "373 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "374 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "375 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "376 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "371 aio:ANN Artificial Neural Network \n",
- "372 aio:DNN Deep Neural Network \n",
- "373 aio:GRU Gated Recurrent Unit \n",
- "374 aio:LSTM Long Short Term Memory \n",
- "375 aio:Network Network \n",
- "376 aio:RecNN Recurrent Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:GRU\n",
- " subject subject_label predicate predicate_label \\\n",
- "371 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "372 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "373 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "374 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "375 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "376 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "371 aio:ANN Artificial Neural Network \n",
- "372 aio:DNN Deep Neural Network \n",
- "373 aio:GRU Gated Recurrent Unit \n",
- "374 aio:LSTM Long Short Term Memory \n",
- "375 aio:Network Network \n",
- "376 aio:RecNN Recurrent Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:GRU\n",
- " subject subject_label predicate predicate_label \\\n",
- "371 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "372 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "373 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "374 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "375 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "376 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "371 aio:ANN Artificial Neural Network \n",
- "372 aio:DNN Deep Neural Network \n",
- "373 aio:GRU Gated Recurrent Unit \n",
- "374 aio:LSTM Long Short Term Memory \n",
- "375 aio:Network Network \n",
- "376 aio:RecNN Recurrent Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:GRU\n",
- " subject subject_label predicate predicate_label \\\n",
- "371 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "372 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "373 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "374 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "375 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "376 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "371 aio:ANN Artificial Neural Network \n",
- "372 aio:DNN Deep Neural Network \n",
- "373 aio:GRU Gated Recurrent Unit \n",
- "374 aio:LSTM Long Short Term Memory \n",
- "375 aio:Network Network \n",
- "376 aio:RecNN Recurrent Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:GRU\n",
- " subject subject_label predicate predicate_label \\\n",
- "371 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "372 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "373 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "374 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "375 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "376 aio:GRU Gated Recurrent Unit rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "371 aio:ANN Artificial Neural Network \n",
- "372 aio:DNN Deep Neural Network \n",
- "373 aio:GRU Gated Recurrent Unit \n",
- "374 aio:LSTM Long Short Term Memory \n",
- "375 aio:Network Network \n",
- "376 aio:RecNN Recurrent Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:GRUCell_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "377 aio:GRUCell_Layer GRUCell Layer rdfs:subClassOf None \n",
- "378 aio:GRUCell_Layer GRUCell Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "377 aio:GRUCell_Layer GRUCell Layer \n",
- "378 aio:Layer Layer \n",
- "parent aio:GRUCell_Layer\n",
- "aio:GRUCell_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "377 aio:GRUCell_Layer GRUCell Layer rdfs:subClassOf None \n",
- "378 aio:GRUCell_Layer GRUCell Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "377 aio:GRUCell_Layer GRUCell Layer \n",
- "378 aio:Layer Layer \n",
- "parent aio:GRUCell_Layer\n",
- "aio:GRU_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "379 aio:GRU_Layer GRU Layer rdfs:subClassOf None \n",
- "380 aio:GRU_Layer GRU Layer rdfs:subClassOf None \n",
- "381 aio:GRU_Layer GRU Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "379 aio:GRU_Layer GRU Layer \n",
- "380 aio:Layer Layer \n",
- "381 aio:Recurrent_Layer Recurrent Layer \n",
- "parent aio:GRU_Layer\n",
- "aio:GRU_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "379 aio:GRU_Layer GRU Layer rdfs:subClassOf None \n",
- "380 aio:GRU_Layer GRU Layer rdfs:subClassOf None \n",
- "381 aio:GRU_Layer GRU Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "379 aio:GRU_Layer GRU Layer \n",
- "380 aio:Layer Layer \n",
- "381 aio:Recurrent_Layer Recurrent Layer \n",
- "parent aio:GRU_Layer\n",
- "aio:GRU_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "379 aio:GRU_Layer GRU Layer rdfs:subClassOf None \n",
- "380 aio:GRU_Layer GRU Layer rdfs:subClassOf None \n",
- "381 aio:GRU_Layer GRU Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "379 aio:GRU_Layer GRU Layer \n",
- "380 aio:Layer Layer \n",
- "381 aio:Recurrent_Layer Recurrent Layer \n",
- "parent aio:GRU_Layer\n",
- "aio:GaussianDropout_Layer\n",
- " subject subject_label predicate \\\n",
- "382 aio:GaussianDropout_Layer GaussianDropout Layer rdfs:subClassOf \n",
- "383 aio:GaussianDropout_Layer GaussianDropout Layer rdfs:subClassOf \n",
- "384 aio:GaussianDropout_Layer GaussianDropout Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "382 None aio:GaussianDropout_Layer GaussianDropout Layer \n",
- "383 None aio:Layer Layer \n",
- "384 None aio:Regularization_Layer Regularization Layer \n",
- "parent aio:GaussianDropout_Layer\n",
- "aio:GaussianDropout_Layer\n",
- " subject subject_label predicate \\\n",
- "382 aio:GaussianDropout_Layer GaussianDropout Layer rdfs:subClassOf \n",
- "383 aio:GaussianDropout_Layer GaussianDropout Layer rdfs:subClassOf \n",
- "384 aio:GaussianDropout_Layer GaussianDropout Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "382 None aio:GaussianDropout_Layer GaussianDropout Layer \n",
- "383 None aio:Layer Layer \n",
- "384 None aio:Regularization_Layer Regularization Layer \n",
- "parent aio:GaussianDropout_Layer\n",
- "aio:GaussianDropout_Layer\n",
- " subject subject_label predicate \\\n",
- "382 aio:GaussianDropout_Layer GaussianDropout Layer rdfs:subClassOf \n",
- "383 aio:GaussianDropout_Layer GaussianDropout Layer rdfs:subClassOf \n",
- "384 aio:GaussianDropout_Layer GaussianDropout Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "382 None aio:GaussianDropout_Layer GaussianDropout Layer \n",
- "383 None aio:Layer Layer \n",
- "384 None aio:Regularization_Layer Regularization Layer \n",
- "parent aio:GaussianDropout_Layer\n",
- "aio:GaussianNoise_Layer\n",
- " subject subject_label predicate \\\n",
- "385 aio:GaussianNoise_Layer GaussianNoise Layer rdfs:subClassOf \n",
- "386 aio:GaussianNoise_Layer GaussianNoise Layer rdfs:subClassOf \n",
- "387 aio:GaussianNoise_Layer GaussianNoise Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "385 None aio:GaussianNoise_Layer GaussianNoise Layer \n",
- "386 None aio:Layer Layer \n",
- "387 None aio:Regularization_Layer Regularization Layer \n",
- "parent aio:GaussianNoise_Layer\n",
- "aio:GaussianNoise_Layer\n",
- " subject subject_label predicate \\\n",
- "385 aio:GaussianNoise_Layer GaussianNoise Layer rdfs:subClassOf \n",
- "386 aio:GaussianNoise_Layer GaussianNoise Layer rdfs:subClassOf \n",
- "387 aio:GaussianNoise_Layer GaussianNoise Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "385 None aio:GaussianNoise_Layer GaussianNoise Layer \n",
- "386 None aio:Layer Layer \n",
- "387 None aio:Regularization_Layer Regularization Layer \n",
- "parent aio:GaussianNoise_Layer\n",
- "aio:GaussianNoise_Layer\n",
- " subject subject_label predicate \\\n",
- "385 aio:GaussianNoise_Layer GaussianNoise Layer rdfs:subClassOf \n",
- "386 aio:GaussianNoise_Layer GaussianNoise Layer rdfs:subClassOf \n",
- "387 aio:GaussianNoise_Layer GaussianNoise Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "385 None aio:GaussianNoise_Layer GaussianNoise Layer \n",
- "386 None aio:Layer Layer \n",
- "387 None aio:Regularization_Layer Regularization Layer \n",
- "parent aio:GaussianNoise_Layer\n",
- "aio:GeLu_Function\n",
- " subject subject_label predicate predicate_label \\\n",
- "388 aio:GeLu_Function GELU Function rdfs:subClassOf None \n",
- "389 aio:GeLu_Function GELU Function rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "388 aio:Function None \n",
- "389 aio:GeLu_Function GELU Function \n",
- "parent aio:Function\n",
- "aio:GeLu_Function\n",
- " subject subject_label predicate predicate_label \\\n",
- "388 aio:GeLu_Function GELU Function rdfs:subClassOf None \n",
- "389 aio:GeLu_Function GELU Function rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "388 aio:Function None \n",
- "389 aio:GeLu_Function GELU Function \n",
- "parent aio:Function\n",
- "aio:Generalized_Few-shot_Learning\n",
- " subject subject_label \\\n",
- "390 aio:Generalized_Few-shot_Learning Generalized Few-shot Learning \n",
- "391 aio:Generalized_Few-shot_Learning Generalized Few-shot Learning \n",
- "392 aio:Generalized_Few-shot_Learning Generalized Few-shot Learning \n",
- "393 aio:Generalized_Few-shot_Learning Generalized Few-shot Learning \n",
- "\n",
- " predicate predicate_label object \\\n",
- "390 rdfs:subClassOf None aio:ANN \n",
- "391 rdfs:subClassOf None aio:DNN \n",
- "392 rdfs:subClassOf None aio:Generalized_Few-shot_Learning \n",
- "393 rdfs:subClassOf None aio:Network \n",
- "\n",
- " object_label \n",
- "390 Artificial Neural Network \n",
- "391 Deep Neural Network \n",
- "392 Generalized Few-shot Learning \n",
- "393 Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Generalized_Few-shot_Learning\n",
- " subject subject_label \\\n",
- "390 aio:Generalized_Few-shot_Learning Generalized Few-shot Learning \n",
- "391 aio:Generalized_Few-shot_Learning Generalized Few-shot Learning \n",
- "392 aio:Generalized_Few-shot_Learning Generalized Few-shot Learning \n",
- "393 aio:Generalized_Few-shot_Learning Generalized Few-shot Learning \n",
- "\n",
- " predicate predicate_label object \\\n",
- "390 rdfs:subClassOf None aio:ANN \n",
- "391 rdfs:subClassOf None aio:DNN \n",
- "392 rdfs:subClassOf None aio:Generalized_Few-shot_Learning \n",
- "393 rdfs:subClassOf None aio:Network \n",
- "\n",
- " object_label \n",
- "390 Artificial Neural Network \n",
- "391 Deep Neural Network \n",
- "392 Generalized Few-shot Learning \n",
- "393 Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Generalized_Few-shot_Learning\n",
- " subject subject_label \\\n",
- "390 aio:Generalized_Few-shot_Learning Generalized Few-shot Learning \n",
- "391 aio:Generalized_Few-shot_Learning Generalized Few-shot Learning \n",
- "392 aio:Generalized_Few-shot_Learning Generalized Few-shot Learning \n",
- "393 aio:Generalized_Few-shot_Learning Generalized Few-shot Learning \n",
- "\n",
- " predicate predicate_label object \\\n",
- "390 rdfs:subClassOf None aio:ANN \n",
- "391 rdfs:subClassOf None aio:DNN \n",
- "392 rdfs:subClassOf None aio:Generalized_Few-shot_Learning \n",
- "393 rdfs:subClassOf None aio:Network \n",
- "\n",
- " object_label \n",
- "390 Artificial Neural Network \n",
- "391 Deep Neural Network \n",
- "392 Generalized Few-shot Learning \n",
- "393 Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Generalized_Few-shot_Learning\n",
- " subject subject_label \\\n",
- "390 aio:Generalized_Few-shot_Learning Generalized Few-shot Learning \n",
- "391 aio:Generalized_Few-shot_Learning Generalized Few-shot Learning \n",
- "392 aio:Generalized_Few-shot_Learning Generalized Few-shot Learning \n",
- "393 aio:Generalized_Few-shot_Learning Generalized Few-shot Learning \n",
- "\n",
- " predicate predicate_label object \\\n",
- "390 rdfs:subClassOf None aio:ANN \n",
- "391 rdfs:subClassOf None aio:DNN \n",
- "392 rdfs:subClassOf None aio:Generalized_Few-shot_Learning \n",
- "393 rdfs:subClassOf None aio:Network \n",
- "\n",
- " object_label \n",
- "390 Artificial Neural Network \n",
- "391 Deep Neural Network \n",
- "392 Generalized Few-shot Learning \n",
- "393 Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Generalized_Linear_Model\n",
- " subject subject_label predicate \\\n",
- "394 aio:Generalized_Linear_Model Generalized Linear Model rdfs:subClassOf \n",
- "395 aio:Generalized_Linear_Model Generalized Linear Model rdfs:subClassOf \n",
- "396 aio:Generalized_Linear_Model Generalized Linear Model rdfs:subClassOf \n",
- "397 aio:Generalized_Linear_Model Generalized Linear Model rdfs:subClassOf \n",
- "398 aio:Generalized_Linear_Model Generalized Linear Model rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "394 None aio:Generalized_Linear_Model Generalized Linear Model \n",
- "395 None aio:Machine_Learning Machine Learning \n",
- "396 None aio:Method Method \n",
- "397 None aio:Regression_Analysis Regression Analysis \n",
- "398 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Generalized_Linear_Model\n",
- "aio:Generalized_Linear_Model\n",
- " subject subject_label predicate \\\n",
- "394 aio:Generalized_Linear_Model Generalized Linear Model rdfs:subClassOf \n",
- "395 aio:Generalized_Linear_Model Generalized Linear Model rdfs:subClassOf \n",
- "396 aio:Generalized_Linear_Model Generalized Linear Model rdfs:subClassOf \n",
- "397 aio:Generalized_Linear_Model Generalized Linear Model rdfs:subClassOf \n",
- "398 aio:Generalized_Linear_Model Generalized Linear Model rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "394 None aio:Generalized_Linear_Model Generalized Linear Model \n",
- "395 None aio:Machine_Learning Machine Learning \n",
- "396 None aio:Method Method \n",
- "397 None aio:Regression_Analysis Regression Analysis \n",
- "398 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Generalized_Linear_Model\n",
- "aio:Generalized_Linear_Model\n",
- " subject subject_label predicate \\\n",
- "394 aio:Generalized_Linear_Model Generalized Linear Model rdfs:subClassOf \n",
- "395 aio:Generalized_Linear_Model Generalized Linear Model rdfs:subClassOf \n",
- "396 aio:Generalized_Linear_Model Generalized Linear Model rdfs:subClassOf \n",
- "397 aio:Generalized_Linear_Model Generalized Linear Model rdfs:subClassOf \n",
- "398 aio:Generalized_Linear_Model Generalized Linear Model rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "394 None aio:Generalized_Linear_Model Generalized Linear Model \n",
- "395 None aio:Machine_Learning Machine Learning \n",
- "396 None aio:Method Method \n",
- "397 None aio:Regression_Analysis Regression Analysis \n",
- "398 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Generalized_Linear_Model\n",
- "aio:Generalized_Linear_Model\n",
- " subject subject_label predicate \\\n",
- "394 aio:Generalized_Linear_Model Generalized Linear Model rdfs:subClassOf \n",
- "395 aio:Generalized_Linear_Model Generalized Linear Model rdfs:subClassOf \n",
- "396 aio:Generalized_Linear_Model Generalized Linear Model rdfs:subClassOf \n",
- "397 aio:Generalized_Linear_Model Generalized Linear Model rdfs:subClassOf \n",
- "398 aio:Generalized_Linear_Model Generalized Linear Model rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "394 None aio:Generalized_Linear_Model Generalized Linear Model \n",
- "395 None aio:Machine_Learning Machine Learning \n",
- "396 None aio:Method Method \n",
- "397 None aio:Regression_Analysis Regression Analysis \n",
- "398 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Generalized_Linear_Model\n",
- "aio:Generalized_Linear_Model\n",
- " subject subject_label predicate \\\n",
- "394 aio:Generalized_Linear_Model Generalized Linear Model rdfs:subClassOf \n",
- "395 aio:Generalized_Linear_Model Generalized Linear Model rdfs:subClassOf \n",
- "396 aio:Generalized_Linear_Model Generalized Linear Model rdfs:subClassOf \n",
- "397 aio:Generalized_Linear_Model Generalized Linear Model rdfs:subClassOf \n",
- "398 aio:Generalized_Linear_Model Generalized Linear Model rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "394 None aio:Generalized_Linear_Model Generalized Linear Model \n",
- "395 None aio:Machine_Learning Machine Learning \n",
- "396 None aio:Method Method \n",
- "397 None aio:Regression_Analysis Regression Analysis \n",
- "398 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Generalized_Linear_Model\n",
- "aio:GlobalAveragePooling1D_Layer\n",
- " subject subject_label \\\n",
- "399 aio:GlobalAveragePooling1D_Layer GlobalAveragePooling1D Layer \n",
- "400 aio:GlobalAveragePooling1D_Layer GlobalAveragePooling1D Layer \n",
- "401 aio:GlobalAveragePooling1D_Layer GlobalAveragePooling1D Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "399 rdfs:subClassOf None aio:GlobalAveragePooling1D_Layer \n",
- "400 rdfs:subClassOf None aio:Layer \n",
- "401 rdfs:subClassOf None aio:Pooling_Layer \n",
- "\n",
- " object_label \n",
- "399 GlobalAveragePooling1D Layer \n",
- "400 Layer \n",
- "401 Pooling Layer \n",
- "parent aio:GlobalAveragePooling1D_Layer\n",
- "aio:GlobalAveragePooling1D_Layer\n",
- " subject subject_label \\\n",
- "399 aio:GlobalAveragePooling1D_Layer GlobalAveragePooling1D Layer \n",
- "400 aio:GlobalAveragePooling1D_Layer GlobalAveragePooling1D Layer \n",
- "401 aio:GlobalAveragePooling1D_Layer GlobalAveragePooling1D Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "399 rdfs:subClassOf None aio:GlobalAveragePooling1D_Layer \n",
- "400 rdfs:subClassOf None aio:Layer \n",
- "401 rdfs:subClassOf None aio:Pooling_Layer \n",
- "\n",
- " object_label \n",
- "399 GlobalAveragePooling1D Layer \n",
- "400 Layer \n",
- "401 Pooling Layer \n",
- "parent aio:GlobalAveragePooling1D_Layer\n",
- "aio:GlobalAveragePooling1D_Layer\n",
- " subject subject_label \\\n",
- "399 aio:GlobalAveragePooling1D_Layer GlobalAveragePooling1D Layer \n",
- "400 aio:GlobalAveragePooling1D_Layer GlobalAveragePooling1D Layer \n",
- "401 aio:GlobalAveragePooling1D_Layer GlobalAveragePooling1D Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "399 rdfs:subClassOf None aio:GlobalAveragePooling1D_Layer \n",
- "400 rdfs:subClassOf None aio:Layer \n",
- "401 rdfs:subClassOf None aio:Pooling_Layer \n",
- "\n",
- " object_label \n",
- "399 GlobalAveragePooling1D Layer \n",
- "400 Layer \n",
- "401 Pooling Layer \n",
- "parent aio:GlobalAveragePooling1D_Layer\n",
- "aio:GlobalAveragePooling2D_Layer\n",
- " subject subject_label \\\n",
- "402 aio:GlobalAveragePooling2D_Layer GlobalAveragePooling2D Layer \n",
- "403 aio:GlobalAveragePooling2D_Layer GlobalAveragePooling2D Layer \n",
- "404 aio:GlobalAveragePooling2D_Layer GlobalAveragePooling2D Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "402 rdfs:subClassOf None aio:GlobalAveragePooling2D_Layer \n",
- "403 rdfs:subClassOf None aio:Layer \n",
- "404 rdfs:subClassOf None aio:Pooling_Layer \n",
- "\n",
- " object_label \n",
- "402 GlobalAveragePooling2D Layer \n",
- "403 Layer \n",
- "404 Pooling Layer \n",
- "parent aio:GlobalAveragePooling2D_Layer\n",
- "aio:GlobalAveragePooling2D_Layer\n",
- " subject subject_label \\\n",
- "402 aio:GlobalAveragePooling2D_Layer GlobalAveragePooling2D Layer \n",
- "403 aio:GlobalAveragePooling2D_Layer GlobalAveragePooling2D Layer \n",
- "404 aio:GlobalAveragePooling2D_Layer GlobalAveragePooling2D Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "402 rdfs:subClassOf None aio:GlobalAveragePooling2D_Layer \n",
- "403 rdfs:subClassOf None aio:Layer \n",
- "404 rdfs:subClassOf None aio:Pooling_Layer \n",
- "\n",
- " object_label \n",
- "402 GlobalAveragePooling2D Layer \n",
- "403 Layer \n",
- "404 Pooling Layer \n",
- "parent aio:GlobalAveragePooling2D_Layer\n",
- "aio:GlobalAveragePooling2D_Layer\n",
- " subject subject_label \\\n",
- "402 aio:GlobalAveragePooling2D_Layer GlobalAveragePooling2D Layer \n",
- "403 aio:GlobalAveragePooling2D_Layer GlobalAveragePooling2D Layer \n",
- "404 aio:GlobalAveragePooling2D_Layer GlobalAveragePooling2D Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "402 rdfs:subClassOf None aio:GlobalAveragePooling2D_Layer \n",
- "403 rdfs:subClassOf None aio:Layer \n",
- "404 rdfs:subClassOf None aio:Pooling_Layer \n",
- "\n",
- " object_label \n",
- "402 GlobalAveragePooling2D Layer \n",
- "403 Layer \n",
- "404 Pooling Layer \n",
- "parent aio:GlobalAveragePooling2D_Layer\n",
- "aio:GlobalAveragePooling3D_Layer\n",
- " subject subject_label \\\n",
- "405 aio:GlobalAveragePooling3D_Layer GlobalAveragePooling3D Layer \n",
- "406 aio:GlobalAveragePooling3D_Layer GlobalAveragePooling3D Layer \n",
- "407 aio:GlobalAveragePooling3D_Layer GlobalAveragePooling3D Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "405 rdfs:subClassOf None aio:GlobalAveragePooling3D_Layer \n",
- "406 rdfs:subClassOf None aio:Layer \n",
- "407 rdfs:subClassOf None aio:Pooling_Layer \n",
- "\n",
- " object_label \n",
- "405 GlobalAveragePooling3D Layer \n",
- "406 Layer \n",
- "407 Pooling Layer \n",
- "parent aio:GlobalAveragePooling3D_Layer\n",
- "aio:GlobalAveragePooling3D_Layer\n",
- " subject subject_label \\\n",
- "405 aio:GlobalAveragePooling3D_Layer GlobalAveragePooling3D Layer \n",
- "406 aio:GlobalAveragePooling3D_Layer GlobalAveragePooling3D Layer \n",
- "407 aio:GlobalAveragePooling3D_Layer GlobalAveragePooling3D Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "405 rdfs:subClassOf None aio:GlobalAveragePooling3D_Layer \n",
- "406 rdfs:subClassOf None aio:Layer \n",
- "407 rdfs:subClassOf None aio:Pooling_Layer \n",
- "\n",
- " object_label \n",
- "405 GlobalAveragePooling3D Layer \n",
- "406 Layer \n",
- "407 Pooling Layer \n",
- "parent aio:GlobalAveragePooling3D_Layer\n",
- "aio:GlobalAveragePooling3D_Layer\n",
- " subject subject_label \\\n",
- "405 aio:GlobalAveragePooling3D_Layer GlobalAveragePooling3D Layer \n",
- "406 aio:GlobalAveragePooling3D_Layer GlobalAveragePooling3D Layer \n",
- "407 aio:GlobalAveragePooling3D_Layer GlobalAveragePooling3D Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "405 rdfs:subClassOf None aio:GlobalAveragePooling3D_Layer \n",
- "406 rdfs:subClassOf None aio:Layer \n",
- "407 rdfs:subClassOf None aio:Pooling_Layer \n",
- "\n",
- " object_label \n",
- "405 GlobalAveragePooling3D Layer \n",
- "406 Layer \n",
- "407 Pooling Layer \n",
- "parent aio:GlobalAveragePooling3D_Layer\n",
- "aio:GlobalMaxPooling1D_Layer\n",
- " subject subject_label predicate \\\n",
- "408 aio:GlobalMaxPooling1D_Layer GlobalMaxPooling1D Layer rdfs:subClassOf \n",
- "409 aio:GlobalMaxPooling1D_Layer GlobalMaxPooling1D Layer rdfs:subClassOf \n",
- "410 aio:GlobalMaxPooling1D_Layer GlobalMaxPooling1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "408 None aio:GlobalMaxPooling1D_Layer GlobalMaxPooling1D Layer \n",
- "409 None aio:Layer Layer \n",
- "410 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:GlobalMaxPooling1D_Layer\n",
- "aio:GlobalMaxPooling1D_Layer\n",
- " subject subject_label predicate \\\n",
- "408 aio:GlobalMaxPooling1D_Layer GlobalMaxPooling1D Layer rdfs:subClassOf \n",
- "409 aio:GlobalMaxPooling1D_Layer GlobalMaxPooling1D Layer rdfs:subClassOf \n",
- "410 aio:GlobalMaxPooling1D_Layer GlobalMaxPooling1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "408 None aio:GlobalMaxPooling1D_Layer GlobalMaxPooling1D Layer \n",
- "409 None aio:Layer Layer \n",
- "410 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:GlobalMaxPooling1D_Layer\n",
- "aio:GlobalMaxPooling1D_Layer\n",
- " subject subject_label predicate \\\n",
- "408 aio:GlobalMaxPooling1D_Layer GlobalMaxPooling1D Layer rdfs:subClassOf \n",
- "409 aio:GlobalMaxPooling1D_Layer GlobalMaxPooling1D Layer rdfs:subClassOf \n",
- "410 aio:GlobalMaxPooling1D_Layer GlobalMaxPooling1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "408 None aio:GlobalMaxPooling1D_Layer GlobalMaxPooling1D Layer \n",
- "409 None aio:Layer Layer \n",
- "410 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:GlobalMaxPooling1D_Layer\n",
- "aio:GlobalMaxPooling2D_Layer\n",
- " subject subject_label predicate \\\n",
- "411 aio:GlobalMaxPooling2D_Layer GlobalMaxPooling2D Layer rdfs:subClassOf \n",
- "412 aio:GlobalMaxPooling2D_Layer GlobalMaxPooling2D Layer rdfs:subClassOf \n",
- "413 aio:GlobalMaxPooling2D_Layer GlobalMaxPooling2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "411 None aio:GlobalMaxPooling2D_Layer GlobalMaxPooling2D Layer \n",
- "412 None aio:Layer Layer \n",
- "413 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:GlobalMaxPooling2D_Layer\n",
- "aio:GlobalMaxPooling2D_Layer\n",
- " subject subject_label predicate \\\n",
- "411 aio:GlobalMaxPooling2D_Layer GlobalMaxPooling2D Layer rdfs:subClassOf \n",
- "412 aio:GlobalMaxPooling2D_Layer GlobalMaxPooling2D Layer rdfs:subClassOf \n",
- "413 aio:GlobalMaxPooling2D_Layer GlobalMaxPooling2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "411 None aio:GlobalMaxPooling2D_Layer GlobalMaxPooling2D Layer \n",
- "412 None aio:Layer Layer \n",
- "413 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:GlobalMaxPooling2D_Layer\n",
- "aio:GlobalMaxPooling2D_Layer\n",
- " subject subject_label predicate \\\n",
- "411 aio:GlobalMaxPooling2D_Layer GlobalMaxPooling2D Layer rdfs:subClassOf \n",
- "412 aio:GlobalMaxPooling2D_Layer GlobalMaxPooling2D Layer rdfs:subClassOf \n",
- "413 aio:GlobalMaxPooling2D_Layer GlobalMaxPooling2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "411 None aio:GlobalMaxPooling2D_Layer GlobalMaxPooling2D Layer \n",
- "412 None aio:Layer Layer \n",
- "413 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:GlobalMaxPooling2D_Layer\n",
- "aio:GlobalMaxPooling3D_Layer\n",
- " subject subject_label predicate \\\n",
- "414 aio:GlobalMaxPooling3D_Layer GlobalMaxPooling3D Layer rdfs:subClassOf \n",
- "415 aio:GlobalMaxPooling3D_Layer GlobalMaxPooling3D Layer rdfs:subClassOf \n",
- "416 aio:GlobalMaxPooling3D_Layer GlobalMaxPooling3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "414 None aio:GlobalMaxPooling3D_Layer GlobalMaxPooling3D Layer \n",
- "415 None aio:Layer Layer \n",
- "416 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:GlobalMaxPooling3D_Layer\n",
- "aio:GlobalMaxPooling3D_Layer\n",
- " subject subject_label predicate \\\n",
- "414 aio:GlobalMaxPooling3D_Layer GlobalMaxPooling3D Layer rdfs:subClassOf \n",
- "415 aio:GlobalMaxPooling3D_Layer GlobalMaxPooling3D Layer rdfs:subClassOf \n",
- "416 aio:GlobalMaxPooling3D_Layer GlobalMaxPooling3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "414 None aio:GlobalMaxPooling3D_Layer GlobalMaxPooling3D Layer \n",
- "415 None aio:Layer Layer \n",
- "416 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:GlobalMaxPooling3D_Layer\n",
- "aio:GlobalMaxPooling3D_Layer\n",
- " subject subject_label predicate \\\n",
- "414 aio:GlobalMaxPooling3D_Layer GlobalMaxPooling3D Layer rdfs:subClassOf \n",
- "415 aio:GlobalMaxPooling3D_Layer GlobalMaxPooling3D Layer rdfs:subClassOf \n",
- "416 aio:GlobalMaxPooling3D_Layer GlobalMaxPooling3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "414 None aio:GlobalMaxPooling3D_Layer GlobalMaxPooling3D Layer \n",
- "415 None aio:Layer Layer \n",
- "416 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:GlobalMaxPooling3D_Layer\n",
- "aio:GroupNorm_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "417 aio:GroupNorm_Layer GroupNorm Layer rdfs:subClassOf None \n",
- "418 aio:GroupNorm_Layer GroupNorm Layer rdfs:subClassOf None \n",
- "419 aio:GroupNorm_Layer GroupNorm Layer rdfs:subClassOf None \n",
- "420 aio:GroupNorm_Layer GroupNorm Layer rdfs:subClassOf None \n",
- "\n",
- " object \\\n",
- "417 aio:GroupNorm_Layer \n",
- "418 aio:Layer \n",
- "419 aio:Normalization_Layer \n",
- "420 aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "417 GroupNorm Layer \n",
- "418 Layer \n",
- "419 Normalization Layer \n",
- "420 Numerical Features Preprocessing Layer \n",
- "parent aio:GroupNorm_Layer\n",
- "aio:GroupNorm_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "417 aio:GroupNorm_Layer GroupNorm Layer rdfs:subClassOf None \n",
- "418 aio:GroupNorm_Layer GroupNorm Layer rdfs:subClassOf None \n",
- "419 aio:GroupNorm_Layer GroupNorm Layer rdfs:subClassOf None \n",
- "420 aio:GroupNorm_Layer GroupNorm Layer rdfs:subClassOf None \n",
- "\n",
- " object \\\n",
- "417 aio:GroupNorm_Layer \n",
- "418 aio:Layer \n",
- "419 aio:Normalization_Layer \n",
- "420 aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "417 GroupNorm Layer \n",
- "418 Layer \n",
- "419 Normalization Layer \n",
- "420 Numerical Features Preprocessing Layer \n",
- "parent aio:GroupNorm_Layer\n",
- "aio:GroupNorm_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "417 aio:GroupNorm_Layer GroupNorm Layer rdfs:subClassOf None \n",
- "418 aio:GroupNorm_Layer GroupNorm Layer rdfs:subClassOf None \n",
- "419 aio:GroupNorm_Layer GroupNorm Layer rdfs:subClassOf None \n",
- "420 aio:GroupNorm_Layer GroupNorm Layer rdfs:subClassOf None \n",
- "\n",
- " object \\\n",
- "417 aio:GroupNorm_Layer \n",
- "418 aio:Layer \n",
- "419 aio:Normalization_Layer \n",
- "420 aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "417 GroupNorm Layer \n",
- "418 Layer \n",
- "419 Normalization Layer \n",
- "420 Numerical Features Preprocessing Layer \n",
- "parent aio:GroupNorm_Layer\n",
- "aio:GroupNorm_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "417 aio:GroupNorm_Layer GroupNorm Layer rdfs:subClassOf None \n",
- "418 aio:GroupNorm_Layer GroupNorm Layer rdfs:subClassOf None \n",
- "419 aio:GroupNorm_Layer GroupNorm Layer rdfs:subClassOf None \n",
- "420 aio:GroupNorm_Layer GroupNorm Layer rdfs:subClassOf None \n",
- "\n",
- " object \\\n",
- "417 aio:GroupNorm_Layer \n",
- "418 aio:Layer \n",
- "419 aio:Normalization_Layer \n",
- "420 aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "417 GroupNorm Layer \n",
- "418 Layer \n",
- "419 Normalization Layer \n",
- "420 Numerical Features Preprocessing Layer \n",
- "parent aio:GroupNorm_Layer\n",
- "aio:Group_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "421 aio:Group_Bias Group Bias rdfs:subClassOf None \n",
- "422 aio:Group_Bias Group Bias rdfs:subClassOf None \n",
- "423 aio:Group_Bias Group Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "421 aio:Bias Bias \n",
- "422 aio:Group_Bias Group Bias \n",
- "423 aio:Human_Bias Human Bias \n",
- "parent aio:Bias\n",
- "aio:Group_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "421 aio:Group_Bias Group Bias rdfs:subClassOf None \n",
- "422 aio:Group_Bias Group Bias rdfs:subClassOf None \n",
- "423 aio:Group_Bias Group Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "421 aio:Bias Bias \n",
- "422 aio:Group_Bias Group Bias \n",
- "423 aio:Human_Bias Human Bias \n",
- "parent aio:Bias\n",
- "aio:Group_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "421 aio:Group_Bias Group Bias rdfs:subClassOf None \n",
- "422 aio:Group_Bias Group Bias rdfs:subClassOf None \n",
- "423 aio:Group_Bias Group Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "421 aio:Bias Bias \n",
- "422 aio:Group_Bias Group Bias \n",
- "423 aio:Human_Bias Human Bias \n",
- "parent aio:Bias\n",
- "aio:Groupthink_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "424 aio:Groupthink_Bias Groupthink Bias rdfs:subClassOf None \n",
- "425 aio:Groupthink_Bias Groupthink Bias rdfs:subClassOf None \n",
- "426 aio:Groupthink_Bias Groupthink Bias rdfs:subClassOf None \n",
- "427 aio:Groupthink_Bias Groupthink Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "424 aio:Bias Bias \n",
- "425 aio:Group_Bias Group Bias \n",
- "426 aio:Groupthink_Bias Groupthink Bias \n",
- "427 aio:Human_Bias Human Bias \n",
- "parent aio:Bias\n",
- "aio:Groupthink_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "424 aio:Groupthink_Bias Groupthink Bias rdfs:subClassOf None \n",
- "425 aio:Groupthink_Bias Groupthink Bias rdfs:subClassOf None \n",
- "426 aio:Groupthink_Bias Groupthink Bias rdfs:subClassOf None \n",
- "427 aio:Groupthink_Bias Groupthink Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "424 aio:Bias Bias \n",
- "425 aio:Group_Bias Group Bias \n",
- "426 aio:Groupthink_Bias Groupthink Bias \n",
- "427 aio:Human_Bias Human Bias \n",
- "parent aio:Bias\n",
- "aio:Groupthink_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "424 aio:Groupthink_Bias Groupthink Bias rdfs:subClassOf None \n",
- "425 aio:Groupthink_Bias Groupthink Bias rdfs:subClassOf None \n",
- "426 aio:Groupthink_Bias Groupthink Bias rdfs:subClassOf None \n",
- "427 aio:Groupthink_Bias Groupthink Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "424 aio:Bias Bias \n",
- "425 aio:Group_Bias Group Bias \n",
- "426 aio:Groupthink_Bias Groupthink Bias \n",
- "427 aio:Human_Bias Human Bias \n",
- "parent aio:Bias\n",
- "aio:Groupthink_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "424 aio:Groupthink_Bias Groupthink Bias rdfs:subClassOf None \n",
- "425 aio:Groupthink_Bias Groupthink Bias rdfs:subClassOf None \n",
- "426 aio:Groupthink_Bias Groupthink Bias rdfs:subClassOf None \n",
- "427 aio:Groupthink_Bias Groupthink Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "424 aio:Bias Bias \n",
- "425 aio:Group_Bias Group Bias \n",
- "426 aio:Groupthink_Bias Groupthink Bias \n",
- "427 aio:Human_Bias Human Bias \n",
- "parent aio:Bias\n",
- "aio:HN\n",
- " subject subject_label predicate predicate_label object \\\n",
- "428 aio:HN Hopfield Network rdfs:subClassOf None aio:HN \n",
- "429 aio:HN Hopfield Network rdfs:subClassOf None aio:Network \n",
- "430 aio:HN Hopfield Network rdfs:subClassOf None aio:SCN \n",
- "\n",
- " object_label \n",
- "428 Hopfield Network \n",
- "429 Network \n",
- "430 Symmetrically Connected Network \n",
- "parent aio:HN\n",
- "aio:HN\n",
- " subject subject_label predicate predicate_label object \\\n",
- "428 aio:HN Hopfield Network rdfs:subClassOf None aio:HN \n",
- "429 aio:HN Hopfield Network rdfs:subClassOf None aio:Network \n",
- "430 aio:HN Hopfield Network rdfs:subClassOf None aio:SCN \n",
- "\n",
- " object_label \n",
- "428 Hopfield Network \n",
- "429 Network \n",
- "430 Symmetrically Connected Network \n",
- "parent aio:HN\n",
- "aio:HN\n",
- " subject subject_label predicate predicate_label object \\\n",
- "428 aio:HN Hopfield Network rdfs:subClassOf None aio:HN \n",
- "429 aio:HN Hopfield Network rdfs:subClassOf None aio:Network \n",
- "430 aio:HN Hopfield Network rdfs:subClassOf None aio:SCN \n",
- "\n",
- " object_label \n",
- "428 Hopfield Network \n",
- "429 Network \n",
- "430 Symmetrically Connected Network \n",
- "parent aio:HN\n",
- "aio:Hard_Sigmoid_Function\n",
- " subject subject_label predicate \\\n",
- "431 aio:Hard_Sigmoid_Function Hard Sigmoid Function rdfs:subClassOf \n",
- "432 aio:Hard_Sigmoid_Function Hard Sigmoid Function rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "431 None aio:Function None \n",
- "432 None aio:Hard_Sigmoid_Function Hard Sigmoid Function \n",
- "parent aio:Function\n",
- "aio:Hard_Sigmoid_Function\n",
- " subject subject_label predicate \\\n",
- "431 aio:Hard_Sigmoid_Function Hard Sigmoid Function rdfs:subClassOf \n",
- "432 aio:Hard_Sigmoid_Function Hard Sigmoid Function rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "431 None aio:Function None \n",
- "432 None aio:Hard_Sigmoid_Function Hard Sigmoid Function \n",
- "parent aio:Function\n",
- "aio:Hashing_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "433 aio:Hashing_Layer Hashing Layer rdfs:subClassOf None \n",
- "434 aio:Hashing_Layer Hashing Layer rdfs:subClassOf None \n",
- "435 aio:Hashing_Layer Hashing Layer rdfs:subClassOf None \n",
- "\n",
- " object \\\n",
- "433 aio:Categorical_Features_Preprocessing_Layer \n",
- "434 aio:Hashing_Layer \n",
- "435 aio:Layer \n",
- "\n",
- " object_label \n",
- "433 Categorical Features Preprocessing Layer \n",
- "434 Hashing Layer \n",
- "435 Layer \n",
- "parent aio:Categorical_Features_Preprocessing_Layer\n",
- "aio:Categorical_Features_Preprocessing_Layer\n",
- " subject \\\n",
- "128 aio:Categorical_Features_Preprocessing_Layer \n",
- "129 aio:Categorical_Features_Preprocessing_Layer \n",
- "\n",
- " subject_label predicate \\\n",
- "128 Categorical Features Preprocessing Layer rdfs:subClassOf \n",
- "129 Categorical Features Preprocessing Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "128 None aio:Categorical_Features_Preprocessing_Layer \n",
- "129 None aio:Layer \n",
- "\n",
- " object_label \n",
- "128 Categorical Features Preprocessing Layer \n",
- "129 Layer \n",
- "parent aio:Categorical_Features_Preprocessing_Layer\n",
- "aio:Hashing_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "433 aio:Hashing_Layer Hashing Layer rdfs:subClassOf None \n",
- "434 aio:Hashing_Layer Hashing Layer rdfs:subClassOf None \n",
- "435 aio:Hashing_Layer Hashing Layer rdfs:subClassOf None \n",
- "\n",
- " object \\\n",
- "433 aio:Categorical_Features_Preprocessing_Layer \n",
- "434 aio:Hashing_Layer \n",
- "435 aio:Layer \n",
- "\n",
- " object_label \n",
- "433 Categorical Features Preprocessing Layer \n",
- "434 Hashing Layer \n",
- "435 Layer \n",
- "parent aio:Categorical_Features_Preprocessing_Layer\n",
- "aio:Categorical_Features_Preprocessing_Layer\n",
- " subject \\\n",
- "128 aio:Categorical_Features_Preprocessing_Layer \n",
- "129 aio:Categorical_Features_Preprocessing_Layer \n",
- "\n",
- " subject_label predicate \\\n",
- "128 Categorical Features Preprocessing Layer rdfs:subClassOf \n",
- "129 Categorical Features Preprocessing Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "128 None aio:Categorical_Features_Preprocessing_Layer \n",
- "129 None aio:Layer \n",
- "\n",
- " object_label \n",
- "128 Categorical Features Preprocessing Layer \n",
- "129 Layer \n",
- "parent aio:Categorical_Features_Preprocessing_Layer\n",
- "aio:Hashing_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "433 aio:Hashing_Layer Hashing Layer rdfs:subClassOf None \n",
- "434 aio:Hashing_Layer Hashing Layer rdfs:subClassOf None \n",
- "435 aio:Hashing_Layer Hashing Layer rdfs:subClassOf None \n",
- "\n",
- " object \\\n",
- "433 aio:Categorical_Features_Preprocessing_Layer \n",
- "434 aio:Hashing_Layer \n",
- "435 aio:Layer \n",
- "\n",
- " object_label \n",
- "433 Categorical Features Preprocessing Layer \n",
- "434 Hashing Layer \n",
- "435 Layer \n",
- "parent aio:Categorical_Features_Preprocessing_Layer\n",
- "aio:Categorical_Features_Preprocessing_Layer\n",
- " subject \\\n",
- "128 aio:Categorical_Features_Preprocessing_Layer \n",
- "129 aio:Categorical_Features_Preprocessing_Layer \n",
- "\n",
- " subject_label predicate \\\n",
- "128 Categorical Features Preprocessing Layer rdfs:subClassOf \n",
- "129 Categorical Features Preprocessing Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "128 None aio:Categorical_Features_Preprocessing_Layer \n",
- "129 None aio:Layer \n",
- "\n",
- " object_label \n",
- "128 Categorical Features Preprocessing Layer \n",
- "129 Layer \n",
- "parent aio:Categorical_Features_Preprocessing_Layer\n",
- "aio:Hidden_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "436 aio:Hidden_Layer Hidden Layer rdfs:subClassOf None \n",
- "437 aio:Hidden_Layer Hidden Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "436 aio:Hidden_Layer Hidden Layer \n",
- "437 aio:Layer Layer \n",
- "parent aio:Hidden_Layer\n",
- "aio:Hidden_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "436 aio:Hidden_Layer Hidden Layer rdfs:subClassOf None \n",
- "437 aio:Hidden_Layer Hidden Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "436 aio:Hidden_Layer Hidden Layer \n",
- "437 aio:Layer Layer \n",
- "parent aio:Hidden_Layer\n",
- "aio:Hierarchical_Classification\n",
- " subject subject_label \\\n",
- "438 aio:Hierarchical_Classification Hierarchical Classification \n",
- "439 aio:Hierarchical_Classification Hierarchical Classification \n",
- "440 aio:Hierarchical_Classification Hierarchical Classification \n",
- "441 aio:Hierarchical_Classification Hierarchical Classification \n",
- "442 aio:Hierarchical_Classification Hierarchical Classification \n",
- "\n",
- " predicate predicate_label object \\\n",
- "438 rdfs:subClassOf None aio:Classification \n",
- "439 rdfs:subClassOf None aio:Hierarchical_Classification \n",
- "440 rdfs:subClassOf None aio:Machine_Learning \n",
- "441 rdfs:subClassOf None aio:Method \n",
- "442 rdfs:subClassOf None aio:Supervised_Learning \n",
- "\n",
- " object_label \n",
- "438 Classification \n",
- "439 Hierarchical Classification \n",
- "440 Machine Learning \n",
- "441 Method \n",
- "442 Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Classification\n",
- " subject subject_label predicate predicate_label \\\n",
- "140 aio:Classification Classification rdfs:subClassOf None \n",
- "141 aio:Classification Classification rdfs:subClassOf None \n",
- "142 aio:Classification Classification rdfs:subClassOf None \n",
- "143 aio:Classification Classification rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "140 aio:Classification Classification \n",
- "141 aio:Machine_Learning Machine Learning \n",
- "142 aio:Method Method \n",
- "143 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Hierarchical_Classification\n",
- " subject subject_label \\\n",
- "438 aio:Hierarchical_Classification Hierarchical Classification \n",
- "439 aio:Hierarchical_Classification Hierarchical Classification \n",
- "440 aio:Hierarchical_Classification Hierarchical Classification \n",
- "441 aio:Hierarchical_Classification Hierarchical Classification \n",
- "442 aio:Hierarchical_Classification Hierarchical Classification \n",
- "\n",
- " predicate predicate_label object \\\n",
- "438 rdfs:subClassOf None aio:Classification \n",
- "439 rdfs:subClassOf None aio:Hierarchical_Classification \n",
- "440 rdfs:subClassOf None aio:Machine_Learning \n",
- "441 rdfs:subClassOf None aio:Method \n",
- "442 rdfs:subClassOf None aio:Supervised_Learning \n",
- "\n",
- " object_label \n",
- "438 Classification \n",
- "439 Hierarchical Classification \n",
- "440 Machine Learning \n",
- "441 Method \n",
- "442 Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Classification\n",
- " subject subject_label predicate predicate_label \\\n",
- "140 aio:Classification Classification rdfs:subClassOf None \n",
- "141 aio:Classification Classification rdfs:subClassOf None \n",
- "142 aio:Classification Classification rdfs:subClassOf None \n",
- "143 aio:Classification Classification rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "140 aio:Classification Classification \n",
- "141 aio:Machine_Learning Machine Learning \n",
- "142 aio:Method Method \n",
- "143 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Hierarchical_Classification\n",
- " subject subject_label \\\n",
- "438 aio:Hierarchical_Classification Hierarchical Classification \n",
- "439 aio:Hierarchical_Classification Hierarchical Classification \n",
- "440 aio:Hierarchical_Classification Hierarchical Classification \n",
- "441 aio:Hierarchical_Classification Hierarchical Classification \n",
- "442 aio:Hierarchical_Classification Hierarchical Classification \n",
- "\n",
- " predicate predicate_label object \\\n",
- "438 rdfs:subClassOf None aio:Classification \n",
- "439 rdfs:subClassOf None aio:Hierarchical_Classification \n",
- "440 rdfs:subClassOf None aio:Machine_Learning \n",
- "441 rdfs:subClassOf None aio:Method \n",
- "442 rdfs:subClassOf None aio:Supervised_Learning \n",
- "\n",
- " object_label \n",
- "438 Classification \n",
- "439 Hierarchical Classification \n",
- "440 Machine Learning \n",
- "441 Method \n",
- "442 Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Classification\n",
- " subject subject_label predicate predicate_label \\\n",
- "140 aio:Classification Classification rdfs:subClassOf None \n",
- "141 aio:Classification Classification rdfs:subClassOf None \n",
- "142 aio:Classification Classification rdfs:subClassOf None \n",
- "143 aio:Classification Classification rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "140 aio:Classification Classification \n",
- "141 aio:Machine_Learning Machine Learning \n",
- "142 aio:Method Method \n",
- "143 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Hierarchical_Classification\n",
- " subject subject_label \\\n",
- "438 aio:Hierarchical_Classification Hierarchical Classification \n",
- "439 aio:Hierarchical_Classification Hierarchical Classification \n",
- "440 aio:Hierarchical_Classification Hierarchical Classification \n",
- "441 aio:Hierarchical_Classification Hierarchical Classification \n",
- "442 aio:Hierarchical_Classification Hierarchical Classification \n",
- "\n",
- " predicate predicate_label object \\\n",
- "438 rdfs:subClassOf None aio:Classification \n",
- "439 rdfs:subClassOf None aio:Hierarchical_Classification \n",
- "440 rdfs:subClassOf None aio:Machine_Learning \n",
- "441 rdfs:subClassOf None aio:Method \n",
- "442 rdfs:subClassOf None aio:Supervised_Learning \n",
- "\n",
- " object_label \n",
- "438 Classification \n",
- "439 Hierarchical Classification \n",
- "440 Machine Learning \n",
- "441 Method \n",
- "442 Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Classification\n",
- " subject subject_label predicate predicate_label \\\n",
- "140 aio:Classification Classification rdfs:subClassOf None \n",
- "141 aio:Classification Classification rdfs:subClassOf None \n",
- "142 aio:Classification Classification rdfs:subClassOf None \n",
- "143 aio:Classification Classification rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "140 aio:Classification Classification \n",
- "141 aio:Machine_Learning Machine Learning \n",
- "142 aio:Method Method \n",
- "143 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Hierarchical_Classification\n",
- " subject subject_label \\\n",
- "438 aio:Hierarchical_Classification Hierarchical Classification \n",
- "439 aio:Hierarchical_Classification Hierarchical Classification \n",
- "440 aio:Hierarchical_Classification Hierarchical Classification \n",
- "441 aio:Hierarchical_Classification Hierarchical Classification \n",
- "442 aio:Hierarchical_Classification Hierarchical Classification \n",
- "\n",
- " predicate predicate_label object \\\n",
- "438 rdfs:subClassOf None aio:Classification \n",
- "439 rdfs:subClassOf None aio:Hierarchical_Classification \n",
- "440 rdfs:subClassOf None aio:Machine_Learning \n",
- "441 rdfs:subClassOf None aio:Method \n",
- "442 rdfs:subClassOf None aio:Supervised_Learning \n",
- "\n",
- " object_label \n",
- "438 Classification \n",
- "439 Hierarchical Classification \n",
- "440 Machine Learning \n",
- "441 Method \n",
- "442 Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Classification\n",
- " subject subject_label predicate predicate_label \\\n",
- "140 aio:Classification Classification rdfs:subClassOf None \n",
- "141 aio:Classification Classification rdfs:subClassOf None \n",
- "142 aio:Classification Classification rdfs:subClassOf None \n",
- "143 aio:Classification Classification rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "140 aio:Classification Classification \n",
- "141 aio:Machine_Learning Machine Learning \n",
- "142 aio:Method Method \n",
- "143 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Hierarchical_Clustering\n",
- " subject subject_label predicate \\\n",
- "443 aio:Hierarchical_Clustering Hierarchical Clustering rdfs:subClassOf \n",
- "444 aio:Hierarchical_Clustering Hierarchical Clustering rdfs:subClassOf \n",
- "445 aio:Hierarchical_Clustering Hierarchical Clustering rdfs:subClassOf \n",
- "446 aio:Hierarchical_Clustering Hierarchical Clustering rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "443 None aio:Clustering Clustering \n",
- "444 None aio:Hierarchical_Clustering Hierarchical Clustering \n",
- "445 None aio:Machine_Learning Machine Learning \n",
- "446 None aio:Method Method \n",
- "parent aio:Clustering\n",
- "aio:Clustering\n",
- " subject subject_label predicate predicate_label \\\n",
- "144 aio:Clustering Clustering rdfs:subClassOf None \n",
- "145 aio:Clustering Clustering rdfs:subClassOf None \n",
- "146 aio:Clustering Clustering rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "144 aio:Clustering Clustering \n",
- "145 aio:Machine_Learning Machine Learning \n",
- "146 aio:Method Method \n",
- "parent aio:Clustering\n",
- "aio:Hierarchical_Clustering\n",
- " subject subject_label predicate \\\n",
- "443 aio:Hierarchical_Clustering Hierarchical Clustering rdfs:subClassOf \n",
- "444 aio:Hierarchical_Clustering Hierarchical Clustering rdfs:subClassOf \n",
- "445 aio:Hierarchical_Clustering Hierarchical Clustering rdfs:subClassOf \n",
- "446 aio:Hierarchical_Clustering Hierarchical Clustering rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "443 None aio:Clustering Clustering \n",
- "444 None aio:Hierarchical_Clustering Hierarchical Clustering \n",
- "445 None aio:Machine_Learning Machine Learning \n",
- "446 None aio:Method Method \n",
- "parent aio:Clustering\n",
- "aio:Clustering\n",
- " subject subject_label predicate predicate_label \\\n",
- "144 aio:Clustering Clustering rdfs:subClassOf None \n",
- "145 aio:Clustering Clustering rdfs:subClassOf None \n",
- "146 aio:Clustering Clustering rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "144 aio:Clustering Clustering \n",
- "145 aio:Machine_Learning Machine Learning \n",
- "146 aio:Method Method \n",
- "parent aio:Clustering\n",
- "aio:Hierarchical_Clustering\n",
- " subject subject_label predicate \\\n",
- "443 aio:Hierarchical_Clustering Hierarchical Clustering rdfs:subClassOf \n",
- "444 aio:Hierarchical_Clustering Hierarchical Clustering rdfs:subClassOf \n",
- "445 aio:Hierarchical_Clustering Hierarchical Clustering rdfs:subClassOf \n",
- "446 aio:Hierarchical_Clustering Hierarchical Clustering rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "443 None aio:Clustering Clustering \n",
- "444 None aio:Hierarchical_Clustering Hierarchical Clustering \n",
- "445 None aio:Machine_Learning Machine Learning \n",
- "446 None aio:Method Method \n",
- "parent aio:Clustering\n",
- "aio:Clustering\n",
- " subject subject_label predicate predicate_label \\\n",
- "144 aio:Clustering Clustering rdfs:subClassOf None \n",
- "145 aio:Clustering Clustering rdfs:subClassOf None \n",
- "146 aio:Clustering Clustering rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "144 aio:Clustering Clustering \n",
- "145 aio:Machine_Learning Machine Learning \n",
- "146 aio:Method Method \n",
- "parent aio:Clustering\n",
- "aio:Hierarchical_Clustering\n",
- " subject subject_label predicate \\\n",
- "443 aio:Hierarchical_Clustering Hierarchical Clustering rdfs:subClassOf \n",
- "444 aio:Hierarchical_Clustering Hierarchical Clustering rdfs:subClassOf \n",
- "445 aio:Hierarchical_Clustering Hierarchical Clustering rdfs:subClassOf \n",
- "446 aio:Hierarchical_Clustering Hierarchical Clustering rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "443 None aio:Clustering Clustering \n",
- "444 None aio:Hierarchical_Clustering Hierarchical Clustering \n",
- "445 None aio:Machine_Learning Machine Learning \n",
- "446 None aio:Method Method \n",
- "parent aio:Clustering\n",
- "aio:Clustering\n",
- " subject subject_label predicate predicate_label \\\n",
- "144 aio:Clustering Clustering rdfs:subClassOf None \n",
- "145 aio:Clustering Clustering rdfs:subClassOf None \n",
- "146 aio:Clustering Clustering rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "144 aio:Clustering Clustering \n",
- "145 aio:Machine_Learning Machine Learning \n",
- "146 aio:Method Method \n",
- "parent aio:Clustering\n",
- "aio:Historical_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "447 aio:Historical_Bias Historical Bias rdfs:subClassOf None \n",
- "448 aio:Historical_Bias Historical Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "447 aio:Bias Bias \n",
- "448 aio:Historical_Bias Historical Bias \n",
- "parent aio:Bias\n",
- "aio:Historical_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "447 aio:Historical_Bias Historical Bias rdfs:subClassOf None \n",
- "448 aio:Historical_Bias Historical Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "447 aio:Bias Bias \n",
- "448 aio:Historical_Bias Historical Bias \n",
- "parent aio:Bias\n",
- "aio:Hostile_Attribution_Bias\n",
- " subject subject_label predicate \\\n",
- "449 aio:Hostile_Attribution_Bias Hostile Attribution Bias rdfs:subClassOf \n",
- "450 aio:Hostile_Attribution_Bias Hostile Attribution Bias rdfs:subClassOf \n",
- "451 aio:Hostile_Attribution_Bias Hostile Attribution Bias rdfs:subClassOf \n",
- "452 aio:Hostile_Attribution_Bias Hostile Attribution Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "449 None aio:Bias \n",
- "450 None aio:Computational_Bias \n",
- "451 None aio:Hostile_Attribution_Bias \n",
- "452 None aio:Use_And_Interpretation_Bias \n",
- "\n",
- " object_label \n",
- "449 Bias \n",
- "450 Computational Bias \n",
- "451 Hostile Attribution Bias \n",
- "452 Use And Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:Hostile_Attribution_Bias\n",
- " subject subject_label predicate \\\n",
- "449 aio:Hostile_Attribution_Bias Hostile Attribution Bias rdfs:subClassOf \n",
- "450 aio:Hostile_Attribution_Bias Hostile Attribution Bias rdfs:subClassOf \n",
- "451 aio:Hostile_Attribution_Bias Hostile Attribution Bias rdfs:subClassOf \n",
- "452 aio:Hostile_Attribution_Bias Hostile Attribution Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "449 None aio:Bias \n",
- "450 None aio:Computational_Bias \n",
- "451 None aio:Hostile_Attribution_Bias \n",
- "452 None aio:Use_And_Interpretation_Bias \n",
- "\n",
- " object_label \n",
- "449 Bias \n",
- "450 Computational Bias \n",
- "451 Hostile Attribution Bias \n",
- "452 Use And Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:Hostile_Attribution_Bias\n",
- " subject subject_label predicate \\\n",
- "449 aio:Hostile_Attribution_Bias Hostile Attribution Bias rdfs:subClassOf \n",
- "450 aio:Hostile_Attribution_Bias Hostile Attribution Bias rdfs:subClassOf \n",
- "451 aio:Hostile_Attribution_Bias Hostile Attribution Bias rdfs:subClassOf \n",
- "452 aio:Hostile_Attribution_Bias Hostile Attribution Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "449 None aio:Bias \n",
- "450 None aio:Computational_Bias \n",
- "451 None aio:Hostile_Attribution_Bias \n",
- "452 None aio:Use_And_Interpretation_Bias \n",
- "\n",
- " object_label \n",
- "449 Bias \n",
- "450 Computational Bias \n",
- "451 Hostile Attribution Bias \n",
- "452 Use And Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:Hostile_Attribution_Bias\n",
- " subject subject_label predicate \\\n",
- "449 aio:Hostile_Attribution_Bias Hostile Attribution Bias rdfs:subClassOf \n",
- "450 aio:Hostile_Attribution_Bias Hostile Attribution Bias rdfs:subClassOf \n",
- "451 aio:Hostile_Attribution_Bias Hostile Attribution Bias rdfs:subClassOf \n",
- "452 aio:Hostile_Attribution_Bias Hostile Attribution Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "449 None aio:Bias \n",
- "450 None aio:Computational_Bias \n",
- "451 None aio:Hostile_Attribution_Bias \n",
- "452 None aio:Use_And_Interpretation_Bias \n",
- "\n",
- " object_label \n",
- "449 Bias \n",
- "450 Computational Bias \n",
- "451 Hostile Attribution Bias \n",
- "452 Use And Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:Human_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "453 aio:Human_Bias Human Bias rdfs:subClassOf None \n",
- "454 aio:Human_Bias Human Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "453 aio:Bias Bias \n",
- "454 aio:Human_Bias Human Bias \n",
- "parent aio:Bias\n",
- "aio:Human_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "453 aio:Human_Bias Human Bias rdfs:subClassOf None \n",
- "454 aio:Human_Bias Human Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "453 aio:Bias Bias \n",
- "454 aio:Human_Bias Human Bias \n",
- "parent aio:Bias\n",
- "aio:Human_Reporting_Bias\n",
- " subject subject_label predicate \\\n",
- "455 aio:Human_Reporting_Bias Human Reporting Bias rdfs:subClassOf \n",
- "456 aio:Human_Reporting_Bias Human Reporting Bias rdfs:subClassOf \n",
- "457 aio:Human_Reporting_Bias Human Reporting Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "455 None aio:Bias Bias \n",
- "456 None aio:Human_Reporting_Bias Human Reporting Bias \n",
- "457 None aio:Individual_Bias Individual Bias \n",
- "parent aio:Bias\n",
- "aio:Human_Reporting_Bias\n",
- " subject subject_label predicate \\\n",
- "455 aio:Human_Reporting_Bias Human Reporting Bias rdfs:subClassOf \n",
- "456 aio:Human_Reporting_Bias Human Reporting Bias rdfs:subClassOf \n",
- "457 aio:Human_Reporting_Bias Human Reporting Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "455 None aio:Bias Bias \n",
- "456 None aio:Human_Reporting_Bias Human Reporting Bias \n",
- "457 None aio:Individual_Bias Individual Bias \n",
- "parent aio:Bias\n",
- "aio:Human_Reporting_Bias\n",
- " subject subject_label predicate \\\n",
- "455 aio:Human_Reporting_Bias Human Reporting Bias rdfs:subClassOf \n",
- "456 aio:Human_Reporting_Bias Human Reporting Bias rdfs:subClassOf \n",
- "457 aio:Human_Reporting_Bias Human Reporting Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "455 None aio:Bias Bias \n",
- "456 None aio:Human_Reporting_Bias Human Reporting Bias \n",
- "457 None aio:Individual_Bias Individual Bias \n",
- "parent aio:Bias\n",
- "aio:Image_Augmentation_Layer\n",
- " subject subject_label predicate \\\n",
- "458 aio:Image_Augmentation_Layer Image Augmentation Layer rdfs:subClassOf \n",
- "459 aio:Image_Augmentation_Layer Image Augmentation Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "458 None aio:Image_Augmentation_Layer Image Augmentation Layer \n",
- "459 None aio:Layer Layer \n",
- "parent aio:Image_Augmentation_Layer\n",
- "aio:Image_Augmentation_Layer\n",
- " subject subject_label predicate \\\n",
- "458 aio:Image_Augmentation_Layer Image Augmentation Layer rdfs:subClassOf \n",
- "459 aio:Image_Augmentation_Layer Image Augmentation Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "458 None aio:Image_Augmentation_Layer Image Augmentation Layer \n",
- "459 None aio:Layer Layer \n",
- "parent aio:Image_Augmentation_Layer\n",
- "aio:Image_Preprocessing_Layer\n",
- " subject subject_label \\\n",
- "460 aio:Image_Preprocessing_Layer Image Preprocessing Layer \n",
- "461 aio:Image_Preprocessing_Layer Image Preprocessing Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "460 rdfs:subClassOf None aio:Image_Preprocessing_Layer \n",
- "461 rdfs:subClassOf None aio:Layer \n",
- "\n",
- " object_label \n",
- "460 Image Preprocessing Layer \n",
- "461 Layer \n",
- "parent aio:Image_Preprocessing_Layer\n",
- "aio:Image_Preprocessing_Layer\n",
- " subject subject_label \\\n",
- "460 aio:Image_Preprocessing_Layer Image Preprocessing Layer \n",
- "461 aio:Image_Preprocessing_Layer Image Preprocessing Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "460 rdfs:subClassOf None aio:Image_Preprocessing_Layer \n",
- "461 rdfs:subClassOf None aio:Layer \n",
- "\n",
- " object_label \n",
- "460 Image Preprocessing Layer \n",
- "461 Layer \n",
- "parent aio:Image_Preprocessing_Layer\n",
- "aio:Implicit_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "462 aio:Implicit_Bias Implicit Bias rdfs:subClassOf None \n",
- "463 aio:Implicit_Bias Implicit Bias rdfs:subClassOf None \n",
- "464 aio:Implicit_Bias Implicit Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "462 aio:Bias Bias \n",
- "463 aio:Implicit_Bias Implicit Bias \n",
- "464 aio:Individual_Bias Individual Bias \n",
- "parent aio:Bias\n",
- "aio:Implicit_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "462 aio:Implicit_Bias Implicit Bias rdfs:subClassOf None \n",
- "463 aio:Implicit_Bias Implicit Bias rdfs:subClassOf None \n",
- "464 aio:Implicit_Bias Implicit Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "462 aio:Bias Bias \n",
- "463 aio:Implicit_Bias Implicit Bias \n",
- "464 aio:Individual_Bias Individual Bias \n",
- "parent aio:Bias\n",
- "aio:Implicit_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "462 aio:Implicit_Bias Implicit Bias rdfs:subClassOf None \n",
- "463 aio:Implicit_Bias Implicit Bias rdfs:subClassOf None \n",
- "464 aio:Implicit_Bias Implicit Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "462 aio:Bias Bias \n",
- "463 aio:Implicit_Bias Implicit Bias \n",
- "464 aio:Individual_Bias Individual Bias \n",
- "parent aio:Bias\n",
- "aio:Incremenetal_Few-shot_Learning\n",
- " subject subject_label \\\n",
- "465 aio:Incremenetal_Few-shot_Learning Incremenetal Few-shot Learning \n",
- "466 aio:Incremenetal_Few-shot_Learning Incremenetal Few-shot Learning \n",
- "467 aio:Incremenetal_Few-shot_Learning Incremenetal Few-shot Learning \n",
- "468 aio:Incremenetal_Few-shot_Learning Incremenetal Few-shot Learning \n",
- "\n",
- " predicate predicate_label object \\\n",
- "465 rdfs:subClassOf None aio:ANN \n",
- "466 rdfs:subClassOf None aio:DNN \n",
- "467 rdfs:subClassOf None aio:Incremenetal_Few-shot_Learning \n",
- "468 rdfs:subClassOf None aio:Network \n",
- "\n",
- " object_label \n",
- "465 Artificial Neural Network \n",
- "466 Deep Neural Network \n",
- "467 Incremenetal Few-shot Learning \n",
- "468 Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Incremenetal_Few-shot_Learning\n",
- " subject subject_label \\\n",
- "465 aio:Incremenetal_Few-shot_Learning Incremenetal Few-shot Learning \n",
- "466 aio:Incremenetal_Few-shot_Learning Incremenetal Few-shot Learning \n",
- "467 aio:Incremenetal_Few-shot_Learning Incremenetal Few-shot Learning \n",
- "468 aio:Incremenetal_Few-shot_Learning Incremenetal Few-shot Learning \n",
- "\n",
- " predicate predicate_label object \\\n",
- "465 rdfs:subClassOf None aio:ANN \n",
- "466 rdfs:subClassOf None aio:DNN \n",
- "467 rdfs:subClassOf None aio:Incremenetal_Few-shot_Learning \n",
- "468 rdfs:subClassOf None aio:Network \n",
- "\n",
- " object_label \n",
- "465 Artificial Neural Network \n",
- "466 Deep Neural Network \n",
- "467 Incremenetal Few-shot Learning \n",
- "468 Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Incremenetal_Few-shot_Learning\n",
- " subject subject_label \\\n",
- "465 aio:Incremenetal_Few-shot_Learning Incremenetal Few-shot Learning \n",
- "466 aio:Incremenetal_Few-shot_Learning Incremenetal Few-shot Learning \n",
- "467 aio:Incremenetal_Few-shot_Learning Incremenetal Few-shot Learning \n",
- "468 aio:Incremenetal_Few-shot_Learning Incremenetal Few-shot Learning \n",
- "\n",
- " predicate predicate_label object \\\n",
- "465 rdfs:subClassOf None aio:ANN \n",
- "466 rdfs:subClassOf None aio:DNN \n",
- "467 rdfs:subClassOf None aio:Incremenetal_Few-shot_Learning \n",
- "468 rdfs:subClassOf None aio:Network \n",
- "\n",
- " object_label \n",
- "465 Artificial Neural Network \n",
- "466 Deep Neural Network \n",
- "467 Incremenetal Few-shot Learning \n",
- "468 Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Incremenetal_Few-shot_Learning\n",
- " subject subject_label \\\n",
- "465 aio:Incremenetal_Few-shot_Learning Incremenetal Few-shot Learning \n",
- "466 aio:Incremenetal_Few-shot_Learning Incremenetal Few-shot Learning \n",
- "467 aio:Incremenetal_Few-shot_Learning Incremenetal Few-shot Learning \n",
- "468 aio:Incremenetal_Few-shot_Learning Incremenetal Few-shot Learning \n",
- "\n",
- " predicate predicate_label object \\\n",
- "465 rdfs:subClassOf None aio:ANN \n",
- "466 rdfs:subClassOf None aio:DNN \n",
- "467 rdfs:subClassOf None aio:Incremenetal_Few-shot_Learning \n",
- "468 rdfs:subClassOf None aio:Network \n",
- "\n",
- " object_label \n",
- "465 Artificial Neural Network \n",
- "466 Deep Neural Network \n",
- "467 Incremenetal Few-shot Learning \n",
- "468 Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Individual_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "469 aio:Individual_Bias Individual Bias rdfs:subClassOf None \n",
- "470 aio:Individual_Bias Individual Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "469 aio:Bias Bias \n",
- "470 aio:Individual_Bias Individual Bias \n",
- "parent aio:Bias\n",
- "aio:Individual_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "469 aio:Individual_Bias Individual Bias rdfs:subClassOf None \n",
- "470 aio:Individual_Bias Individual Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "469 aio:Bias Bias \n",
- "470 aio:Individual_Bias Individual Bias \n",
- "parent aio:Bias\n",
- "aio:Inherited_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "471 aio:Inherited_Bias Inherited Bias rdfs:subClassOf None \n",
- "472 aio:Inherited_Bias Inherited Bias rdfs:subClassOf None \n",
- "473 aio:Inherited_Bias Inherited Bias rdfs:subClassOf None \n",
- "474 aio:Inherited_Bias Inherited Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "471 aio:Bias Bias \n",
- "472 aio:Computational_Bias Computational Bias \n",
- "473 aio:Inherited_Bias Inherited Bias \n",
- "474 aio:Processing_Bias Processing Bias \n",
- "parent aio:Bias\n",
- "aio:Inherited_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "471 aio:Inherited_Bias Inherited Bias rdfs:subClassOf None \n",
- "472 aio:Inherited_Bias Inherited Bias rdfs:subClassOf None \n",
- "473 aio:Inherited_Bias Inherited Bias rdfs:subClassOf None \n",
- "474 aio:Inherited_Bias Inherited Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "471 aio:Bias Bias \n",
- "472 aio:Computational_Bias Computational Bias \n",
- "473 aio:Inherited_Bias Inherited Bias \n",
- "474 aio:Processing_Bias Processing Bias \n",
- "parent aio:Bias\n",
- "aio:Inherited_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "471 aio:Inherited_Bias Inherited Bias rdfs:subClassOf None \n",
- "472 aio:Inherited_Bias Inherited Bias rdfs:subClassOf None \n",
- "473 aio:Inherited_Bias Inherited Bias rdfs:subClassOf None \n",
- "474 aio:Inherited_Bias Inherited Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "471 aio:Bias Bias \n",
- "472 aio:Computational_Bias Computational Bias \n",
- "473 aio:Inherited_Bias Inherited Bias \n",
- "474 aio:Processing_Bias Processing Bias \n",
- "parent aio:Bias\n",
- "aio:Inherited_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "471 aio:Inherited_Bias Inherited Bias rdfs:subClassOf None \n",
- "472 aio:Inherited_Bias Inherited Bias rdfs:subClassOf None \n",
- "473 aio:Inherited_Bias Inherited Bias rdfs:subClassOf None \n",
- "474 aio:Inherited_Bias Inherited Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "471 aio:Bias Bias \n",
- "472 aio:Computational_Bias Computational Bias \n",
- "473 aio:Inherited_Bias Inherited Bias \n",
- "474 aio:Processing_Bias Processing Bias \n",
- "parent aio:Bias\n",
- "aio:InputLayer_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "475 aio:InputLayer_Layer InputLayer Layer rdfs:subClassOf None \n",
- "476 aio:InputLayer_Layer InputLayer Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "475 aio:InputLayer_Layer InputLayer Layer \n",
- "476 aio:Layer Layer \n",
- "parent aio:InputLayer_Layer\n",
- "aio:InputLayer_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "475 aio:InputLayer_Layer InputLayer Layer rdfs:subClassOf None \n",
- "476 aio:InputLayer_Layer InputLayer Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "475 aio:InputLayer_Layer InputLayer Layer \n",
- "476 aio:Layer Layer \n",
- "parent aio:InputLayer_Layer\n",
- "aio:InputSpec_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "477 aio:InputSpec_Layer InputSpec Layer rdfs:subClassOf None \n",
- "478 aio:InputSpec_Layer InputSpec Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "477 aio:InputSpec_Layer InputSpec Layer \n",
- "478 aio:Layer Layer \n",
- "parent aio:InputSpec_Layer\n",
- "aio:InputSpec_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "477 aio:InputSpec_Layer InputSpec Layer rdfs:subClassOf None \n",
- "478 aio:InputSpec_Layer InputSpec Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "477 aio:InputSpec_Layer InputSpec Layer \n",
- "478 aio:Layer Layer \n",
- "parent aio:InputSpec_Layer\n",
- "aio:Input_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "479 aio:Input_Layer Input Layer rdfs:subClassOf None \n",
- "480 aio:Input_Layer Input Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "479 aio:Input_Layer Input Layer \n",
- "480 aio:Layer Layer \n",
- "parent aio:Input_Layer\n",
- "aio:Input_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "479 aio:Input_Layer Input Layer rdfs:subClassOf None \n",
- "480 aio:Input_Layer Input Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "479 aio:Input_Layer Input Layer \n",
- "480 aio:Layer Layer \n",
- "parent aio:Input_Layer\n",
- "aio:InstanceNorm1d_Layer\n",
- " subject subject_label predicate \\\n",
- "481 aio:InstanceNorm1d_Layer InstanceNorm1d Layer rdfs:subClassOf \n",
- "482 aio:InstanceNorm1d_Layer InstanceNorm1d Layer rdfs:subClassOf \n",
- "483 aio:InstanceNorm1d_Layer InstanceNorm1d Layer rdfs:subClassOf \n",
- "484 aio:InstanceNorm1d_Layer InstanceNorm1d Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "481 None aio:InstanceNorm1d_Layer \n",
- "482 None aio:Layer \n",
- "483 None aio:Normalization_Layer \n",
- "484 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "481 InstanceNorm1d Layer \n",
- "482 Layer \n",
- "483 Normalization Layer \n",
- "484 Numerical Features Preprocessing Layer \n",
- "parent aio:InstanceNorm1d_Layer\n",
- "aio:InstanceNorm1d_Layer\n",
- " subject subject_label predicate \\\n",
- "481 aio:InstanceNorm1d_Layer InstanceNorm1d Layer rdfs:subClassOf \n",
- "482 aio:InstanceNorm1d_Layer InstanceNorm1d Layer rdfs:subClassOf \n",
- "483 aio:InstanceNorm1d_Layer InstanceNorm1d Layer rdfs:subClassOf \n",
- "484 aio:InstanceNorm1d_Layer InstanceNorm1d Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "481 None aio:InstanceNorm1d_Layer \n",
- "482 None aio:Layer \n",
- "483 None aio:Normalization_Layer \n",
- "484 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "481 InstanceNorm1d Layer \n",
- "482 Layer \n",
- "483 Normalization Layer \n",
- "484 Numerical Features Preprocessing Layer \n",
- "parent aio:InstanceNorm1d_Layer\n",
- "aio:InstanceNorm1d_Layer\n",
- " subject subject_label predicate \\\n",
- "481 aio:InstanceNorm1d_Layer InstanceNorm1d Layer rdfs:subClassOf \n",
- "482 aio:InstanceNorm1d_Layer InstanceNorm1d Layer rdfs:subClassOf \n",
- "483 aio:InstanceNorm1d_Layer InstanceNorm1d Layer rdfs:subClassOf \n",
- "484 aio:InstanceNorm1d_Layer InstanceNorm1d Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "481 None aio:InstanceNorm1d_Layer \n",
- "482 None aio:Layer \n",
- "483 None aio:Normalization_Layer \n",
- "484 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "481 InstanceNorm1d Layer \n",
- "482 Layer \n",
- "483 Normalization Layer \n",
- "484 Numerical Features Preprocessing Layer \n",
- "parent aio:InstanceNorm1d_Layer\n",
- "aio:InstanceNorm1d_Layer\n",
- " subject subject_label predicate \\\n",
- "481 aio:InstanceNorm1d_Layer InstanceNorm1d Layer rdfs:subClassOf \n",
- "482 aio:InstanceNorm1d_Layer InstanceNorm1d Layer rdfs:subClassOf \n",
- "483 aio:InstanceNorm1d_Layer InstanceNorm1d Layer rdfs:subClassOf \n",
- "484 aio:InstanceNorm1d_Layer InstanceNorm1d Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "481 None aio:InstanceNorm1d_Layer \n",
- "482 None aio:Layer \n",
- "483 None aio:Normalization_Layer \n",
- "484 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "481 InstanceNorm1d Layer \n",
- "482 Layer \n",
- "483 Normalization Layer \n",
- "484 Numerical Features Preprocessing Layer \n",
- "parent aio:InstanceNorm1d_Layer\n",
- "aio:InstanceNorm2d\n",
- " subject subject_label predicate predicate_label \\\n",
- "485 aio:InstanceNorm2d InstanceNorm2d rdfs:subClassOf None \n",
- "486 aio:InstanceNorm2d InstanceNorm2d rdfs:subClassOf None \n",
- "487 aio:InstanceNorm2d InstanceNorm2d rdfs:subClassOf None \n",
- "488 aio:InstanceNorm2d InstanceNorm2d rdfs:subClassOf None \n",
- "\n",
- " object \\\n",
- "485 aio:InstanceNorm2d \n",
- "486 aio:Layer \n",
- "487 aio:Normalization_Layer \n",
- "488 aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "485 InstanceNorm2d \n",
- "486 Layer \n",
- "487 Normalization Layer \n",
- "488 Numerical Features Preprocessing Layer \n",
- "parent aio:InstanceNorm2d\n",
- "aio:InstanceNorm2d\n",
- " subject subject_label predicate predicate_label \\\n",
- "485 aio:InstanceNorm2d InstanceNorm2d rdfs:subClassOf None \n",
- "486 aio:InstanceNorm2d InstanceNorm2d rdfs:subClassOf None \n",
- "487 aio:InstanceNorm2d InstanceNorm2d rdfs:subClassOf None \n",
- "488 aio:InstanceNorm2d InstanceNorm2d rdfs:subClassOf None \n",
- "\n",
- " object \\\n",
- "485 aio:InstanceNorm2d \n",
- "486 aio:Layer \n",
- "487 aio:Normalization_Layer \n",
- "488 aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "485 InstanceNorm2d \n",
- "486 Layer \n",
- "487 Normalization Layer \n",
- "488 Numerical Features Preprocessing Layer \n",
- "parent aio:InstanceNorm2d\n",
- "aio:InstanceNorm2d\n",
- " subject subject_label predicate predicate_label \\\n",
- "485 aio:InstanceNorm2d InstanceNorm2d rdfs:subClassOf None \n",
- "486 aio:InstanceNorm2d InstanceNorm2d rdfs:subClassOf None \n",
- "487 aio:InstanceNorm2d InstanceNorm2d rdfs:subClassOf None \n",
- "488 aio:InstanceNorm2d InstanceNorm2d rdfs:subClassOf None \n",
- "\n",
- " object \\\n",
- "485 aio:InstanceNorm2d \n",
- "486 aio:Layer \n",
- "487 aio:Normalization_Layer \n",
- "488 aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "485 InstanceNorm2d \n",
- "486 Layer \n",
- "487 Normalization Layer \n",
- "488 Numerical Features Preprocessing Layer \n",
- "parent aio:InstanceNorm2d\n",
- "aio:InstanceNorm2d\n",
- " subject subject_label predicate predicate_label \\\n",
- "485 aio:InstanceNorm2d InstanceNorm2d rdfs:subClassOf None \n",
- "486 aio:InstanceNorm2d InstanceNorm2d rdfs:subClassOf None \n",
- "487 aio:InstanceNorm2d InstanceNorm2d rdfs:subClassOf None \n",
- "488 aio:InstanceNorm2d InstanceNorm2d rdfs:subClassOf None \n",
- "\n",
- " object \\\n",
- "485 aio:InstanceNorm2d \n",
- "486 aio:Layer \n",
- "487 aio:Normalization_Layer \n",
- "488 aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "485 InstanceNorm2d \n",
- "486 Layer \n",
- "487 Normalization Layer \n",
- "488 Numerical Features Preprocessing Layer \n",
- "parent aio:InstanceNorm2d\n",
- "aio:InstanceNorm3d_Layer\n",
- " subject subject_label predicate \\\n",
- "489 aio:InstanceNorm3d_Layer InstanceNorm3d Layer rdfs:subClassOf \n",
- "490 aio:InstanceNorm3d_Layer InstanceNorm3d Layer rdfs:subClassOf \n",
- "491 aio:InstanceNorm3d_Layer InstanceNorm3d Layer rdfs:subClassOf \n",
- "492 aio:InstanceNorm3d_Layer InstanceNorm3d Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "489 None aio:InstanceNorm3d_Layer \n",
- "490 None aio:Layer \n",
- "491 None aio:Normalization_Layer \n",
- "492 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "489 InstanceNorm3d Layer \n",
- "490 Layer \n",
- "491 Normalization Layer \n",
- "492 Numerical Features Preprocessing Layer \n",
- "parent aio:InstanceNorm3d_Layer\n",
- "aio:InstanceNorm3d_Layer\n",
- " subject subject_label predicate \\\n",
- "489 aio:InstanceNorm3d_Layer InstanceNorm3d Layer rdfs:subClassOf \n",
- "490 aio:InstanceNorm3d_Layer InstanceNorm3d Layer rdfs:subClassOf \n",
- "491 aio:InstanceNorm3d_Layer InstanceNorm3d Layer rdfs:subClassOf \n",
- "492 aio:InstanceNorm3d_Layer InstanceNorm3d Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "489 None aio:InstanceNorm3d_Layer \n",
- "490 None aio:Layer \n",
- "491 None aio:Normalization_Layer \n",
- "492 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "489 InstanceNorm3d Layer \n",
- "490 Layer \n",
- "491 Normalization Layer \n",
- "492 Numerical Features Preprocessing Layer \n",
- "parent aio:InstanceNorm3d_Layer\n",
- "aio:InstanceNorm3d_Layer\n",
- " subject subject_label predicate \\\n",
- "489 aio:InstanceNorm3d_Layer InstanceNorm3d Layer rdfs:subClassOf \n",
- "490 aio:InstanceNorm3d_Layer InstanceNorm3d Layer rdfs:subClassOf \n",
- "491 aio:InstanceNorm3d_Layer InstanceNorm3d Layer rdfs:subClassOf \n",
- "492 aio:InstanceNorm3d_Layer InstanceNorm3d Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "489 None aio:InstanceNorm3d_Layer \n",
- "490 None aio:Layer \n",
- "491 None aio:Normalization_Layer \n",
- "492 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "489 InstanceNorm3d Layer \n",
- "490 Layer \n",
- "491 Normalization Layer \n",
- "492 Numerical Features Preprocessing Layer \n",
- "parent aio:InstanceNorm3d_Layer\n",
- "aio:InstanceNorm3d_Layer\n",
- " subject subject_label predicate \\\n",
- "489 aio:InstanceNorm3d_Layer InstanceNorm3d Layer rdfs:subClassOf \n",
- "490 aio:InstanceNorm3d_Layer InstanceNorm3d Layer rdfs:subClassOf \n",
- "491 aio:InstanceNorm3d_Layer InstanceNorm3d Layer rdfs:subClassOf \n",
- "492 aio:InstanceNorm3d_Layer InstanceNorm3d Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "489 None aio:InstanceNorm3d_Layer \n",
- "490 None aio:Layer \n",
- "491 None aio:Normalization_Layer \n",
- "492 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "489 InstanceNorm3d Layer \n",
- "490 Layer \n",
- "491 Normalization Layer \n",
- "492 Numerical Features Preprocessing Layer \n",
- "parent aio:InstanceNorm3d_Layer\n",
- "aio:Institutional_Bias\n",
- " subject subject_label predicate \\\n",
- "493 aio:Institutional_Bias Institutional Bias rdfs:subClassOf \n",
- "494 aio:Institutional_Bias Institutional Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "493 None aio:Bias Bias \n",
- "494 None aio:Institutional_Bias Institutional Bias \n",
- "parent aio:Bias\n",
- "aio:Institutional_Bias\n",
- " subject subject_label predicate \\\n",
- "493 aio:Institutional_Bias Institutional Bias rdfs:subClassOf \n",
- "494 aio:Institutional_Bias Institutional Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "493 None aio:Bias Bias \n",
- "494 None aio:Institutional_Bias Institutional Bias \n",
- "parent aio:Bias\n",
- "aio:IntegerLookup_Layer\n",
- " subject subject_label predicate \\\n",
- "495 aio:IntegerLookup_Layer IntegerLookup Layer rdfs:subClassOf \n",
- "496 aio:IntegerLookup_Layer IntegerLookup Layer rdfs:subClassOf \n",
- "497 aio:IntegerLookup_Layer IntegerLookup Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "495 None aio:Categorical_Features_Preprocessing_Layer \n",
- "496 None aio:IntegerLookup_Layer \n",
- "497 None aio:Layer \n",
- "\n",
- " object_label \n",
- "495 Categorical Features Preprocessing Layer \n",
- "496 IntegerLookup Layer \n",
- "497 Layer \n",
- "parent aio:Categorical_Features_Preprocessing_Layer\n",
- "aio:Categorical_Features_Preprocessing_Layer\n",
- " subject \\\n",
- "128 aio:Categorical_Features_Preprocessing_Layer \n",
- "129 aio:Categorical_Features_Preprocessing_Layer \n",
- "\n",
- " subject_label predicate \\\n",
- "128 Categorical Features Preprocessing Layer rdfs:subClassOf \n",
- "129 Categorical Features Preprocessing Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "128 None aio:Categorical_Features_Preprocessing_Layer \n",
- "129 None aio:Layer \n",
- "\n",
- " object_label \n",
- "128 Categorical Features Preprocessing Layer \n",
- "129 Layer \n",
- "parent aio:Categorical_Features_Preprocessing_Layer\n",
- "aio:IntegerLookup_Layer\n",
- " subject subject_label predicate \\\n",
- "495 aio:IntegerLookup_Layer IntegerLookup Layer rdfs:subClassOf \n",
- "496 aio:IntegerLookup_Layer IntegerLookup Layer rdfs:subClassOf \n",
- "497 aio:IntegerLookup_Layer IntegerLookup Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "495 None aio:Categorical_Features_Preprocessing_Layer \n",
- "496 None aio:IntegerLookup_Layer \n",
- "497 None aio:Layer \n",
- "\n",
- " object_label \n",
- "495 Categorical Features Preprocessing Layer \n",
- "496 IntegerLookup Layer \n",
- "497 Layer \n",
- "parent aio:Categorical_Features_Preprocessing_Layer\n",
- "aio:Categorical_Features_Preprocessing_Layer\n",
- " subject \\\n",
- "128 aio:Categorical_Features_Preprocessing_Layer \n",
- "129 aio:Categorical_Features_Preprocessing_Layer \n",
- "\n",
- " subject_label predicate \\\n",
- "128 Categorical Features Preprocessing Layer rdfs:subClassOf \n",
- "129 Categorical Features Preprocessing Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "128 None aio:Categorical_Features_Preprocessing_Layer \n",
- "129 None aio:Layer \n",
- "\n",
- " object_label \n",
- "128 Categorical Features Preprocessing Layer \n",
- "129 Layer \n",
- "parent aio:Categorical_Features_Preprocessing_Layer\n",
- "aio:IntegerLookup_Layer\n",
- " subject subject_label predicate \\\n",
- "495 aio:IntegerLookup_Layer IntegerLookup Layer rdfs:subClassOf \n",
- "496 aio:IntegerLookup_Layer IntegerLookup Layer rdfs:subClassOf \n",
- "497 aio:IntegerLookup_Layer IntegerLookup Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "495 None aio:Categorical_Features_Preprocessing_Layer \n",
- "496 None aio:IntegerLookup_Layer \n",
- "497 None aio:Layer \n",
- "\n",
- " object_label \n",
- "495 Categorical Features Preprocessing Layer \n",
- "496 IntegerLookup Layer \n",
- "497 Layer \n",
- "parent aio:Categorical_Features_Preprocessing_Layer\n",
- "aio:Categorical_Features_Preprocessing_Layer\n",
- " subject \\\n",
- "128 aio:Categorical_Features_Preprocessing_Layer \n",
- "129 aio:Categorical_Features_Preprocessing_Layer \n",
- "\n",
- " subject_label predicate \\\n",
- "128 Categorical Features Preprocessing Layer rdfs:subClassOf \n",
- "129 Categorical Features Preprocessing Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "128 None aio:Categorical_Features_Preprocessing_Layer \n",
- "129 None aio:Layer \n",
- "\n",
- " object_label \n",
- "128 Categorical Features Preprocessing Layer \n",
- "129 Layer \n",
- "parent aio:Categorical_Features_Preprocessing_Layer\n",
- "aio:Interpretation_Bias\n",
- " subject subject_label predicate \\\n",
- "498 aio:Interpretation_Bias Interpretation Bias rdfs:subClassOf \n",
- "499 aio:Interpretation_Bias Interpretation Bias rdfs:subClassOf \n",
- "500 aio:Interpretation_Bias Interpretation Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "498 None aio:Bias Bias \n",
- "499 None aio:Individual_Bias Individual Bias \n",
- "500 None aio:Interpretation_Bias Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:Interpretation_Bias\n",
- " subject subject_label predicate \\\n",
- "498 aio:Interpretation_Bias Interpretation Bias rdfs:subClassOf \n",
- "499 aio:Interpretation_Bias Interpretation Bias rdfs:subClassOf \n",
- "500 aio:Interpretation_Bias Interpretation Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "498 None aio:Bias Bias \n",
- "499 None aio:Individual_Bias Individual Bias \n",
- "500 None aio:Interpretation_Bias Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:Interpretation_Bias\n",
- " subject subject_label predicate \\\n",
- "498 aio:Interpretation_Bias Interpretation Bias rdfs:subClassOf \n",
- "499 aio:Interpretation_Bias Interpretation Bias rdfs:subClassOf \n",
- "500 aio:Interpretation_Bias Interpretation Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "498 None aio:Bias Bias \n",
- "499 None aio:Individual_Bias Individual Bias \n",
- "500 None aio:Interpretation_Bias Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:K-nearest_Neighbor_Algorithm\n",
- " subject subject_label \\\n",
- "501 aio:K-nearest_Neighbor_Algorithm K-nearest Neighbor Algorithm \n",
- "502 aio:K-nearest_Neighbor_Algorithm K-nearest Neighbor Algorithm \n",
- "503 aio:K-nearest_Neighbor_Algorithm K-nearest Neighbor Algorithm \n",
- "\n",
- " predicate predicate_label object \\\n",
- "501 rdfs:subClassOf None aio:K-nearest_Neighbor_Algorithm \n",
- "502 rdfs:subClassOf None aio:Machine_Learning \n",
- "503 rdfs:subClassOf None aio:Method \n",
- "\n",
- " object_label \n",
- "501 K-nearest Neighbor Algorithm \n",
- "502 Machine Learning \n",
- "503 Method \n",
- "parent aio:K-nearest_Neighbor_Algorithm\n",
- "aio:K-nearest_Neighbor_Algorithm\n",
- " subject subject_label \\\n",
- "501 aio:K-nearest_Neighbor_Algorithm K-nearest Neighbor Algorithm \n",
- "502 aio:K-nearest_Neighbor_Algorithm K-nearest Neighbor Algorithm \n",
- "503 aio:K-nearest_Neighbor_Algorithm K-nearest Neighbor Algorithm \n",
- "\n",
- " predicate predicate_label object \\\n",
- "501 rdfs:subClassOf None aio:K-nearest_Neighbor_Algorithm \n",
- "502 rdfs:subClassOf None aio:Machine_Learning \n",
- "503 rdfs:subClassOf None aio:Method \n",
- "\n",
- " object_label \n",
- "501 K-nearest Neighbor Algorithm \n",
- "502 Machine Learning \n",
- "503 Method \n",
- "parent aio:K-nearest_Neighbor_Algorithm\n",
- "aio:K-nearest_Neighbor_Algorithm\n",
- " subject subject_label \\\n",
- "501 aio:K-nearest_Neighbor_Algorithm K-nearest Neighbor Algorithm \n",
- "502 aio:K-nearest_Neighbor_Algorithm K-nearest Neighbor Algorithm \n",
- "503 aio:K-nearest_Neighbor_Algorithm K-nearest Neighbor Algorithm \n",
- "\n",
- " predicate predicate_label object \\\n",
- "501 rdfs:subClassOf None aio:K-nearest_Neighbor_Algorithm \n",
- "502 rdfs:subClassOf None aio:Machine_Learning \n",
- "503 rdfs:subClassOf None aio:Method \n",
- "\n",
- " object_label \n",
- "501 K-nearest Neighbor Algorithm \n",
- "502 Machine Learning \n",
- "503 Method \n",
- "parent aio:K-nearest_Neighbor_Algorithm\n",
- "aio:K-nearest_Neighbor_Classification_Algorithm\n",
- " subject \\\n",
- "504 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "505 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "506 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "507 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "508 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "509 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "\n",
- " subject_label predicate \\\n",
- "504 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "505 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "506 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "507 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "508 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "509 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "504 None aio:Classification \n",
- "505 None aio:Clustering \n",
- "506 None aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "507 None aio:Machine_Learning \n",
- "508 None aio:Method \n",
- "509 None aio:Supervised_Learning \n",
- "\n",
- " object_label \n",
- "504 Classification \n",
- "505 Clustering \n",
- "506 K-nearest Neighbor Classification Algorithm \n",
- "507 Machine Learning \n",
- "508 Method \n",
- "509 Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Classification\n",
- " subject subject_label predicate predicate_label \\\n",
- "140 aio:Classification Classification rdfs:subClassOf None \n",
- "141 aio:Classification Classification rdfs:subClassOf None \n",
- "142 aio:Classification Classification rdfs:subClassOf None \n",
- "143 aio:Classification Classification rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "140 aio:Classification Classification \n",
- "141 aio:Machine_Learning Machine Learning \n",
- "142 aio:Method Method \n",
- "143 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:K-nearest_Neighbor_Classification_Algorithm\n",
- " subject \\\n",
- "504 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "505 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "506 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "507 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "508 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "509 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "\n",
- " subject_label predicate \\\n",
- "504 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "505 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "506 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "507 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "508 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "509 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "504 None aio:Classification \n",
- "505 None aio:Clustering \n",
- "506 None aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "507 None aio:Machine_Learning \n",
- "508 None aio:Method \n",
- "509 None aio:Supervised_Learning \n",
- "\n",
- " object_label \n",
- "504 Classification \n",
- "505 Clustering \n",
- "506 K-nearest Neighbor Classification Algorithm \n",
- "507 Machine Learning \n",
- "508 Method \n",
- "509 Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Classification\n",
- " subject subject_label predicate predicate_label \\\n",
- "140 aio:Classification Classification rdfs:subClassOf None \n",
- "141 aio:Classification Classification rdfs:subClassOf None \n",
- "142 aio:Classification Classification rdfs:subClassOf None \n",
- "143 aio:Classification Classification rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "140 aio:Classification Classification \n",
- "141 aio:Machine_Learning Machine Learning \n",
- "142 aio:Method Method \n",
- "143 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:K-nearest_Neighbor_Classification_Algorithm\n",
- " subject \\\n",
- "504 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "505 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "506 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "507 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "508 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "509 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "\n",
- " subject_label predicate \\\n",
- "504 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "505 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "506 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "507 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "508 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "509 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "504 None aio:Classification \n",
- "505 None aio:Clustering \n",
- "506 None aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "507 None aio:Machine_Learning \n",
- "508 None aio:Method \n",
- "509 None aio:Supervised_Learning \n",
- "\n",
- " object_label \n",
- "504 Classification \n",
- "505 Clustering \n",
- "506 K-nearest Neighbor Classification Algorithm \n",
- "507 Machine Learning \n",
- "508 Method \n",
- "509 Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Classification\n",
- " subject subject_label predicate predicate_label \\\n",
- "140 aio:Classification Classification rdfs:subClassOf None \n",
- "141 aio:Classification Classification rdfs:subClassOf None \n",
- "142 aio:Classification Classification rdfs:subClassOf None \n",
- "143 aio:Classification Classification rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "140 aio:Classification Classification \n",
- "141 aio:Machine_Learning Machine Learning \n",
- "142 aio:Method Method \n",
- "143 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:K-nearest_Neighbor_Classification_Algorithm\n",
- " subject \\\n",
- "504 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "505 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "506 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "507 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "508 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "509 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "\n",
- " subject_label predicate \\\n",
- "504 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "505 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "506 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "507 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "508 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "509 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "504 None aio:Classification \n",
- "505 None aio:Clustering \n",
- "506 None aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "507 None aio:Machine_Learning \n",
- "508 None aio:Method \n",
- "509 None aio:Supervised_Learning \n",
- "\n",
- " object_label \n",
- "504 Classification \n",
- "505 Clustering \n",
- "506 K-nearest Neighbor Classification Algorithm \n",
- "507 Machine Learning \n",
- "508 Method \n",
- "509 Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Classification\n",
- " subject subject_label predicate predicate_label \\\n",
- "140 aio:Classification Classification rdfs:subClassOf None \n",
- "141 aio:Classification Classification rdfs:subClassOf None \n",
- "142 aio:Classification Classification rdfs:subClassOf None \n",
- "143 aio:Classification Classification rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "140 aio:Classification Classification \n",
- "141 aio:Machine_Learning Machine Learning \n",
- "142 aio:Method Method \n",
- "143 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:K-nearest_Neighbor_Classification_Algorithm\n",
- " subject \\\n",
- "504 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "505 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "506 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "507 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "508 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "509 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "\n",
- " subject_label predicate \\\n",
- "504 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "505 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "506 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "507 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "508 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "509 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "504 None aio:Classification \n",
- "505 None aio:Clustering \n",
- "506 None aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "507 None aio:Machine_Learning \n",
- "508 None aio:Method \n",
- "509 None aio:Supervised_Learning \n",
- "\n",
- " object_label \n",
- "504 Classification \n",
- "505 Clustering \n",
- "506 K-nearest Neighbor Classification Algorithm \n",
- "507 Machine Learning \n",
- "508 Method \n",
- "509 Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Classification\n",
- " subject subject_label predicate predicate_label \\\n",
- "140 aio:Classification Classification rdfs:subClassOf None \n",
- "141 aio:Classification Classification rdfs:subClassOf None \n",
- "142 aio:Classification Classification rdfs:subClassOf None \n",
- "143 aio:Classification Classification rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "140 aio:Classification Classification \n",
- "141 aio:Machine_Learning Machine Learning \n",
- "142 aio:Method Method \n",
- "143 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:K-nearest_Neighbor_Classification_Algorithm\n",
- " subject \\\n",
- "504 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "505 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "506 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "507 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "508 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "509 aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "\n",
- " subject_label predicate \\\n",
- "504 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "505 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "506 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "507 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "508 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "509 K-nearest Neighbor Classification Algorithm rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "504 None aio:Classification \n",
- "505 None aio:Clustering \n",
- "506 None aio:K-nearest_Neighbor_Classification_Algorithm \n",
- "507 None aio:Machine_Learning \n",
- "508 None aio:Method \n",
- "509 None aio:Supervised_Learning \n",
- "\n",
- " object_label \n",
- "504 Classification \n",
- "505 Clustering \n",
- "506 K-nearest Neighbor Classification Algorithm \n",
- "507 Machine Learning \n",
- "508 Method \n",
- "509 Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Classification\n",
- " subject subject_label predicate predicate_label \\\n",
- "140 aio:Classification Classification rdfs:subClassOf None \n",
- "141 aio:Classification Classification rdfs:subClassOf None \n",
- "142 aio:Classification Classification rdfs:subClassOf None \n",
- "143 aio:Classification Classification rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "140 aio:Classification Classification \n",
- "141 aio:Machine_Learning Machine Learning \n",
- "142 aio:Method Method \n",
- "143 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:K-nearest_Neighbor_Regression_Algorithm\n",
- " subject \\\n",
- "510 aio:K-nearest_Neighbor_Regression_Algorithm \n",
- "511 aio:K-nearest_Neighbor_Regression_Algorithm \n",
- "512 aio:K-nearest_Neighbor_Regression_Algorithm \n",
- "513 aio:K-nearest_Neighbor_Regression_Algorithm \n",
- "514 aio:K-nearest_Neighbor_Regression_Algorithm \n",
- "\n",
- " subject_label predicate predicate_label \\\n",
- "510 K-nearest Neighbor Regression Algorithm rdfs:subClassOf None \n",
- "511 K-nearest Neighbor Regression Algorithm rdfs:subClassOf None \n",
- "512 K-nearest Neighbor Regression Algorithm rdfs:subClassOf None \n",
- "513 K-nearest Neighbor Regression Algorithm rdfs:subClassOf None \n",
- "514 K-nearest Neighbor Regression Algorithm rdfs:subClassOf None \n",
- "\n",
- " object \\\n",
- "510 aio:K-nearest_Neighbor_Regression_Algorithm \n",
- "511 aio:Machine_Learning \n",
- "512 aio:Method \n",
- "513 aio:Regression_Analysis \n",
- "514 aio:Supervised_Learning \n",
- "\n",
- " object_label \n",
- "510 K-nearest Neighbor Regression Algorithm \n",
- "511 Machine Learning \n",
- "512 Method \n",
- "513 Regression Analysis \n",
- "514 Supervised Learning \n",
- "parent aio:K-nearest_Neighbor_Regression_Algorithm\n",
- "aio:K-nearest_Neighbor_Regression_Algorithm\n",
- " subject \\\n",
- "510 aio:K-nearest_Neighbor_Regression_Algorithm \n",
- "511 aio:K-nearest_Neighbor_Regression_Algorithm \n",
- "512 aio:K-nearest_Neighbor_Regression_Algorithm \n",
- "513 aio:K-nearest_Neighbor_Regression_Algorithm \n",
- "514 aio:K-nearest_Neighbor_Regression_Algorithm \n",
- "\n",
- " subject_label predicate predicate_label \\\n",
- "510 K-nearest Neighbor Regression Algorithm rdfs:subClassOf None \n",
- "511 K-nearest Neighbor Regression Algorithm rdfs:subClassOf None \n",
- "512 K-nearest Neighbor Regression Algorithm rdfs:subClassOf None \n",
- "513 K-nearest Neighbor Regression Algorithm rdfs:subClassOf None \n",
- "514 K-nearest Neighbor Regression Algorithm rdfs:subClassOf None \n",
- "\n",
- " object \\\n",
- "510 aio:K-nearest_Neighbor_Regression_Algorithm \n",
- "511 aio:Machine_Learning \n",
- "512 aio:Method \n",
- "513 aio:Regression_Analysis \n",
- "514 aio:Supervised_Learning \n",
- "\n",
- " object_label \n",
- "510 K-nearest Neighbor Regression Algorithm \n",
- "511 Machine Learning \n",
- "512 Method \n",
- "513 Regression Analysis \n",
- "514 Supervised Learning \n",
- "parent aio:K-nearest_Neighbor_Regression_Algorithm\n",
- "aio:K-nearest_Neighbor_Regression_Algorithm\n",
- " subject \\\n",
- "510 aio:K-nearest_Neighbor_Regression_Algorithm \n",
- "511 aio:K-nearest_Neighbor_Regression_Algorithm \n",
- "512 aio:K-nearest_Neighbor_Regression_Algorithm \n",
- "513 aio:K-nearest_Neighbor_Regression_Algorithm \n",
- "514 aio:K-nearest_Neighbor_Regression_Algorithm \n",
- "\n",
- " subject_label predicate predicate_label \\\n",
- "510 K-nearest Neighbor Regression Algorithm rdfs:subClassOf None \n",
- "511 K-nearest Neighbor Regression Algorithm rdfs:subClassOf None \n",
- "512 K-nearest Neighbor Regression Algorithm rdfs:subClassOf None \n",
- "513 K-nearest Neighbor Regression Algorithm rdfs:subClassOf None \n",
- "514 K-nearest Neighbor Regression Algorithm rdfs:subClassOf None \n",
- "\n",
- " object \\\n",
- "510 aio:K-nearest_Neighbor_Regression_Algorithm \n",
- "511 aio:Machine_Learning \n",
- "512 aio:Method \n",
- "513 aio:Regression_Analysis \n",
- "514 aio:Supervised_Learning \n",
- "\n",
- " object_label \n",
- "510 K-nearest Neighbor Regression Algorithm \n",
- "511 Machine Learning \n",
- "512 Method \n",
- "513 Regression Analysis \n",
- "514 Supervised Learning \n",
- "parent aio:K-nearest_Neighbor_Regression_Algorithm\n",
- "aio:K-nearest_Neighbor_Regression_Algorithm\n",
- " subject \\\n",
- "510 aio:K-nearest_Neighbor_Regression_Algorithm \n",
- "511 aio:K-nearest_Neighbor_Regression_Algorithm \n",
- "512 aio:K-nearest_Neighbor_Regression_Algorithm \n",
- "513 aio:K-nearest_Neighbor_Regression_Algorithm \n",
- "514 aio:K-nearest_Neighbor_Regression_Algorithm \n",
- "\n",
- " subject_label predicate predicate_label \\\n",
- "510 K-nearest Neighbor Regression Algorithm rdfs:subClassOf None \n",
- "511 K-nearest Neighbor Regression Algorithm rdfs:subClassOf None \n",
- "512 K-nearest Neighbor Regression Algorithm rdfs:subClassOf None \n",
- "513 K-nearest Neighbor Regression Algorithm rdfs:subClassOf None \n",
- "514 K-nearest Neighbor Regression Algorithm rdfs:subClassOf None \n",
- "\n",
- " object \\\n",
- "510 aio:K-nearest_Neighbor_Regression_Algorithm \n",
- "511 aio:Machine_Learning \n",
- "512 aio:Method \n",
- "513 aio:Regression_Analysis \n",
- "514 aio:Supervised_Learning \n",
- "\n",
- " object_label \n",
- "510 K-nearest Neighbor Regression Algorithm \n",
- "511 Machine Learning \n",
- "512 Method \n",
- "513 Regression Analysis \n",
- "514 Supervised Learning \n",
- "parent aio:K-nearest_Neighbor_Regression_Algorithm\n",
- "aio:K-nearest_Neighbor_Regression_Algorithm\n",
- " subject \\\n",
- "510 aio:K-nearest_Neighbor_Regression_Algorithm \n",
- "511 aio:K-nearest_Neighbor_Regression_Algorithm \n",
- "512 aio:K-nearest_Neighbor_Regression_Algorithm \n",
- "513 aio:K-nearest_Neighbor_Regression_Algorithm \n",
- "514 aio:K-nearest_Neighbor_Regression_Algorithm \n",
- "\n",
- " subject_label predicate predicate_label \\\n",
- "510 K-nearest Neighbor Regression Algorithm rdfs:subClassOf None \n",
- "511 K-nearest Neighbor Regression Algorithm rdfs:subClassOf None \n",
- "512 K-nearest Neighbor Regression Algorithm rdfs:subClassOf None \n",
- "513 K-nearest Neighbor Regression Algorithm rdfs:subClassOf None \n",
- "514 K-nearest Neighbor Regression Algorithm rdfs:subClassOf None \n",
- "\n",
- " object \\\n",
- "510 aio:K-nearest_Neighbor_Regression_Algorithm \n",
- "511 aio:Machine_Learning \n",
- "512 aio:Method \n",
- "513 aio:Regression_Analysis \n",
- "514 aio:Supervised_Learning \n",
- "\n",
- " object_label \n",
- "510 K-nearest Neighbor Regression Algorithm \n",
- "511 Machine Learning \n",
- "512 Method \n",
- "513 Regression Analysis \n",
- "514 Supervised Learning \n",
- "parent aio:K-nearest_Neighbor_Regression_Algorithm\n",
- "aio:KN\n",
- " subject subject_label predicate predicate_label object \\\n",
- "515 aio:KN Kohonen Network rdfs:subClassOf None aio:KN \n",
- "516 aio:KN Kohonen Network rdfs:subClassOf None aio:Network \n",
- "\n",
- " object_label \n",
- "515 Kohonen Network \n",
- "516 Network \n",
- "parent aio:KN\n",
- "aio:KN\n",
- " subject subject_label predicate predicate_label object \\\n",
- "515 aio:KN Kohonen Network rdfs:subClassOf None aio:KN \n",
- "516 aio:KN Kohonen Network rdfs:subClassOf None aio:Network \n",
- "\n",
- " object_label \n",
- "515 Kohonen Network \n",
- "516 Network \n",
- "parent aio:KN\n",
- "aio:LPPool1D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "517 aio:LPPool1D_Layer LPPool1D Layer rdfs:subClassOf None \n",
- "518 aio:LPPool1D_Layer LPPool1D Layer rdfs:subClassOf None \n",
- "519 aio:LPPool1D_Layer LPPool1D Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "517 aio:LPPool1D_Layer LPPool1D Layer \n",
- "518 aio:Layer Layer \n",
- "519 aio:Pooling_Layer Pooling Layer \n",
- "parent aio:LPPool1D_Layer\n",
- "aio:LPPool1D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "517 aio:LPPool1D_Layer LPPool1D Layer rdfs:subClassOf None \n",
- "518 aio:LPPool1D_Layer LPPool1D Layer rdfs:subClassOf None \n",
- "519 aio:LPPool1D_Layer LPPool1D Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "517 aio:LPPool1D_Layer LPPool1D Layer \n",
- "518 aio:Layer Layer \n",
- "519 aio:Pooling_Layer Pooling Layer \n",
- "parent aio:LPPool1D_Layer\n",
- "aio:LPPool1D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "517 aio:LPPool1D_Layer LPPool1D Layer rdfs:subClassOf None \n",
- "518 aio:LPPool1D_Layer LPPool1D Layer rdfs:subClassOf None \n",
- "519 aio:LPPool1D_Layer LPPool1D Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "517 aio:LPPool1D_Layer LPPool1D Layer \n",
- "518 aio:Layer Layer \n",
- "519 aio:Pooling_Layer Pooling Layer \n",
- "parent aio:LPPool1D_Layer\n",
- "aio:LPPool2D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "520 aio:LPPool2D_Layer LPPool2D Layer rdfs:subClassOf None \n",
- "521 aio:LPPool2D_Layer LPPool2D Layer rdfs:subClassOf None \n",
- "522 aio:LPPool2D_Layer LPPool2D Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "520 aio:LPPool2D_Layer LPPool2D Layer \n",
- "521 aio:Layer Layer \n",
- "522 aio:Pooling_Layer Pooling Layer \n",
- "parent aio:LPPool2D_Layer\n",
- "aio:LPPool2D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "520 aio:LPPool2D_Layer LPPool2D Layer rdfs:subClassOf None \n",
- "521 aio:LPPool2D_Layer LPPool2D Layer rdfs:subClassOf None \n",
- "522 aio:LPPool2D_Layer LPPool2D Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "520 aio:LPPool2D_Layer LPPool2D Layer \n",
- "521 aio:Layer Layer \n",
- "522 aio:Pooling_Layer Pooling Layer \n",
- "parent aio:LPPool2D_Layer\n",
- "aio:LPPool2D_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "520 aio:LPPool2D_Layer LPPool2D Layer rdfs:subClassOf None \n",
- "521 aio:LPPool2D_Layer LPPool2D Layer rdfs:subClassOf None \n",
- "522 aio:LPPool2D_Layer LPPool2D Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "520 aio:LPPool2D_Layer LPPool2D Layer \n",
- "521 aio:Layer Layer \n",
- "522 aio:Pooling_Layer Pooling Layer \n",
- "parent aio:LPPool2D_Layer\n",
- "aio:LSM\n",
- " subject subject_label predicate predicate_label \\\n",
- "523 aio:LSM Liquid State Machine Network rdfs:subClassOf None \n",
- "524 aio:LSM Liquid State Machine Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "523 aio:LSM Liquid State Machine Network \n",
- "524 aio:Network Network \n",
- "parent aio:LSM\n",
- "aio:LSM\n",
- " subject subject_label predicate predicate_label \\\n",
- "523 aio:LSM Liquid State Machine Network rdfs:subClassOf None \n",
- "524 aio:LSM Liquid State Machine Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "523 aio:LSM Liquid State Machine Network \n",
- "524 aio:Network Network \n",
- "parent aio:LSM\n",
- "aio:LSTM\n",
- " subject subject_label predicate predicate_label \\\n",
- "525 aio:LSTM Long Short Term Memory rdfs:subClassOf None \n",
- "526 aio:LSTM Long Short Term Memory rdfs:subClassOf None \n",
- "527 aio:LSTM Long Short Term Memory rdfs:subClassOf None \n",
- "528 aio:LSTM Long Short Term Memory rdfs:subClassOf None \n",
- "529 aio:LSTM Long Short Term Memory rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "525 aio:ANN Artificial Neural Network \n",
- "526 aio:DNN Deep Neural Network \n",
- "527 aio:LSTM Long Short Term Memory \n",
- "528 aio:Network Network \n",
- "529 aio:RecNN Recurrent Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:LSTM\n",
- " subject subject_label predicate predicate_label \\\n",
- "525 aio:LSTM Long Short Term Memory rdfs:subClassOf None \n",
- "526 aio:LSTM Long Short Term Memory rdfs:subClassOf None \n",
- "527 aio:LSTM Long Short Term Memory rdfs:subClassOf None \n",
- "528 aio:LSTM Long Short Term Memory rdfs:subClassOf None \n",
- "529 aio:LSTM Long Short Term Memory rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "525 aio:ANN Artificial Neural Network \n",
- "526 aio:DNN Deep Neural Network \n",
- "527 aio:LSTM Long Short Term Memory \n",
- "528 aio:Network Network \n",
- "529 aio:RecNN Recurrent Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:LSTM\n",
- " subject subject_label predicate predicate_label \\\n",
- "525 aio:LSTM Long Short Term Memory rdfs:subClassOf None \n",
- "526 aio:LSTM Long Short Term Memory rdfs:subClassOf None \n",
- "527 aio:LSTM Long Short Term Memory rdfs:subClassOf None \n",
- "528 aio:LSTM Long Short Term Memory rdfs:subClassOf None \n",
- "529 aio:LSTM Long Short Term Memory rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "525 aio:ANN Artificial Neural Network \n",
- "526 aio:DNN Deep Neural Network \n",
- "527 aio:LSTM Long Short Term Memory \n",
- "528 aio:Network Network \n",
- "529 aio:RecNN Recurrent Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:LSTM\n",
- " subject subject_label predicate predicate_label \\\n",
- "525 aio:LSTM Long Short Term Memory rdfs:subClassOf None \n",
- "526 aio:LSTM Long Short Term Memory rdfs:subClassOf None \n",
- "527 aio:LSTM Long Short Term Memory rdfs:subClassOf None \n",
- "528 aio:LSTM Long Short Term Memory rdfs:subClassOf None \n",
- "529 aio:LSTM Long Short Term Memory rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "525 aio:ANN Artificial Neural Network \n",
- "526 aio:DNN Deep Neural Network \n",
- "527 aio:LSTM Long Short Term Memory \n",
- "528 aio:Network Network \n",
- "529 aio:RecNN Recurrent Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:LSTM\n",
- " subject subject_label predicate predicate_label \\\n",
- "525 aio:LSTM Long Short Term Memory rdfs:subClassOf None \n",
- "526 aio:LSTM Long Short Term Memory rdfs:subClassOf None \n",
- "527 aio:LSTM Long Short Term Memory rdfs:subClassOf None \n",
- "528 aio:LSTM Long Short Term Memory rdfs:subClassOf None \n",
- "529 aio:LSTM Long Short Term Memory rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "525 aio:ANN Artificial Neural Network \n",
- "526 aio:DNN Deep Neural Network \n",
- "527 aio:LSTM Long Short Term Memory \n",
- "528 aio:Network Network \n",
- "529 aio:RecNN Recurrent Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:LSTMCell_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "530 aio:LSTMCell_Layer LSTMCell Layer rdfs:subClassOf None \n",
- "531 aio:LSTMCell_Layer LSTMCell Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "530 aio:LSTMCell_Layer LSTMCell Layer \n",
- "531 aio:Layer Layer \n",
- "parent aio:LSTMCell_Layer\n",
- "aio:LSTMCell_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "530 aio:LSTMCell_Layer LSTMCell Layer rdfs:subClassOf None \n",
- "531 aio:LSTMCell_Layer LSTMCell Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "530 aio:LSTMCell_Layer LSTMCell Layer \n",
- "531 aio:Layer Layer \n",
- "parent aio:LSTMCell_Layer\n",
- "aio:LSTM_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "532 aio:LSTM_Layer LSTM Layer rdfs:subClassOf None \n",
- "533 aio:LSTM_Layer LSTM Layer rdfs:subClassOf None \n",
- "534 aio:LSTM_Layer LSTM Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "532 aio:LSTM_Layer LSTM Layer \n",
- "533 aio:Layer Layer \n",
- "534 aio:Recurrent_Layer Recurrent Layer \n",
- "parent aio:LSTM_Layer\n",
- "aio:LSTM_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "532 aio:LSTM_Layer LSTM Layer rdfs:subClassOf None \n",
- "533 aio:LSTM_Layer LSTM Layer rdfs:subClassOf None \n",
- "534 aio:LSTM_Layer LSTM Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "532 aio:LSTM_Layer LSTM Layer \n",
- "533 aio:Layer Layer \n",
- "534 aio:Recurrent_Layer Recurrent Layer \n",
- "parent aio:LSTM_Layer\n",
- "aio:LSTM_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "532 aio:LSTM_Layer LSTM Layer rdfs:subClassOf None \n",
- "533 aio:LSTM_Layer LSTM Layer rdfs:subClassOf None \n",
- "534 aio:LSTM_Layer LSTM Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "532 aio:LSTM_Layer LSTM Layer \n",
- "533 aio:Layer Layer \n",
- "534 aio:Recurrent_Layer Recurrent Layer \n",
- "parent aio:LSTM_Layer\n",
- "aio:Lambda_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "535 aio:Lambda_Layer Lambda Layer rdfs:subClassOf None \n",
- "536 aio:Lambda_Layer Lambda Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "535 aio:Lambda_Layer Lambda Layer \n",
- "536 aio:Layer Layer \n",
- "parent aio:Lambda_Layer\n",
- "aio:Lambda_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "535 aio:Lambda_Layer Lambda Layer rdfs:subClassOf None \n",
- "536 aio:Lambda_Layer Lambda Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "535 aio:Lambda_Layer Lambda Layer \n",
- "536 aio:Layer Layer \n",
- "parent aio:Lambda_Layer\n",
- "aio:Lasso_Regression\n",
- " subject subject_label predicate predicate_label \\\n",
- "537 aio:Lasso_Regression Lasso Regression rdfs:subClassOf None \n",
- "538 aio:Lasso_Regression Lasso Regression rdfs:subClassOf None \n",
- "539 aio:Lasso_Regression Lasso Regression rdfs:subClassOf None \n",
- "540 aio:Lasso_Regression Lasso Regression rdfs:subClassOf None \n",
- "541 aio:Lasso_Regression Lasso Regression rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "537 aio:Lasso_Regression Lasso Regression \n",
- "538 aio:Machine_Learning Machine Learning \n",
- "539 aio:Method Method \n",
- "540 aio:Regression_Analysis Regression Analysis \n",
- "541 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Lasso_Regression\n",
- "aio:Lasso_Regression\n",
- " subject subject_label predicate predicate_label \\\n",
- "537 aio:Lasso_Regression Lasso Regression rdfs:subClassOf None \n",
- "538 aio:Lasso_Regression Lasso Regression rdfs:subClassOf None \n",
- "539 aio:Lasso_Regression Lasso Regression rdfs:subClassOf None \n",
- "540 aio:Lasso_Regression Lasso Regression rdfs:subClassOf None \n",
- "541 aio:Lasso_Regression Lasso Regression rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "537 aio:Lasso_Regression Lasso Regression \n",
- "538 aio:Machine_Learning Machine Learning \n",
- "539 aio:Method Method \n",
- "540 aio:Regression_Analysis Regression Analysis \n",
- "541 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Lasso_Regression\n",
- "aio:Lasso_Regression\n",
- " subject subject_label predicate predicate_label \\\n",
- "537 aio:Lasso_Regression Lasso Regression rdfs:subClassOf None \n",
- "538 aio:Lasso_Regression Lasso Regression rdfs:subClassOf None \n",
- "539 aio:Lasso_Regression Lasso Regression rdfs:subClassOf None \n",
- "540 aio:Lasso_Regression Lasso Regression rdfs:subClassOf None \n",
- "541 aio:Lasso_Regression Lasso Regression rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "537 aio:Lasso_Regression Lasso Regression \n",
- "538 aio:Machine_Learning Machine Learning \n",
- "539 aio:Method Method \n",
- "540 aio:Regression_Analysis Regression Analysis \n",
- "541 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Lasso_Regression\n",
- "aio:Lasso_Regression\n",
- " subject subject_label predicate predicate_label \\\n",
- "537 aio:Lasso_Regression Lasso Regression rdfs:subClassOf None \n",
- "538 aio:Lasso_Regression Lasso Regression rdfs:subClassOf None \n",
- "539 aio:Lasso_Regression Lasso Regression rdfs:subClassOf None \n",
- "540 aio:Lasso_Regression Lasso Regression rdfs:subClassOf None \n",
- "541 aio:Lasso_Regression Lasso Regression rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "537 aio:Lasso_Regression Lasso Regression \n",
- "538 aio:Machine_Learning Machine Learning \n",
- "539 aio:Method Method \n",
- "540 aio:Regression_Analysis Regression Analysis \n",
- "541 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Lasso_Regression\n",
- "aio:Lasso_Regression\n",
- " subject subject_label predicate predicate_label \\\n",
- "537 aio:Lasso_Regression Lasso Regression rdfs:subClassOf None \n",
- "538 aio:Lasso_Regression Lasso Regression rdfs:subClassOf None \n",
- "539 aio:Lasso_Regression Lasso Regression rdfs:subClassOf None \n",
- "540 aio:Lasso_Regression Lasso Regression rdfs:subClassOf None \n",
- "541 aio:Lasso_Regression Lasso Regression rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "537 aio:Lasso_Regression Lasso Regression \n",
- "538 aio:Machine_Learning Machine Learning \n",
- "539 aio:Method Method \n",
- "540 aio:Regression_Analysis Regression Analysis \n",
- "541 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Lasso_Regression\n",
- "aio:Layer\n",
- " subject subject_label predicate predicate_label object \\\n",
- "542 aio:Layer Layer rdfs:subClassOf None aio:Layer \n",
- "\n",
- " object_label \n",
- "542 Layer \n",
- "parent aio:Layer\n",
- "aio:LayerNorm_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "543 aio:LayerNorm_Layer LayerNorm Layer rdfs:subClassOf None \n",
- "544 aio:LayerNorm_Layer LayerNorm Layer rdfs:subClassOf None \n",
- "545 aio:LayerNorm_Layer LayerNorm Layer rdfs:subClassOf None \n",
- "546 aio:LayerNorm_Layer LayerNorm Layer rdfs:subClassOf None \n",
- "\n",
- " object \\\n",
- "543 aio:Layer \n",
- "544 aio:LayerNorm_Layer \n",
- "545 aio:Normalization_Layer \n",
- "546 aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "543 Layer \n",
- "544 LayerNorm Layer \n",
- "545 Normalization Layer \n",
- "546 Numerical Features Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:LayerNorm_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "543 aio:LayerNorm_Layer LayerNorm Layer rdfs:subClassOf None \n",
- "544 aio:LayerNorm_Layer LayerNorm Layer rdfs:subClassOf None \n",
- "545 aio:LayerNorm_Layer LayerNorm Layer rdfs:subClassOf None \n",
- "546 aio:LayerNorm_Layer LayerNorm Layer rdfs:subClassOf None \n",
- "\n",
- " object \\\n",
- "543 aio:Layer \n",
- "544 aio:LayerNorm_Layer \n",
- "545 aio:Normalization_Layer \n",
- "546 aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "543 Layer \n",
- "544 LayerNorm Layer \n",
- "545 Normalization Layer \n",
- "546 Numerical Features Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:LayerNorm_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "543 aio:LayerNorm_Layer LayerNorm Layer rdfs:subClassOf None \n",
- "544 aio:LayerNorm_Layer LayerNorm Layer rdfs:subClassOf None \n",
- "545 aio:LayerNorm_Layer LayerNorm Layer rdfs:subClassOf None \n",
- "546 aio:LayerNorm_Layer LayerNorm Layer rdfs:subClassOf None \n",
- "\n",
- " object \\\n",
- "543 aio:Layer \n",
- "544 aio:LayerNorm_Layer \n",
- "545 aio:Normalization_Layer \n",
- "546 aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "543 Layer \n",
- "544 LayerNorm Layer \n",
- "545 Normalization Layer \n",
- "546 Numerical Features Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:LayerNorm_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "543 aio:LayerNorm_Layer LayerNorm Layer rdfs:subClassOf None \n",
- "544 aio:LayerNorm_Layer LayerNorm Layer rdfs:subClassOf None \n",
- "545 aio:LayerNorm_Layer LayerNorm Layer rdfs:subClassOf None \n",
- "546 aio:LayerNorm_Layer LayerNorm Layer rdfs:subClassOf None \n",
- "\n",
- " object \\\n",
- "543 aio:Layer \n",
- "544 aio:LayerNorm_Layer \n",
- "545 aio:Normalization_Layer \n",
- "546 aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "543 Layer \n",
- "544 LayerNorm Layer \n",
- "545 Normalization Layer \n",
- "546 Numerical Features Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:LayerNormalization_Layer\n",
- " subject subject_label predicate \\\n",
- "547 aio:LayerNormalization_Layer LayerNormalization Layer rdfs:subClassOf \n",
- "548 aio:LayerNormalization_Layer LayerNormalization Layer rdfs:subClassOf \n",
- "549 aio:LayerNormalization_Layer LayerNormalization Layer rdfs:subClassOf \n",
- "550 aio:LayerNormalization_Layer LayerNormalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "547 None aio:Layer \n",
- "548 None aio:LayerNormalization_Layer \n",
- "549 None aio:Normalization_Layer \n",
- "550 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "547 Layer \n",
- "548 LayerNormalization Layer \n",
- "549 Normalization Layer \n",
- "550 Numerical Features Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:LayerNormalization_Layer\n",
- " subject subject_label predicate \\\n",
- "547 aio:LayerNormalization_Layer LayerNormalization Layer rdfs:subClassOf \n",
- "548 aio:LayerNormalization_Layer LayerNormalization Layer rdfs:subClassOf \n",
- "549 aio:LayerNormalization_Layer LayerNormalization Layer rdfs:subClassOf \n",
- "550 aio:LayerNormalization_Layer LayerNormalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "547 None aio:Layer \n",
- "548 None aio:LayerNormalization_Layer \n",
- "549 None aio:Normalization_Layer \n",
- "550 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "547 Layer \n",
- "548 LayerNormalization Layer \n",
- "549 Normalization Layer \n",
- "550 Numerical Features Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:LayerNormalization_Layer\n",
- " subject subject_label predicate \\\n",
- "547 aio:LayerNormalization_Layer LayerNormalization Layer rdfs:subClassOf \n",
- "548 aio:LayerNormalization_Layer LayerNormalization Layer rdfs:subClassOf \n",
- "549 aio:LayerNormalization_Layer LayerNormalization Layer rdfs:subClassOf \n",
- "550 aio:LayerNormalization_Layer LayerNormalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "547 None aio:Layer \n",
- "548 None aio:LayerNormalization_Layer \n",
- "549 None aio:Normalization_Layer \n",
- "550 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "547 Layer \n",
- "548 LayerNormalization Layer \n",
- "549 Normalization Layer \n",
- "550 Numerical Features Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:LayerNormalization_Layer\n",
- " subject subject_label predicate \\\n",
- "547 aio:LayerNormalization_Layer LayerNormalization Layer rdfs:subClassOf \n",
- "548 aio:LayerNormalization_Layer LayerNormalization Layer rdfs:subClassOf \n",
- "549 aio:LayerNormalization_Layer LayerNormalization Layer rdfs:subClassOf \n",
- "550 aio:LayerNormalization_Layer LayerNormalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "547 None aio:Layer \n",
- "548 None aio:LayerNormalization_Layer \n",
- "549 None aio:Normalization_Layer \n",
- "550 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "547 Layer \n",
- "548 LayerNormalization Layer \n",
- "549 Normalization Layer \n",
- "550 Numerical Features Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:Layer_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "551 aio:Layer_Layer Layer Layer rdfs:subClassOf None \n",
- "552 aio:Layer_Layer Layer Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "551 aio:Layer Layer \n",
- "552 aio:Layer_Layer Layer Layer \n",
- "parent aio:Layer\n",
- "aio:Layer_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "551 aio:Layer_Layer Layer Layer rdfs:subClassOf None \n",
- "552 aio:Layer_Layer Layer Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "551 aio:Layer Layer \n",
- "552 aio:Layer_Layer Layer Layer \n",
- "parent aio:Layer\n",
- "aio:LazyBatchNorm1D_Layer\n",
- " subject subject_label predicate \\\n",
- "553 aio:LazyBatchNorm1D_Layer LazyBatchNorm1D Layer rdfs:subClassOf \n",
- "554 aio:LazyBatchNorm1D_Layer LazyBatchNorm1D Layer rdfs:subClassOf \n",
- "555 aio:LazyBatchNorm1D_Layer LazyBatchNorm1D Layer rdfs:subClassOf \n",
- "556 aio:LazyBatchNorm1D_Layer LazyBatchNorm1D Layer rdfs:subClassOf \n",
- "557 aio:LazyBatchNorm1D_Layer LazyBatchNorm1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "553 None aio:BatchNormalization_Layer \n",
- "554 None aio:Layer \n",
- "555 None aio:LazyBatchNorm1D_Layer \n",
- "556 None aio:Normalization_Layer \n",
- "557 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "553 BatchNormalization Layer \n",
- "554 Layer \n",
- "555 LazyBatchNorm1D Layer \n",
- "556 Normalization Layer \n",
- "557 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:BatchNormalization_Layer\n",
- " subject subject_label predicate \\\n",
- "107 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "108 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "109 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "110 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "107 None aio:BatchNormalization_Layer \n",
- "108 None aio:Layer \n",
- "109 None aio:Normalization_Layer \n",
- "110 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "107 BatchNormalization Layer \n",
- "108 Layer \n",
- "109 Normalization Layer \n",
- "110 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:LazyBatchNorm1D_Layer\n",
- " subject subject_label predicate \\\n",
- "553 aio:LazyBatchNorm1D_Layer LazyBatchNorm1D Layer rdfs:subClassOf \n",
- "554 aio:LazyBatchNorm1D_Layer LazyBatchNorm1D Layer rdfs:subClassOf \n",
- "555 aio:LazyBatchNorm1D_Layer LazyBatchNorm1D Layer rdfs:subClassOf \n",
- "556 aio:LazyBatchNorm1D_Layer LazyBatchNorm1D Layer rdfs:subClassOf \n",
- "557 aio:LazyBatchNorm1D_Layer LazyBatchNorm1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "553 None aio:BatchNormalization_Layer \n",
- "554 None aio:Layer \n",
- "555 None aio:LazyBatchNorm1D_Layer \n",
- "556 None aio:Normalization_Layer \n",
- "557 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "553 BatchNormalization Layer \n",
- "554 Layer \n",
- "555 LazyBatchNorm1D Layer \n",
- "556 Normalization Layer \n",
- "557 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:BatchNormalization_Layer\n",
- " subject subject_label predicate \\\n",
- "107 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "108 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "109 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "110 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "107 None aio:BatchNormalization_Layer \n",
- "108 None aio:Layer \n",
- "109 None aio:Normalization_Layer \n",
- "110 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "107 BatchNormalization Layer \n",
- "108 Layer \n",
- "109 Normalization Layer \n",
- "110 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:LazyBatchNorm1D_Layer\n",
- " subject subject_label predicate \\\n",
- "553 aio:LazyBatchNorm1D_Layer LazyBatchNorm1D Layer rdfs:subClassOf \n",
- "554 aio:LazyBatchNorm1D_Layer LazyBatchNorm1D Layer rdfs:subClassOf \n",
- "555 aio:LazyBatchNorm1D_Layer LazyBatchNorm1D Layer rdfs:subClassOf \n",
- "556 aio:LazyBatchNorm1D_Layer LazyBatchNorm1D Layer rdfs:subClassOf \n",
- "557 aio:LazyBatchNorm1D_Layer LazyBatchNorm1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "553 None aio:BatchNormalization_Layer \n",
- "554 None aio:Layer \n",
- "555 None aio:LazyBatchNorm1D_Layer \n",
- "556 None aio:Normalization_Layer \n",
- "557 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "553 BatchNormalization Layer \n",
- "554 Layer \n",
- "555 LazyBatchNorm1D Layer \n",
- "556 Normalization Layer \n",
- "557 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:BatchNormalization_Layer\n",
- " subject subject_label predicate \\\n",
- "107 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "108 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "109 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "110 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "107 None aio:BatchNormalization_Layer \n",
- "108 None aio:Layer \n",
- "109 None aio:Normalization_Layer \n",
- "110 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "107 BatchNormalization Layer \n",
- "108 Layer \n",
- "109 Normalization Layer \n",
- "110 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:LazyBatchNorm1D_Layer\n",
- " subject subject_label predicate \\\n",
- "553 aio:LazyBatchNorm1D_Layer LazyBatchNorm1D Layer rdfs:subClassOf \n",
- "554 aio:LazyBatchNorm1D_Layer LazyBatchNorm1D Layer rdfs:subClassOf \n",
- "555 aio:LazyBatchNorm1D_Layer LazyBatchNorm1D Layer rdfs:subClassOf \n",
- "556 aio:LazyBatchNorm1D_Layer LazyBatchNorm1D Layer rdfs:subClassOf \n",
- "557 aio:LazyBatchNorm1D_Layer LazyBatchNorm1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "553 None aio:BatchNormalization_Layer \n",
- "554 None aio:Layer \n",
- "555 None aio:LazyBatchNorm1D_Layer \n",
- "556 None aio:Normalization_Layer \n",
- "557 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "553 BatchNormalization Layer \n",
- "554 Layer \n",
- "555 LazyBatchNorm1D Layer \n",
- "556 Normalization Layer \n",
- "557 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:BatchNormalization_Layer\n",
- " subject subject_label predicate \\\n",
- "107 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "108 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "109 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "110 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "107 None aio:BatchNormalization_Layer \n",
- "108 None aio:Layer \n",
- "109 None aio:Normalization_Layer \n",
- "110 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "107 BatchNormalization Layer \n",
- "108 Layer \n",
- "109 Normalization Layer \n",
- "110 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:LazyBatchNorm1D_Layer\n",
- " subject subject_label predicate \\\n",
- "553 aio:LazyBatchNorm1D_Layer LazyBatchNorm1D Layer rdfs:subClassOf \n",
- "554 aio:LazyBatchNorm1D_Layer LazyBatchNorm1D Layer rdfs:subClassOf \n",
- "555 aio:LazyBatchNorm1D_Layer LazyBatchNorm1D Layer rdfs:subClassOf \n",
- "556 aio:LazyBatchNorm1D_Layer LazyBatchNorm1D Layer rdfs:subClassOf \n",
- "557 aio:LazyBatchNorm1D_Layer LazyBatchNorm1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "553 None aio:BatchNormalization_Layer \n",
- "554 None aio:Layer \n",
- "555 None aio:LazyBatchNorm1D_Layer \n",
- "556 None aio:Normalization_Layer \n",
- "557 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "553 BatchNormalization Layer \n",
- "554 Layer \n",
- "555 LazyBatchNorm1D Layer \n",
- "556 Normalization Layer \n",
- "557 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:BatchNormalization_Layer\n",
- " subject subject_label predicate \\\n",
- "107 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "108 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "109 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "110 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "107 None aio:BatchNormalization_Layer \n",
- "108 None aio:Layer \n",
- "109 None aio:Normalization_Layer \n",
- "110 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "107 BatchNormalization Layer \n",
- "108 Layer \n",
- "109 Normalization Layer \n",
- "110 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:LazyBatchNorm2D_Layer\n",
- " subject subject_label predicate \\\n",
- "558 aio:LazyBatchNorm2D_Layer LazyBatchNorm2D Layer rdfs:subClassOf \n",
- "559 aio:LazyBatchNorm2D_Layer LazyBatchNorm2D Layer rdfs:subClassOf \n",
- "560 aio:LazyBatchNorm2D_Layer LazyBatchNorm2D Layer rdfs:subClassOf \n",
- "561 aio:LazyBatchNorm2D_Layer LazyBatchNorm2D Layer rdfs:subClassOf \n",
- "562 aio:LazyBatchNorm2D_Layer LazyBatchNorm2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "558 None aio:BatchNormalization_Layer \n",
- "559 None aio:Layer \n",
- "560 None aio:LazyBatchNorm2D_Layer \n",
- "561 None aio:Normalization_Layer \n",
- "562 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "558 BatchNormalization Layer \n",
- "559 Layer \n",
- "560 LazyBatchNorm2D Layer \n",
- "561 Normalization Layer \n",
- "562 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:BatchNormalization_Layer\n",
- " subject subject_label predicate \\\n",
- "107 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "108 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "109 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "110 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "107 None aio:BatchNormalization_Layer \n",
- "108 None aio:Layer \n",
- "109 None aio:Normalization_Layer \n",
- "110 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "107 BatchNormalization Layer \n",
- "108 Layer \n",
- "109 Normalization Layer \n",
- "110 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:LazyBatchNorm2D_Layer\n",
- " subject subject_label predicate \\\n",
- "558 aio:LazyBatchNorm2D_Layer LazyBatchNorm2D Layer rdfs:subClassOf \n",
- "559 aio:LazyBatchNorm2D_Layer LazyBatchNorm2D Layer rdfs:subClassOf \n",
- "560 aio:LazyBatchNorm2D_Layer LazyBatchNorm2D Layer rdfs:subClassOf \n",
- "561 aio:LazyBatchNorm2D_Layer LazyBatchNorm2D Layer rdfs:subClassOf \n",
- "562 aio:LazyBatchNorm2D_Layer LazyBatchNorm2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "558 None aio:BatchNormalization_Layer \n",
- "559 None aio:Layer \n",
- "560 None aio:LazyBatchNorm2D_Layer \n",
- "561 None aio:Normalization_Layer \n",
- "562 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "558 BatchNormalization Layer \n",
- "559 Layer \n",
- "560 LazyBatchNorm2D Layer \n",
- "561 Normalization Layer \n",
- "562 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:BatchNormalization_Layer\n",
- " subject subject_label predicate \\\n",
- "107 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "108 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "109 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "110 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "107 None aio:BatchNormalization_Layer \n",
- "108 None aio:Layer \n",
- "109 None aio:Normalization_Layer \n",
- "110 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "107 BatchNormalization Layer \n",
- "108 Layer \n",
- "109 Normalization Layer \n",
- "110 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:LazyBatchNorm2D_Layer\n",
- " subject subject_label predicate \\\n",
- "558 aio:LazyBatchNorm2D_Layer LazyBatchNorm2D Layer rdfs:subClassOf \n",
- "559 aio:LazyBatchNorm2D_Layer LazyBatchNorm2D Layer rdfs:subClassOf \n",
- "560 aio:LazyBatchNorm2D_Layer LazyBatchNorm2D Layer rdfs:subClassOf \n",
- "561 aio:LazyBatchNorm2D_Layer LazyBatchNorm2D Layer rdfs:subClassOf \n",
- "562 aio:LazyBatchNorm2D_Layer LazyBatchNorm2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "558 None aio:BatchNormalization_Layer \n",
- "559 None aio:Layer \n",
- "560 None aio:LazyBatchNorm2D_Layer \n",
- "561 None aio:Normalization_Layer \n",
- "562 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "558 BatchNormalization Layer \n",
- "559 Layer \n",
- "560 LazyBatchNorm2D Layer \n",
- "561 Normalization Layer \n",
- "562 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:BatchNormalization_Layer\n",
- " subject subject_label predicate \\\n",
- "107 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "108 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "109 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "110 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "107 None aio:BatchNormalization_Layer \n",
- "108 None aio:Layer \n",
- "109 None aio:Normalization_Layer \n",
- "110 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "107 BatchNormalization Layer \n",
- "108 Layer \n",
- "109 Normalization Layer \n",
- "110 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:LazyBatchNorm2D_Layer\n",
- " subject subject_label predicate \\\n",
- "558 aio:LazyBatchNorm2D_Layer LazyBatchNorm2D Layer rdfs:subClassOf \n",
- "559 aio:LazyBatchNorm2D_Layer LazyBatchNorm2D Layer rdfs:subClassOf \n",
- "560 aio:LazyBatchNorm2D_Layer LazyBatchNorm2D Layer rdfs:subClassOf \n",
- "561 aio:LazyBatchNorm2D_Layer LazyBatchNorm2D Layer rdfs:subClassOf \n",
- "562 aio:LazyBatchNorm2D_Layer LazyBatchNorm2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "558 None aio:BatchNormalization_Layer \n",
- "559 None aio:Layer \n",
- "560 None aio:LazyBatchNorm2D_Layer \n",
- "561 None aio:Normalization_Layer \n",
- "562 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "558 BatchNormalization Layer \n",
- "559 Layer \n",
- "560 LazyBatchNorm2D Layer \n",
- "561 Normalization Layer \n",
- "562 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:BatchNormalization_Layer\n",
- " subject subject_label predicate \\\n",
- "107 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "108 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "109 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "110 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "107 None aio:BatchNormalization_Layer \n",
- "108 None aio:Layer \n",
- "109 None aio:Normalization_Layer \n",
- "110 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "107 BatchNormalization Layer \n",
- "108 Layer \n",
- "109 Normalization Layer \n",
- "110 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:LazyBatchNorm2D_Layer\n",
- " subject subject_label predicate \\\n",
- "558 aio:LazyBatchNorm2D_Layer LazyBatchNorm2D Layer rdfs:subClassOf \n",
- "559 aio:LazyBatchNorm2D_Layer LazyBatchNorm2D Layer rdfs:subClassOf \n",
- "560 aio:LazyBatchNorm2D_Layer LazyBatchNorm2D Layer rdfs:subClassOf \n",
- "561 aio:LazyBatchNorm2D_Layer LazyBatchNorm2D Layer rdfs:subClassOf \n",
- "562 aio:LazyBatchNorm2D_Layer LazyBatchNorm2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "558 None aio:BatchNormalization_Layer \n",
- "559 None aio:Layer \n",
- "560 None aio:LazyBatchNorm2D_Layer \n",
- "561 None aio:Normalization_Layer \n",
- "562 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "558 BatchNormalization Layer \n",
- "559 Layer \n",
- "560 LazyBatchNorm2D Layer \n",
- "561 Normalization Layer \n",
- "562 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:BatchNormalization_Layer\n",
- " subject subject_label predicate \\\n",
- "107 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "108 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "109 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "110 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "107 None aio:BatchNormalization_Layer \n",
- "108 None aio:Layer \n",
- "109 None aio:Normalization_Layer \n",
- "110 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "107 BatchNormalization Layer \n",
- "108 Layer \n",
- "109 Normalization Layer \n",
- "110 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:LazyBatchNorm3D_Layer\n",
- " subject subject_label predicate \\\n",
- "563 aio:LazyBatchNorm3D_Layer LazyBatchNorm3D Layer rdfs:subClassOf \n",
- "564 aio:LazyBatchNorm3D_Layer LazyBatchNorm3D Layer rdfs:subClassOf \n",
- "565 aio:LazyBatchNorm3D_Layer LazyBatchNorm3D Layer rdfs:subClassOf \n",
- "566 aio:LazyBatchNorm3D_Layer LazyBatchNorm3D Layer rdfs:subClassOf \n",
- "567 aio:LazyBatchNorm3D_Layer LazyBatchNorm3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "563 None aio:BatchNormalization_Layer \n",
- "564 None aio:Layer \n",
- "565 None aio:LazyBatchNorm3D_Layer \n",
- "566 None aio:Normalization_Layer \n",
- "567 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "563 BatchNormalization Layer \n",
- "564 Layer \n",
- "565 LazyBatchNorm3D Layer \n",
- "566 Normalization Layer \n",
- "567 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:BatchNormalization_Layer\n",
- " subject subject_label predicate \\\n",
- "107 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "108 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "109 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "110 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "107 None aio:BatchNormalization_Layer \n",
- "108 None aio:Layer \n",
- "109 None aio:Normalization_Layer \n",
- "110 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "107 BatchNormalization Layer \n",
- "108 Layer \n",
- "109 Normalization Layer \n",
- "110 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:LazyBatchNorm3D_Layer\n",
- " subject subject_label predicate \\\n",
- "563 aio:LazyBatchNorm3D_Layer LazyBatchNorm3D Layer rdfs:subClassOf \n",
- "564 aio:LazyBatchNorm3D_Layer LazyBatchNorm3D Layer rdfs:subClassOf \n",
- "565 aio:LazyBatchNorm3D_Layer LazyBatchNorm3D Layer rdfs:subClassOf \n",
- "566 aio:LazyBatchNorm3D_Layer LazyBatchNorm3D Layer rdfs:subClassOf \n",
- "567 aio:LazyBatchNorm3D_Layer LazyBatchNorm3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "563 None aio:BatchNormalization_Layer \n",
- "564 None aio:Layer \n",
- "565 None aio:LazyBatchNorm3D_Layer \n",
- "566 None aio:Normalization_Layer \n",
- "567 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "563 BatchNormalization Layer \n",
- "564 Layer \n",
- "565 LazyBatchNorm3D Layer \n",
- "566 Normalization Layer \n",
- "567 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:BatchNormalization_Layer\n",
- " subject subject_label predicate \\\n",
- "107 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "108 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "109 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "110 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "107 None aio:BatchNormalization_Layer \n",
- "108 None aio:Layer \n",
- "109 None aio:Normalization_Layer \n",
- "110 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "107 BatchNormalization Layer \n",
- "108 Layer \n",
- "109 Normalization Layer \n",
- "110 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:LazyBatchNorm3D_Layer\n",
- " subject subject_label predicate \\\n",
- "563 aio:LazyBatchNorm3D_Layer LazyBatchNorm3D Layer rdfs:subClassOf \n",
- "564 aio:LazyBatchNorm3D_Layer LazyBatchNorm3D Layer rdfs:subClassOf \n",
- "565 aio:LazyBatchNorm3D_Layer LazyBatchNorm3D Layer rdfs:subClassOf \n",
- "566 aio:LazyBatchNorm3D_Layer LazyBatchNorm3D Layer rdfs:subClassOf \n",
- "567 aio:LazyBatchNorm3D_Layer LazyBatchNorm3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "563 None aio:BatchNormalization_Layer \n",
- "564 None aio:Layer \n",
- "565 None aio:LazyBatchNorm3D_Layer \n",
- "566 None aio:Normalization_Layer \n",
- "567 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "563 BatchNormalization Layer \n",
- "564 Layer \n",
- "565 LazyBatchNorm3D Layer \n",
- "566 Normalization Layer \n",
- "567 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:BatchNormalization_Layer\n",
- " subject subject_label predicate \\\n",
- "107 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "108 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "109 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "110 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "107 None aio:BatchNormalization_Layer \n",
- "108 None aio:Layer \n",
- "109 None aio:Normalization_Layer \n",
- "110 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "107 BatchNormalization Layer \n",
- "108 Layer \n",
- "109 Normalization Layer \n",
- "110 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:LazyBatchNorm3D_Layer\n",
- " subject subject_label predicate \\\n",
- "563 aio:LazyBatchNorm3D_Layer LazyBatchNorm3D Layer rdfs:subClassOf \n",
- "564 aio:LazyBatchNorm3D_Layer LazyBatchNorm3D Layer rdfs:subClassOf \n",
- "565 aio:LazyBatchNorm3D_Layer LazyBatchNorm3D Layer rdfs:subClassOf \n",
- "566 aio:LazyBatchNorm3D_Layer LazyBatchNorm3D Layer rdfs:subClassOf \n",
- "567 aio:LazyBatchNorm3D_Layer LazyBatchNorm3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "563 None aio:BatchNormalization_Layer \n",
- "564 None aio:Layer \n",
- "565 None aio:LazyBatchNorm3D_Layer \n",
- "566 None aio:Normalization_Layer \n",
- "567 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "563 BatchNormalization Layer \n",
- "564 Layer \n",
- "565 LazyBatchNorm3D Layer \n",
- "566 Normalization Layer \n",
- "567 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:BatchNormalization_Layer\n",
- " subject subject_label predicate \\\n",
- "107 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "108 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "109 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "110 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "107 None aio:BatchNormalization_Layer \n",
- "108 None aio:Layer \n",
- "109 None aio:Normalization_Layer \n",
- "110 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "107 BatchNormalization Layer \n",
- "108 Layer \n",
- "109 Normalization Layer \n",
- "110 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:LazyBatchNorm3D_Layer\n",
- " subject subject_label predicate \\\n",
- "563 aio:LazyBatchNorm3D_Layer LazyBatchNorm3D Layer rdfs:subClassOf \n",
- "564 aio:LazyBatchNorm3D_Layer LazyBatchNorm3D Layer rdfs:subClassOf \n",
- "565 aio:LazyBatchNorm3D_Layer LazyBatchNorm3D Layer rdfs:subClassOf \n",
- "566 aio:LazyBatchNorm3D_Layer LazyBatchNorm3D Layer rdfs:subClassOf \n",
- "567 aio:LazyBatchNorm3D_Layer LazyBatchNorm3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "563 None aio:BatchNormalization_Layer \n",
- "564 None aio:Layer \n",
- "565 None aio:LazyBatchNorm3D_Layer \n",
- "566 None aio:Normalization_Layer \n",
- "567 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "563 BatchNormalization Layer \n",
- "564 Layer \n",
- "565 LazyBatchNorm3D Layer \n",
- "566 Normalization Layer \n",
- "567 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:BatchNormalization_Layer\n",
- " subject subject_label predicate \\\n",
- "107 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "108 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "109 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "110 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "107 None aio:BatchNormalization_Layer \n",
- "108 None aio:Layer \n",
- "109 None aio:Normalization_Layer \n",
- "110 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "107 BatchNormalization Layer \n",
- "108 Layer \n",
- "109 Normalization Layer \n",
- "110 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:LazyInstanceNorm1d_Layer\n",
- " subject subject_label predicate \\\n",
- "568 aio:LazyInstanceNorm1d_Layer LazyInstanceNorm1d Layer rdfs:subClassOf \n",
- "569 aio:LazyInstanceNorm1d_Layer LazyInstanceNorm1d Layer rdfs:subClassOf \n",
- "570 aio:LazyInstanceNorm1d_Layer LazyInstanceNorm1d Layer rdfs:subClassOf \n",
- "571 aio:LazyInstanceNorm1d_Layer LazyInstanceNorm1d Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "568 None aio:Layer \n",
- "569 None aio:LazyInstanceNorm1d_Layer \n",
- "570 None aio:Normalization_Layer \n",
- "571 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "568 Layer \n",
- "569 LazyInstanceNorm1d Layer \n",
- "570 Normalization Layer \n",
- "571 Numerical Features Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:LazyInstanceNorm1d_Layer\n",
- " subject subject_label predicate \\\n",
- "568 aio:LazyInstanceNorm1d_Layer LazyInstanceNorm1d Layer rdfs:subClassOf \n",
- "569 aio:LazyInstanceNorm1d_Layer LazyInstanceNorm1d Layer rdfs:subClassOf \n",
- "570 aio:LazyInstanceNorm1d_Layer LazyInstanceNorm1d Layer rdfs:subClassOf \n",
- "571 aio:LazyInstanceNorm1d_Layer LazyInstanceNorm1d Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "568 None aio:Layer \n",
- "569 None aio:LazyInstanceNorm1d_Layer \n",
- "570 None aio:Normalization_Layer \n",
- "571 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "568 Layer \n",
- "569 LazyInstanceNorm1d Layer \n",
- "570 Normalization Layer \n",
- "571 Numerical Features Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:LazyInstanceNorm1d_Layer\n",
- " subject subject_label predicate \\\n",
- "568 aio:LazyInstanceNorm1d_Layer LazyInstanceNorm1d Layer rdfs:subClassOf \n",
- "569 aio:LazyInstanceNorm1d_Layer LazyInstanceNorm1d Layer rdfs:subClassOf \n",
- "570 aio:LazyInstanceNorm1d_Layer LazyInstanceNorm1d Layer rdfs:subClassOf \n",
- "571 aio:LazyInstanceNorm1d_Layer LazyInstanceNorm1d Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "568 None aio:Layer \n",
- "569 None aio:LazyInstanceNorm1d_Layer \n",
- "570 None aio:Normalization_Layer \n",
- "571 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "568 Layer \n",
- "569 LazyInstanceNorm1d Layer \n",
- "570 Normalization Layer \n",
- "571 Numerical Features Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:LazyInstanceNorm1d_Layer\n",
- " subject subject_label predicate \\\n",
- "568 aio:LazyInstanceNorm1d_Layer LazyInstanceNorm1d Layer rdfs:subClassOf \n",
- "569 aio:LazyInstanceNorm1d_Layer LazyInstanceNorm1d Layer rdfs:subClassOf \n",
- "570 aio:LazyInstanceNorm1d_Layer LazyInstanceNorm1d Layer rdfs:subClassOf \n",
- "571 aio:LazyInstanceNorm1d_Layer LazyInstanceNorm1d Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "568 None aio:Layer \n",
- "569 None aio:LazyInstanceNorm1d_Layer \n",
- "570 None aio:Normalization_Layer \n",
- "571 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "568 Layer \n",
- "569 LazyInstanceNorm1d Layer \n",
- "570 Normalization Layer \n",
- "571 Numerical Features Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:LazyInstanceNorm2d_Layer\n",
- " subject subject_label predicate \\\n",
- "572 aio:LazyInstanceNorm2d_Layer LazyInstanceNorm2d Layer rdfs:subClassOf \n",
- "573 aio:LazyInstanceNorm2d_Layer LazyInstanceNorm2d Layer rdfs:subClassOf \n",
- "574 aio:LazyInstanceNorm2d_Layer LazyInstanceNorm2d Layer rdfs:subClassOf \n",
- "575 aio:LazyInstanceNorm2d_Layer LazyInstanceNorm2d Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "572 None aio:Layer \n",
- "573 None aio:LazyInstanceNorm2d_Layer \n",
- "574 None aio:Normalization_Layer \n",
- "575 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "572 Layer \n",
- "573 LazyInstanceNorm2d Layer \n",
- "574 Normalization Layer \n",
- "575 Numerical Features Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:LazyInstanceNorm2d_Layer\n",
- " subject subject_label predicate \\\n",
- "572 aio:LazyInstanceNorm2d_Layer LazyInstanceNorm2d Layer rdfs:subClassOf \n",
- "573 aio:LazyInstanceNorm2d_Layer LazyInstanceNorm2d Layer rdfs:subClassOf \n",
- "574 aio:LazyInstanceNorm2d_Layer LazyInstanceNorm2d Layer rdfs:subClassOf \n",
- "575 aio:LazyInstanceNorm2d_Layer LazyInstanceNorm2d Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "572 None aio:Layer \n",
- "573 None aio:LazyInstanceNorm2d_Layer \n",
- "574 None aio:Normalization_Layer \n",
- "575 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "572 Layer \n",
- "573 LazyInstanceNorm2d Layer \n",
- "574 Normalization Layer \n",
- "575 Numerical Features Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:LazyInstanceNorm2d_Layer\n",
- " subject subject_label predicate \\\n",
- "572 aio:LazyInstanceNorm2d_Layer LazyInstanceNorm2d Layer rdfs:subClassOf \n",
- "573 aio:LazyInstanceNorm2d_Layer LazyInstanceNorm2d Layer rdfs:subClassOf \n",
- "574 aio:LazyInstanceNorm2d_Layer LazyInstanceNorm2d Layer rdfs:subClassOf \n",
- "575 aio:LazyInstanceNorm2d_Layer LazyInstanceNorm2d Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "572 None aio:Layer \n",
- "573 None aio:LazyInstanceNorm2d_Layer \n",
- "574 None aio:Normalization_Layer \n",
- "575 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "572 Layer \n",
- "573 LazyInstanceNorm2d Layer \n",
- "574 Normalization Layer \n",
- "575 Numerical Features Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:LazyInstanceNorm2d_Layer\n",
- " subject subject_label predicate \\\n",
- "572 aio:LazyInstanceNorm2d_Layer LazyInstanceNorm2d Layer rdfs:subClassOf \n",
- "573 aio:LazyInstanceNorm2d_Layer LazyInstanceNorm2d Layer rdfs:subClassOf \n",
- "574 aio:LazyInstanceNorm2d_Layer LazyInstanceNorm2d Layer rdfs:subClassOf \n",
- "575 aio:LazyInstanceNorm2d_Layer LazyInstanceNorm2d Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "572 None aio:Layer \n",
- "573 None aio:LazyInstanceNorm2d_Layer \n",
- "574 None aio:Normalization_Layer \n",
- "575 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "572 Layer \n",
- "573 LazyInstanceNorm2d Layer \n",
- "574 Normalization Layer \n",
- "575 Numerical Features Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:LazyInstanceNorm3d_Layer\n",
- " subject subject_label predicate \\\n",
- "576 aio:LazyInstanceNorm3d_Layer LazyInstanceNorm3d Layer rdfs:subClassOf \n",
- "577 aio:LazyInstanceNorm3d_Layer LazyInstanceNorm3d Layer rdfs:subClassOf \n",
- "578 aio:LazyInstanceNorm3d_Layer LazyInstanceNorm3d Layer rdfs:subClassOf \n",
- "579 aio:LazyInstanceNorm3d_Layer LazyInstanceNorm3d Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "576 None aio:Layer \n",
- "577 None aio:LazyInstanceNorm3d_Layer \n",
- "578 None aio:Normalization_Layer \n",
- "579 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "576 Layer \n",
- "577 LazyInstanceNorm3d Layer \n",
- "578 Normalization Layer \n",
- "579 Numerical Features Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:LazyInstanceNorm3d_Layer\n",
- " subject subject_label predicate \\\n",
- "576 aio:LazyInstanceNorm3d_Layer LazyInstanceNorm3d Layer rdfs:subClassOf \n",
- "577 aio:LazyInstanceNorm3d_Layer LazyInstanceNorm3d Layer rdfs:subClassOf \n",
- "578 aio:LazyInstanceNorm3d_Layer LazyInstanceNorm3d Layer rdfs:subClassOf \n",
- "579 aio:LazyInstanceNorm3d_Layer LazyInstanceNorm3d Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "576 None aio:Layer \n",
- "577 None aio:LazyInstanceNorm3d_Layer \n",
- "578 None aio:Normalization_Layer \n",
- "579 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "576 Layer \n",
- "577 LazyInstanceNorm3d Layer \n",
- "578 Normalization Layer \n",
- "579 Numerical Features Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:LazyInstanceNorm3d_Layer\n",
- " subject subject_label predicate \\\n",
- "576 aio:LazyInstanceNorm3d_Layer LazyInstanceNorm3d Layer rdfs:subClassOf \n",
- "577 aio:LazyInstanceNorm3d_Layer LazyInstanceNorm3d Layer rdfs:subClassOf \n",
- "578 aio:LazyInstanceNorm3d_Layer LazyInstanceNorm3d Layer rdfs:subClassOf \n",
- "579 aio:LazyInstanceNorm3d_Layer LazyInstanceNorm3d Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "576 None aio:Layer \n",
- "577 None aio:LazyInstanceNorm3d_Layer \n",
- "578 None aio:Normalization_Layer \n",
- "579 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "576 Layer \n",
- "577 LazyInstanceNorm3d Layer \n",
- "578 Normalization Layer \n",
- "579 Numerical Features Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:LazyInstanceNorm3d_Layer\n",
- " subject subject_label predicate \\\n",
- "576 aio:LazyInstanceNorm3d_Layer LazyInstanceNorm3d Layer rdfs:subClassOf \n",
- "577 aio:LazyInstanceNorm3d_Layer LazyInstanceNorm3d Layer rdfs:subClassOf \n",
- "578 aio:LazyInstanceNorm3d_Layer LazyInstanceNorm3d Layer rdfs:subClassOf \n",
- "579 aio:LazyInstanceNorm3d_Layer LazyInstanceNorm3d Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "576 None aio:Layer \n",
- "577 None aio:LazyInstanceNorm3d_Layer \n",
- "578 None aio:Normalization_Layer \n",
- "579 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "576 Layer \n",
- "577 LazyInstanceNorm3d Layer \n",
- "578 Normalization Layer \n",
- "579 Numerical Features Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:LeakyReLU_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "580 aio:LeakyReLU_Layer LeakyReLU Layer rdfs:subClassOf None \n",
- "581 aio:LeakyReLU_Layer LeakyReLU Layer rdfs:subClassOf None \n",
- "582 aio:LeakyReLU_Layer LeakyReLU Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "580 aio:Activation_Layer Activation Layer \n",
- "581 aio:Layer Layer \n",
- "582 aio:LeakyReLU_Layer LeakyReLU Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:Activation_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "7 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "8 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "7 aio:Activation_Layer Activation Layer \n",
- "8 aio:Layer Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:LeakyReLU_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "580 aio:LeakyReLU_Layer LeakyReLU Layer rdfs:subClassOf None \n",
- "581 aio:LeakyReLU_Layer LeakyReLU Layer rdfs:subClassOf None \n",
- "582 aio:LeakyReLU_Layer LeakyReLU Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "580 aio:Activation_Layer Activation Layer \n",
- "581 aio:Layer Layer \n",
- "582 aio:LeakyReLU_Layer LeakyReLU Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:Activation_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "7 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "8 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "7 aio:Activation_Layer Activation Layer \n",
- "8 aio:Layer Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:LeakyReLU_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "580 aio:LeakyReLU_Layer LeakyReLU Layer rdfs:subClassOf None \n",
- "581 aio:LeakyReLU_Layer LeakyReLU Layer rdfs:subClassOf None \n",
- "582 aio:LeakyReLU_Layer LeakyReLU Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "580 aio:Activation_Layer Activation Layer \n",
- "581 aio:Layer Layer \n",
- "582 aio:LeakyReLU_Layer LeakyReLU Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:Activation_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "7 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "8 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "7 aio:Activation_Layer Activation Layer \n",
- "8 aio:Layer Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:Least-squares_Analysis\n",
- " subject subject_label predicate \\\n",
- "583 aio:Least-squares_Analysis Least-squares Analysis rdfs:subClassOf \n",
- "584 aio:Least-squares_Analysis Least-squares Analysis rdfs:subClassOf \n",
- "585 aio:Least-squares_Analysis Least-squares Analysis rdfs:subClassOf \n",
- "586 aio:Least-squares_Analysis Least-squares Analysis rdfs:subClassOf \n",
- "587 aio:Least-squares_Analysis Least-squares Analysis rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "583 None aio:Least-squares_Analysis Least-squares Analysis \n",
- "584 None aio:Machine_Learning Machine Learning \n",
- "585 None aio:Method Method \n",
- "586 None aio:Regression_Analysis Regression Analysis \n",
- "587 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Least-squares_Analysis\n",
- "aio:Least-squares_Analysis\n",
- " subject subject_label predicate \\\n",
- "583 aio:Least-squares_Analysis Least-squares Analysis rdfs:subClassOf \n",
- "584 aio:Least-squares_Analysis Least-squares Analysis rdfs:subClassOf \n",
- "585 aio:Least-squares_Analysis Least-squares Analysis rdfs:subClassOf \n",
- "586 aio:Least-squares_Analysis Least-squares Analysis rdfs:subClassOf \n",
- "587 aio:Least-squares_Analysis Least-squares Analysis rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "583 None aio:Least-squares_Analysis Least-squares Analysis \n",
- "584 None aio:Machine_Learning Machine Learning \n",
- "585 None aio:Method Method \n",
- "586 None aio:Regression_Analysis Regression Analysis \n",
- "587 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Least-squares_Analysis\n",
- "aio:Least-squares_Analysis\n",
- " subject subject_label predicate \\\n",
- "583 aio:Least-squares_Analysis Least-squares Analysis rdfs:subClassOf \n",
- "584 aio:Least-squares_Analysis Least-squares Analysis rdfs:subClassOf \n",
- "585 aio:Least-squares_Analysis Least-squares Analysis rdfs:subClassOf \n",
- "586 aio:Least-squares_Analysis Least-squares Analysis rdfs:subClassOf \n",
- "587 aio:Least-squares_Analysis Least-squares Analysis rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "583 None aio:Least-squares_Analysis Least-squares Analysis \n",
- "584 None aio:Machine_Learning Machine Learning \n",
- "585 None aio:Method Method \n",
- "586 None aio:Regression_Analysis Regression Analysis \n",
- "587 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Least-squares_Analysis\n",
- "aio:Least-squares_Analysis\n",
- " subject subject_label predicate \\\n",
- "583 aio:Least-squares_Analysis Least-squares Analysis rdfs:subClassOf \n",
- "584 aio:Least-squares_Analysis Least-squares Analysis rdfs:subClassOf \n",
- "585 aio:Least-squares_Analysis Least-squares Analysis rdfs:subClassOf \n",
- "586 aio:Least-squares_Analysis Least-squares Analysis rdfs:subClassOf \n",
- "587 aio:Least-squares_Analysis Least-squares Analysis rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "583 None aio:Least-squares_Analysis Least-squares Analysis \n",
- "584 None aio:Machine_Learning Machine Learning \n",
- "585 None aio:Method Method \n",
- "586 None aio:Regression_Analysis Regression Analysis \n",
- "587 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Least-squares_Analysis\n",
- "aio:Least-squares_Analysis\n",
- " subject subject_label predicate \\\n",
- "583 aio:Least-squares_Analysis Least-squares Analysis rdfs:subClassOf \n",
- "584 aio:Least-squares_Analysis Least-squares Analysis rdfs:subClassOf \n",
- "585 aio:Least-squares_Analysis Least-squares Analysis rdfs:subClassOf \n",
- "586 aio:Least-squares_Analysis Least-squares Analysis rdfs:subClassOf \n",
- "587 aio:Least-squares_Analysis Least-squares Analysis rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "583 None aio:Least-squares_Analysis Least-squares Analysis \n",
- "584 None aio:Machine_Learning Machine Learning \n",
- "585 None aio:Method Method \n",
- "586 None aio:Regression_Analysis Regression Analysis \n",
- "587 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Least-squares_Analysis\n",
- "aio:Linear_Function\n",
- " subject subject_label predicate predicate_label \\\n",
- "588 aio:Linear_Function Linear Function rdfs:subClassOf None \n",
- "589 aio:Linear_Function Linear Function rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "588 aio:Function None \n",
- "589 aio:Linear_Function Linear Function \n",
- "parent aio:Function\n",
- "aio:Linear_Function\n",
- " subject subject_label predicate predicate_label \\\n",
- "588 aio:Linear_Function Linear Function rdfs:subClassOf None \n",
- "589 aio:Linear_Function Linear Function rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "588 aio:Function None \n",
- "589 aio:Linear_Function Linear Function \n",
- "parent aio:Function\n",
- "aio:Linear_Regression\n",
- " subject subject_label predicate \\\n",
- "590 aio:Linear_Regression Linear Regression rdfs:subClassOf \n",
- "591 aio:Linear_Regression Linear Regression rdfs:subClassOf \n",
- "592 aio:Linear_Regression Linear Regression rdfs:subClassOf \n",
- "593 aio:Linear_Regression Linear Regression rdfs:subClassOf \n",
- "594 aio:Linear_Regression Linear Regression rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "590 None aio:Linear_Regression Linear Regression \n",
- "591 None aio:Machine_Learning Machine Learning \n",
- "592 None aio:Method Method \n",
- "593 None aio:Regression_Analysis Regression Analysis \n",
- "594 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Linear_Regression\n",
- "aio:Linear_Regression\n",
- " subject subject_label predicate \\\n",
- "590 aio:Linear_Regression Linear Regression rdfs:subClassOf \n",
- "591 aio:Linear_Regression Linear Regression rdfs:subClassOf \n",
- "592 aio:Linear_Regression Linear Regression rdfs:subClassOf \n",
- "593 aio:Linear_Regression Linear Regression rdfs:subClassOf \n",
- "594 aio:Linear_Regression Linear Regression rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "590 None aio:Linear_Regression Linear Regression \n",
- "591 None aio:Machine_Learning Machine Learning \n",
- "592 None aio:Method Method \n",
- "593 None aio:Regression_Analysis Regression Analysis \n",
- "594 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Linear_Regression\n",
- "aio:Linear_Regression\n",
- " subject subject_label predicate \\\n",
- "590 aio:Linear_Regression Linear Regression rdfs:subClassOf \n",
- "591 aio:Linear_Regression Linear Regression rdfs:subClassOf \n",
- "592 aio:Linear_Regression Linear Regression rdfs:subClassOf \n",
- "593 aio:Linear_Regression Linear Regression rdfs:subClassOf \n",
- "594 aio:Linear_Regression Linear Regression rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "590 None aio:Linear_Regression Linear Regression \n",
- "591 None aio:Machine_Learning Machine Learning \n",
- "592 None aio:Method Method \n",
- "593 None aio:Regression_Analysis Regression Analysis \n",
- "594 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Linear_Regression\n",
- "aio:Linear_Regression\n",
- " subject subject_label predicate \\\n",
- "590 aio:Linear_Regression Linear Regression rdfs:subClassOf \n",
- "591 aio:Linear_Regression Linear Regression rdfs:subClassOf \n",
- "592 aio:Linear_Regression Linear Regression rdfs:subClassOf \n",
- "593 aio:Linear_Regression Linear Regression rdfs:subClassOf \n",
- "594 aio:Linear_Regression Linear Regression rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "590 None aio:Linear_Regression Linear Regression \n",
- "591 None aio:Machine_Learning Machine Learning \n",
- "592 None aio:Method Method \n",
- "593 None aio:Regression_Analysis Regression Analysis \n",
- "594 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Linear_Regression\n",
- "aio:Linear_Regression\n",
- " subject subject_label predicate \\\n",
- "590 aio:Linear_Regression Linear Regression rdfs:subClassOf \n",
- "591 aio:Linear_Regression Linear Regression rdfs:subClassOf \n",
- "592 aio:Linear_Regression Linear Regression rdfs:subClassOf \n",
- "593 aio:Linear_Regression Linear Regression rdfs:subClassOf \n",
- "594 aio:Linear_Regression Linear Regression rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "590 None aio:Linear_Regression Linear Regression \n",
- "591 None aio:Machine_Learning Machine Learning \n",
- "592 None aio:Method Method \n",
- "593 None aio:Regression_Analysis Regression Analysis \n",
- "594 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Linear_Regression\n",
- "aio:Linking_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "595 aio:Linking_Bias Linking Bias rdfs:subClassOf None \n",
- "596 aio:Linking_Bias Linking Bias rdfs:subClassOf None \n",
- "597 aio:Linking_Bias Linking Bias rdfs:subClassOf None \n",
- "598 aio:Linking_Bias Linking Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "595 aio:Bias Bias \n",
- "596 aio:Computational_Bias Computational Bias \n",
- "597 aio:Linking_Bias Linking Bias \n",
- "598 aio:Use_And_Interpretation_Bias Use And Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:Linking_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "595 aio:Linking_Bias Linking Bias rdfs:subClassOf None \n",
- "596 aio:Linking_Bias Linking Bias rdfs:subClassOf None \n",
- "597 aio:Linking_Bias Linking Bias rdfs:subClassOf None \n",
- "598 aio:Linking_Bias Linking Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "595 aio:Bias Bias \n",
- "596 aio:Computational_Bias Computational Bias \n",
- "597 aio:Linking_Bias Linking Bias \n",
- "598 aio:Use_And_Interpretation_Bias Use And Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:Linking_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "595 aio:Linking_Bias Linking Bias rdfs:subClassOf None \n",
- "596 aio:Linking_Bias Linking Bias rdfs:subClassOf None \n",
- "597 aio:Linking_Bias Linking Bias rdfs:subClassOf None \n",
- "598 aio:Linking_Bias Linking Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "595 aio:Bias Bias \n",
- "596 aio:Computational_Bias Computational Bias \n",
- "597 aio:Linking_Bias Linking Bias \n",
- "598 aio:Use_And_Interpretation_Bias Use And Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:Linking_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "595 aio:Linking_Bias Linking Bias rdfs:subClassOf None \n",
- "596 aio:Linking_Bias Linking Bias rdfs:subClassOf None \n",
- "597 aio:Linking_Bias Linking Bias rdfs:subClassOf None \n",
- "598 aio:Linking_Bias Linking Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "595 aio:Bias Bias \n",
- "596 aio:Computational_Bias Computational Bias \n",
- "597 aio:Linking_Bias Linking Bias \n",
- "598 aio:Use_And_Interpretation_Bias Use And Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:LocalResponseNorm_Layer\n",
- " subject subject_label predicate \\\n",
- "599 aio:LocalResponseNorm_Layer LocalResponseNorm Layer rdfs:subClassOf \n",
- "600 aio:LocalResponseNorm_Layer LocalResponseNorm Layer rdfs:subClassOf \n",
- "601 aio:LocalResponseNorm_Layer LocalResponseNorm Layer rdfs:subClassOf \n",
- "602 aio:LocalResponseNorm_Layer LocalResponseNorm Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "599 None aio:Layer \n",
- "600 None aio:LocalResponseNorm_Layer \n",
- "601 None aio:Normalization_Layer \n",
- "602 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "599 Layer \n",
- "600 LocalResponseNorm Layer \n",
- "601 Normalization Layer \n",
- "602 Numerical Features Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:LocalResponseNorm_Layer\n",
- " subject subject_label predicate \\\n",
- "599 aio:LocalResponseNorm_Layer LocalResponseNorm Layer rdfs:subClassOf \n",
- "600 aio:LocalResponseNorm_Layer LocalResponseNorm Layer rdfs:subClassOf \n",
- "601 aio:LocalResponseNorm_Layer LocalResponseNorm Layer rdfs:subClassOf \n",
- "602 aio:LocalResponseNorm_Layer LocalResponseNorm Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "599 None aio:Layer \n",
- "600 None aio:LocalResponseNorm_Layer \n",
- "601 None aio:Normalization_Layer \n",
- "602 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "599 Layer \n",
- "600 LocalResponseNorm Layer \n",
- "601 Normalization Layer \n",
- "602 Numerical Features Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:LocalResponseNorm_Layer\n",
- " subject subject_label predicate \\\n",
- "599 aio:LocalResponseNorm_Layer LocalResponseNorm Layer rdfs:subClassOf \n",
- "600 aio:LocalResponseNorm_Layer LocalResponseNorm Layer rdfs:subClassOf \n",
- "601 aio:LocalResponseNorm_Layer LocalResponseNorm Layer rdfs:subClassOf \n",
- "602 aio:LocalResponseNorm_Layer LocalResponseNorm Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "599 None aio:Layer \n",
- "600 None aio:LocalResponseNorm_Layer \n",
- "601 None aio:Normalization_Layer \n",
- "602 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "599 Layer \n",
- "600 LocalResponseNorm Layer \n",
- "601 Normalization Layer \n",
- "602 Numerical Features Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:LocalResponseNorm_Layer\n",
- " subject subject_label predicate \\\n",
- "599 aio:LocalResponseNorm_Layer LocalResponseNorm Layer rdfs:subClassOf \n",
- "600 aio:LocalResponseNorm_Layer LocalResponseNorm Layer rdfs:subClassOf \n",
- "601 aio:LocalResponseNorm_Layer LocalResponseNorm Layer rdfs:subClassOf \n",
- "602 aio:LocalResponseNorm_Layer LocalResponseNorm Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "599 None aio:Layer \n",
- "600 None aio:LocalResponseNorm_Layer \n",
- "601 None aio:Normalization_Layer \n",
- "602 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "599 Layer \n",
- "600 LocalResponseNorm Layer \n",
- "601 Normalization Layer \n",
- "602 Numerical Features Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:Locally-connected_Layer\n",
- " subject subject_label predicate \\\n",
- "603 aio:Locally-connected_Layer Locally-connected Layer rdfs:subClassOf \n",
- "604 aio:Locally-connected_Layer Locally-connected Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "603 None aio:Layer Layer \n",
- "604 None aio:Locally-connected_Layer Locally-connected Layer \n",
- "parent aio:Layer\n",
- "aio:Locally-connected_Layer\n",
- " subject subject_label predicate \\\n",
- "603 aio:Locally-connected_Layer Locally-connected Layer rdfs:subClassOf \n",
- "604 aio:Locally-connected_Layer Locally-connected Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "603 None aio:Layer Layer \n",
- "604 None aio:Locally-connected_Layer Locally-connected Layer \n",
- "parent aio:Layer\n",
- "aio:LocallyConnected1D_Layer\n",
- " subject subject_label predicate \\\n",
- "605 aio:LocallyConnected1D_Layer LocallyConnected1D Layer rdfs:subClassOf \n",
- "606 aio:LocallyConnected1D_Layer LocallyConnected1D Layer rdfs:subClassOf \n",
- "607 aio:LocallyConnected1D_Layer LocallyConnected1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "605 None aio:Layer Layer \n",
- "606 None aio:Locally-connected_Layer Locally-connected Layer \n",
- "607 None aio:LocallyConnected1D_Layer LocallyConnected1D Layer \n",
- "parent aio:Layer\n",
- "aio:LocallyConnected1D_Layer\n",
- " subject subject_label predicate \\\n",
- "605 aio:LocallyConnected1D_Layer LocallyConnected1D Layer rdfs:subClassOf \n",
- "606 aio:LocallyConnected1D_Layer LocallyConnected1D Layer rdfs:subClassOf \n",
- "607 aio:LocallyConnected1D_Layer LocallyConnected1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "605 None aio:Layer Layer \n",
- "606 None aio:Locally-connected_Layer Locally-connected Layer \n",
- "607 None aio:LocallyConnected1D_Layer LocallyConnected1D Layer \n",
- "parent aio:Layer\n",
- "aio:LocallyConnected1D_Layer\n",
- " subject subject_label predicate \\\n",
- "605 aio:LocallyConnected1D_Layer LocallyConnected1D Layer rdfs:subClassOf \n",
- "606 aio:LocallyConnected1D_Layer LocallyConnected1D Layer rdfs:subClassOf \n",
- "607 aio:LocallyConnected1D_Layer LocallyConnected1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "605 None aio:Layer Layer \n",
- "606 None aio:Locally-connected_Layer Locally-connected Layer \n",
- "607 None aio:LocallyConnected1D_Layer LocallyConnected1D Layer \n",
- "parent aio:Layer\n",
- "aio:LocallyConnected2D_Layer\n",
- " subject subject_label predicate \\\n",
- "608 aio:LocallyConnected2D_Layer LocallyConnected2D Layer rdfs:subClassOf \n",
- "609 aio:LocallyConnected2D_Layer LocallyConnected2D Layer rdfs:subClassOf \n",
- "610 aio:LocallyConnected2D_Layer LocallyConnected2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "608 None aio:Layer Layer \n",
- "609 None aio:Locally-connected_Layer Locally-connected Layer \n",
- "610 None aio:LocallyConnected2D_Layer LocallyConnected2D Layer \n",
- "parent aio:Layer\n",
- "aio:LocallyConnected2D_Layer\n",
- " subject subject_label predicate \\\n",
- "608 aio:LocallyConnected2D_Layer LocallyConnected2D Layer rdfs:subClassOf \n",
- "609 aio:LocallyConnected2D_Layer LocallyConnected2D Layer rdfs:subClassOf \n",
- "610 aio:LocallyConnected2D_Layer LocallyConnected2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "608 None aio:Layer Layer \n",
- "609 None aio:Locally-connected_Layer Locally-connected Layer \n",
- "610 None aio:LocallyConnected2D_Layer LocallyConnected2D Layer \n",
- "parent aio:Layer\n",
- "aio:LocallyConnected2D_Layer\n",
- " subject subject_label predicate \\\n",
- "608 aio:LocallyConnected2D_Layer LocallyConnected2D Layer rdfs:subClassOf \n",
- "609 aio:LocallyConnected2D_Layer LocallyConnected2D Layer rdfs:subClassOf \n",
- "610 aio:LocallyConnected2D_Layer LocallyConnected2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "608 None aio:Layer Layer \n",
- "609 None aio:Locally-connected_Layer Locally-connected Layer \n",
- "610 None aio:LocallyConnected2D_Layer LocallyConnected2D Layer \n",
- "parent aio:Layer\n",
- "aio:Logistic_Regression\n",
- " subject subject_label predicate \\\n",
- "611 aio:Logistic_Regression Logistic Regression rdfs:subClassOf \n",
- "612 aio:Logistic_Regression Logistic Regression rdfs:subClassOf \n",
- "613 aio:Logistic_Regression Logistic Regression rdfs:subClassOf \n",
- "614 aio:Logistic_Regression Logistic Regression rdfs:subClassOf \n",
- "615 aio:Logistic_Regression Logistic Regression rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "611 None aio:Logistic_Regression Logistic Regression \n",
- "612 None aio:Machine_Learning Machine Learning \n",
- "613 None aio:Method Method \n",
- "614 None aio:Regression_Analysis Regression Analysis \n",
- "615 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Logistic_Regression\n",
- "aio:Logistic_Regression\n",
- " subject subject_label predicate \\\n",
- "611 aio:Logistic_Regression Logistic Regression rdfs:subClassOf \n",
- "612 aio:Logistic_Regression Logistic Regression rdfs:subClassOf \n",
- "613 aio:Logistic_Regression Logistic Regression rdfs:subClassOf \n",
- "614 aio:Logistic_Regression Logistic Regression rdfs:subClassOf \n",
- "615 aio:Logistic_Regression Logistic Regression rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "611 None aio:Logistic_Regression Logistic Regression \n",
- "612 None aio:Machine_Learning Machine Learning \n",
- "613 None aio:Method Method \n",
- "614 None aio:Regression_Analysis Regression Analysis \n",
- "615 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Logistic_Regression\n",
- "aio:Logistic_Regression\n",
- " subject subject_label predicate \\\n",
- "611 aio:Logistic_Regression Logistic Regression rdfs:subClassOf \n",
- "612 aio:Logistic_Regression Logistic Regression rdfs:subClassOf \n",
- "613 aio:Logistic_Regression Logistic Regression rdfs:subClassOf \n",
- "614 aio:Logistic_Regression Logistic Regression rdfs:subClassOf \n",
- "615 aio:Logistic_Regression Logistic Regression rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "611 None aio:Logistic_Regression Logistic Regression \n",
- "612 None aio:Machine_Learning Machine Learning \n",
- "613 None aio:Method Method \n",
- "614 None aio:Regression_Analysis Regression Analysis \n",
- "615 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Logistic_Regression\n",
- "aio:Logistic_Regression\n",
- " subject subject_label predicate \\\n",
- "611 aio:Logistic_Regression Logistic Regression rdfs:subClassOf \n",
- "612 aio:Logistic_Regression Logistic Regression rdfs:subClassOf \n",
- "613 aio:Logistic_Regression Logistic Regression rdfs:subClassOf \n",
- "614 aio:Logistic_Regression Logistic Regression rdfs:subClassOf \n",
- "615 aio:Logistic_Regression Logistic Regression rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "611 None aio:Logistic_Regression Logistic Regression \n",
- "612 None aio:Machine_Learning Machine Learning \n",
- "613 None aio:Method Method \n",
- "614 None aio:Regression_Analysis Regression Analysis \n",
- "615 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Logistic_Regression\n",
- "aio:Logistic_Regression\n",
- " subject subject_label predicate \\\n",
- "611 aio:Logistic_Regression Logistic Regression rdfs:subClassOf \n",
- "612 aio:Logistic_Regression Logistic Regression rdfs:subClassOf \n",
- "613 aio:Logistic_Regression Logistic Regression rdfs:subClassOf \n",
- "614 aio:Logistic_Regression Logistic Regression rdfs:subClassOf \n",
- "615 aio:Logistic_Regression Logistic Regression rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "611 None aio:Logistic_Regression Logistic Regression \n",
- "612 None aio:Machine_Learning Machine Learning \n",
- "613 None aio:Method Method \n",
- "614 None aio:Regression_Analysis Regression Analysis \n",
- "615 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Logistic_Regression\n",
- "aio:Loss_Of_Situational_Awareness_Bias\n",
- " subject \\\n",
- "616 aio:Loss_Of_Situational_Awareness_Bias \n",
- "617 aio:Loss_Of_Situational_Awareness_Bias \n",
- "618 aio:Loss_Of_Situational_Awareness_Bias \n",
- "\n",
- " subject_label predicate predicate_label \\\n",
- "616 Loss Of Situational Awareness Bias rdfs:subClassOf None \n",
- "617 Loss Of Situational Awareness Bias rdfs:subClassOf None \n",
- "618 Loss Of Situational Awareness Bias rdfs:subClassOf None \n",
- "\n",
- " object \\\n",
- "616 aio:Bias \n",
- "617 aio:Individual_Bias \n",
- "618 aio:Loss_Of_Situational_Awareness_Bias \n",
- "\n",
- " object_label \n",
- "616 Bias \n",
- "617 Individual Bias \n",
- "618 Loss Of Situational Awareness Bias \n",
- "parent aio:Bias\n",
- "aio:Loss_Of_Situational_Awareness_Bias\n",
- " subject \\\n",
- "616 aio:Loss_Of_Situational_Awareness_Bias \n",
- "617 aio:Loss_Of_Situational_Awareness_Bias \n",
- "618 aio:Loss_Of_Situational_Awareness_Bias \n",
- "\n",
- " subject_label predicate predicate_label \\\n",
- "616 Loss Of Situational Awareness Bias rdfs:subClassOf None \n",
- "617 Loss Of Situational Awareness Bias rdfs:subClassOf None \n",
- "618 Loss Of Situational Awareness Bias rdfs:subClassOf None \n",
- "\n",
- " object \\\n",
- "616 aio:Bias \n",
- "617 aio:Individual_Bias \n",
- "618 aio:Loss_Of_Situational_Awareness_Bias \n",
- "\n",
- " object_label \n",
- "616 Bias \n",
- "617 Individual Bias \n",
- "618 Loss Of Situational Awareness Bias \n",
- "parent aio:Bias\n",
- "aio:Loss_Of_Situational_Awareness_Bias\n",
- " subject \\\n",
- "616 aio:Loss_Of_Situational_Awareness_Bias \n",
- "617 aio:Loss_Of_Situational_Awareness_Bias \n",
- "618 aio:Loss_Of_Situational_Awareness_Bias \n",
- "\n",
- " subject_label predicate predicate_label \\\n",
- "616 Loss Of Situational Awareness Bias rdfs:subClassOf None \n",
- "617 Loss Of Situational Awareness Bias rdfs:subClassOf None \n",
- "618 Loss Of Situational Awareness Bias rdfs:subClassOf None \n",
- "\n",
- " object \\\n",
- "616 aio:Bias \n",
- "617 aio:Individual_Bias \n",
- "618 aio:Loss_Of_Situational_Awareness_Bias \n",
- "\n",
- " object_label \n",
- "616 Bias \n",
- "617 Individual Bias \n",
- "618 Loss Of Situational Awareness Bias \n",
- "parent aio:Bias\n",
- "aio:MC\n",
- " subject subject_label predicate predicate_label object \\\n",
- "619 aio:MC Markov Chain rdfs:subClassOf None aio:MC \n",
- "620 aio:MC Markov Chain rdfs:subClassOf None aio:Network \n",
- "\n",
- " object_label \n",
- "619 Markov Chain \n",
- "620 Network \n",
- "parent aio:MC\n",
- "aio:MC\n",
- " subject subject_label predicate predicate_label object \\\n",
- "619 aio:MC Markov Chain rdfs:subClassOf None aio:MC \n",
- "620 aio:MC Markov Chain rdfs:subClassOf None aio:Network \n",
- "\n",
- " object_label \n",
- "619 Markov Chain \n",
- "620 Network \n",
- "parent aio:MC\n",
- "aio:Machine_Learning\n",
- " subject subject_label predicate predicate_label \\\n",
- "621 aio:Machine_Learning Machine Learning rdfs:subClassOf None \n",
- "622 aio:Machine_Learning Machine Learning rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "621 aio:Machine_Learning Machine Learning \n",
- "622 aio:Method Method \n",
- "parent aio:Machine_Learning\n",
- "aio:Machine_Learning\n",
- " subject subject_label predicate predicate_label \\\n",
- "621 aio:Machine_Learning Machine Learning rdfs:subClassOf None \n",
- "622 aio:Machine_Learning Machine Learning rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "621 aio:Machine_Learning Machine Learning \n",
- "622 aio:Method Method \n",
- "parent aio:Machine_Learning\n",
- "aio:Manifold_Learning\n",
- " subject subject_label predicate \\\n",
- "623 aio:Manifold_Learning Manifold Learning rdfs:subClassOf \n",
- "624 aio:Manifold_Learning Manifold Learning rdfs:subClassOf \n",
- "625 aio:Manifold_Learning Manifold Learning rdfs:subClassOf \n",
- "626 aio:Manifold_Learning Manifold Learning rdfs:subClassOf \n",
- "627 aio:Manifold_Learning Manifold Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "623 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "624 None aio:Machine_Learning Machine Learning \n",
- "625 None aio:Manifold_Learning Manifold Learning \n",
- "626 None aio:Method Method \n",
- "627 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Dimensionality_Reduction\n",
- " subject subject_label predicate \\\n",
- "275 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "276 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "277 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "278 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "275 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "276 None aio:Machine_Learning Machine Learning \n",
- "277 None aio:Method Method \n",
- "278 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Manifold_Learning\n",
- " subject subject_label predicate \\\n",
- "623 aio:Manifold_Learning Manifold Learning rdfs:subClassOf \n",
- "624 aio:Manifold_Learning Manifold Learning rdfs:subClassOf \n",
- "625 aio:Manifold_Learning Manifold Learning rdfs:subClassOf \n",
- "626 aio:Manifold_Learning Manifold Learning rdfs:subClassOf \n",
- "627 aio:Manifold_Learning Manifold Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "623 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "624 None aio:Machine_Learning Machine Learning \n",
- "625 None aio:Manifold_Learning Manifold Learning \n",
- "626 None aio:Method Method \n",
- "627 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Dimensionality_Reduction\n",
- " subject subject_label predicate \\\n",
- "275 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "276 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "277 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "278 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "275 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "276 None aio:Machine_Learning Machine Learning \n",
- "277 None aio:Method Method \n",
- "278 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Manifold_Learning\n",
- " subject subject_label predicate \\\n",
- "623 aio:Manifold_Learning Manifold Learning rdfs:subClassOf \n",
- "624 aio:Manifold_Learning Manifold Learning rdfs:subClassOf \n",
- "625 aio:Manifold_Learning Manifold Learning rdfs:subClassOf \n",
- "626 aio:Manifold_Learning Manifold Learning rdfs:subClassOf \n",
- "627 aio:Manifold_Learning Manifold Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "623 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "624 None aio:Machine_Learning Machine Learning \n",
- "625 None aio:Manifold_Learning Manifold Learning \n",
- "626 None aio:Method Method \n",
- "627 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Dimensionality_Reduction\n",
- " subject subject_label predicate \\\n",
- "275 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "276 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "277 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "278 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "275 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "276 None aio:Machine_Learning Machine Learning \n",
- "277 None aio:Method Method \n",
- "278 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Manifold_Learning\n",
- " subject subject_label predicate \\\n",
- "623 aio:Manifold_Learning Manifold Learning rdfs:subClassOf \n",
- "624 aio:Manifold_Learning Manifold Learning rdfs:subClassOf \n",
- "625 aio:Manifold_Learning Manifold Learning rdfs:subClassOf \n",
- "626 aio:Manifold_Learning Manifold Learning rdfs:subClassOf \n",
- "627 aio:Manifold_Learning Manifold Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "623 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "624 None aio:Machine_Learning Machine Learning \n",
- "625 None aio:Manifold_Learning Manifold Learning \n",
- "626 None aio:Method Method \n",
- "627 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Dimensionality_Reduction\n",
- " subject subject_label predicate \\\n",
- "275 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "276 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "277 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "278 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "275 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "276 None aio:Machine_Learning Machine Learning \n",
- "277 None aio:Method Method \n",
- "278 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Manifold_Learning\n",
- " subject subject_label predicate \\\n",
- "623 aio:Manifold_Learning Manifold Learning rdfs:subClassOf \n",
- "624 aio:Manifold_Learning Manifold Learning rdfs:subClassOf \n",
- "625 aio:Manifold_Learning Manifold Learning rdfs:subClassOf \n",
- "626 aio:Manifold_Learning Manifold Learning rdfs:subClassOf \n",
- "627 aio:Manifold_Learning Manifold Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "623 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "624 None aio:Machine_Learning Machine Learning \n",
- "625 None aio:Manifold_Learning Manifold Learning \n",
- "626 None aio:Method Method \n",
- "627 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Dimensionality_Reduction\n",
- " subject subject_label predicate \\\n",
- "275 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "276 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "277 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "278 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "275 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "276 None aio:Machine_Learning Machine Learning \n",
- "277 None aio:Method Method \n",
- "278 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Masking_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "628 aio:Masking_Layer Masking Layer rdfs:subClassOf None \n",
- "629 aio:Masking_Layer Masking Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "628 aio:Layer Layer \n",
- "629 aio:Masking_Layer Masking Layer \n",
- "parent aio:Layer\n",
- "aio:Masking_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "628 aio:Masking_Layer Masking Layer rdfs:subClassOf None \n",
- "629 aio:Masking_Layer Masking Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "628 aio:Layer Layer \n",
- "629 aio:Masking_Layer Masking Layer \n",
- "parent aio:Layer\n",
- "aio:MaxPooling1D_Layer\n",
- " subject subject_label predicate \\\n",
- "630 aio:MaxPooling1D_Layer MaxPooling1D Layer rdfs:subClassOf \n",
- "631 aio:MaxPooling1D_Layer MaxPooling1D Layer rdfs:subClassOf \n",
- "632 aio:MaxPooling1D_Layer MaxPooling1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "630 None aio:Layer Layer \n",
- "631 None aio:MaxPooling1D_Layer MaxPooling1D Layer \n",
- "632 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:Layer\n",
- "aio:MaxPooling1D_Layer\n",
- " subject subject_label predicate \\\n",
- "630 aio:MaxPooling1D_Layer MaxPooling1D Layer rdfs:subClassOf \n",
- "631 aio:MaxPooling1D_Layer MaxPooling1D Layer rdfs:subClassOf \n",
- "632 aio:MaxPooling1D_Layer MaxPooling1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "630 None aio:Layer Layer \n",
- "631 None aio:MaxPooling1D_Layer MaxPooling1D Layer \n",
- "632 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:Layer\n",
- "aio:MaxPooling1D_Layer\n",
- " subject subject_label predicate \\\n",
- "630 aio:MaxPooling1D_Layer MaxPooling1D Layer rdfs:subClassOf \n",
- "631 aio:MaxPooling1D_Layer MaxPooling1D Layer rdfs:subClassOf \n",
- "632 aio:MaxPooling1D_Layer MaxPooling1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "630 None aio:Layer Layer \n",
- "631 None aio:MaxPooling1D_Layer MaxPooling1D Layer \n",
- "632 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:Layer\n",
- "aio:MaxPooling2D_Layer\n",
- " subject subject_label predicate \\\n",
- "633 aio:MaxPooling2D_Layer MaxPooling2D Layer rdfs:subClassOf \n",
- "634 aio:MaxPooling2D_Layer MaxPooling2D Layer rdfs:subClassOf \n",
- "635 aio:MaxPooling2D_Layer MaxPooling2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "633 None aio:Layer Layer \n",
- "634 None aio:MaxPooling2D_Layer MaxPooling2D Layer \n",
- "635 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:Layer\n",
- "aio:MaxPooling2D_Layer\n",
- " subject subject_label predicate \\\n",
- "633 aio:MaxPooling2D_Layer MaxPooling2D Layer rdfs:subClassOf \n",
- "634 aio:MaxPooling2D_Layer MaxPooling2D Layer rdfs:subClassOf \n",
- "635 aio:MaxPooling2D_Layer MaxPooling2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "633 None aio:Layer Layer \n",
- "634 None aio:MaxPooling2D_Layer MaxPooling2D Layer \n",
- "635 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:Layer\n",
- "aio:MaxPooling2D_Layer\n",
- " subject subject_label predicate \\\n",
- "633 aio:MaxPooling2D_Layer MaxPooling2D Layer rdfs:subClassOf \n",
- "634 aio:MaxPooling2D_Layer MaxPooling2D Layer rdfs:subClassOf \n",
- "635 aio:MaxPooling2D_Layer MaxPooling2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "633 None aio:Layer Layer \n",
- "634 None aio:MaxPooling2D_Layer MaxPooling2D Layer \n",
- "635 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:Layer\n",
- "aio:MaxPooling3D_Layer\n",
- " subject subject_label predicate \\\n",
- "636 aio:MaxPooling3D_Layer MaxPooling3D Layer rdfs:subClassOf \n",
- "637 aio:MaxPooling3D_Layer MaxPooling3D Layer rdfs:subClassOf \n",
- "638 aio:MaxPooling3D_Layer MaxPooling3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "636 None aio:Layer Layer \n",
- "637 None aio:MaxPooling3D_Layer MaxPooling3D Layer \n",
- "638 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:Layer\n",
- "aio:MaxPooling3D_Layer\n",
- " subject subject_label predicate \\\n",
- "636 aio:MaxPooling3D_Layer MaxPooling3D Layer rdfs:subClassOf \n",
- "637 aio:MaxPooling3D_Layer MaxPooling3D Layer rdfs:subClassOf \n",
- "638 aio:MaxPooling3D_Layer MaxPooling3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "636 None aio:Layer Layer \n",
- "637 None aio:MaxPooling3D_Layer MaxPooling3D Layer \n",
- "638 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:Layer\n",
- "aio:MaxPooling3D_Layer\n",
- " subject subject_label predicate \\\n",
- "636 aio:MaxPooling3D_Layer MaxPooling3D Layer rdfs:subClassOf \n",
- "637 aio:MaxPooling3D_Layer MaxPooling3D Layer rdfs:subClassOf \n",
- "638 aio:MaxPooling3D_Layer MaxPooling3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "636 None aio:Layer Layer \n",
- "637 None aio:MaxPooling3D_Layer MaxPooling3D Layer \n",
- "638 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:Layer\n",
- "aio:MaxUnpool1D_Layer\n",
- " subject subject_label predicate \\\n",
- "639 aio:MaxUnpool1D_Layer MaxUnpool1D Layer rdfs:subClassOf \n",
- "640 aio:MaxUnpool1D_Layer MaxUnpool1D Layer rdfs:subClassOf \n",
- "641 aio:MaxUnpool1D_Layer MaxUnpool1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "639 None aio:Layer Layer \n",
- "640 None aio:MaxUnpool1D_Layer MaxUnpool1D Layer \n",
- "641 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:Layer\n",
- "aio:MaxUnpool1D_Layer\n",
- " subject subject_label predicate \\\n",
- "639 aio:MaxUnpool1D_Layer MaxUnpool1D Layer rdfs:subClassOf \n",
- "640 aio:MaxUnpool1D_Layer MaxUnpool1D Layer rdfs:subClassOf \n",
- "641 aio:MaxUnpool1D_Layer MaxUnpool1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "639 None aio:Layer Layer \n",
- "640 None aio:MaxUnpool1D_Layer MaxUnpool1D Layer \n",
- "641 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:Layer\n",
- "aio:MaxUnpool1D_Layer\n",
- " subject subject_label predicate \\\n",
- "639 aio:MaxUnpool1D_Layer MaxUnpool1D Layer rdfs:subClassOf \n",
- "640 aio:MaxUnpool1D_Layer MaxUnpool1D Layer rdfs:subClassOf \n",
- "641 aio:MaxUnpool1D_Layer MaxUnpool1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "639 None aio:Layer Layer \n",
- "640 None aio:MaxUnpool1D_Layer MaxUnpool1D Layer \n",
- "641 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:Layer\n",
- "aio:MaxUnpool2D_Layer\n",
- " subject subject_label predicate \\\n",
- "642 aio:MaxUnpool2D_Layer MaxUnpool2D Layer rdfs:subClassOf \n",
- "643 aio:MaxUnpool2D_Layer MaxUnpool2D Layer rdfs:subClassOf \n",
- "644 aio:MaxUnpool2D_Layer MaxUnpool2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "642 None aio:Layer Layer \n",
- "643 None aio:MaxUnpool2D_Layer MaxUnpool2D Layer \n",
- "644 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:Layer\n",
- "aio:MaxUnpool2D_Layer\n",
- " subject subject_label predicate \\\n",
- "642 aio:MaxUnpool2D_Layer MaxUnpool2D Layer rdfs:subClassOf \n",
- "643 aio:MaxUnpool2D_Layer MaxUnpool2D Layer rdfs:subClassOf \n",
- "644 aio:MaxUnpool2D_Layer MaxUnpool2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "642 None aio:Layer Layer \n",
- "643 None aio:MaxUnpool2D_Layer MaxUnpool2D Layer \n",
- "644 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:Layer\n",
- "aio:MaxUnpool2D_Layer\n",
- " subject subject_label predicate \\\n",
- "642 aio:MaxUnpool2D_Layer MaxUnpool2D Layer rdfs:subClassOf \n",
- "643 aio:MaxUnpool2D_Layer MaxUnpool2D Layer rdfs:subClassOf \n",
- "644 aio:MaxUnpool2D_Layer MaxUnpool2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "642 None aio:Layer Layer \n",
- "643 None aio:MaxUnpool2D_Layer MaxUnpool2D Layer \n",
- "644 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:Layer\n",
- "aio:MaxUnpool3D_Layer\n",
- " subject subject_label predicate \\\n",
- "645 aio:MaxUnpool3D_Layer MaxUnpool3D Layer rdfs:subClassOf \n",
- "646 aio:MaxUnpool3D_Layer MaxUnpool3D Layer rdfs:subClassOf \n",
- "647 aio:MaxUnpool3D_Layer MaxUnpool3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "645 None aio:Layer Layer \n",
- "646 None aio:MaxUnpool3D_Layer MaxUnpool3D Layer \n",
- "647 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:Layer\n",
- "aio:MaxUnpool3D_Layer\n",
- " subject subject_label predicate \\\n",
- "645 aio:MaxUnpool3D_Layer MaxUnpool3D Layer rdfs:subClassOf \n",
- "646 aio:MaxUnpool3D_Layer MaxUnpool3D Layer rdfs:subClassOf \n",
- "647 aio:MaxUnpool3D_Layer MaxUnpool3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "645 None aio:Layer Layer \n",
- "646 None aio:MaxUnpool3D_Layer MaxUnpool3D Layer \n",
- "647 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:Layer\n",
- "aio:MaxUnpool3D_Layer\n",
- " subject subject_label predicate \\\n",
- "645 aio:MaxUnpool3D_Layer MaxUnpool3D Layer rdfs:subClassOf \n",
- "646 aio:MaxUnpool3D_Layer MaxUnpool3D Layer rdfs:subClassOf \n",
- "647 aio:MaxUnpool3D_Layer MaxUnpool3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "645 None aio:Layer Layer \n",
- "646 None aio:MaxUnpool3D_Layer MaxUnpool3D Layer \n",
- "647 None aio:Pooling_Layer Pooling Layer \n",
- "parent aio:Layer\n",
- "aio:Maximum_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "648 aio:Maximum_Layer Maximum Layer rdfs:subClassOf None \n",
- "649 aio:Maximum_Layer Maximum Layer rdfs:subClassOf None \n",
- "650 aio:Maximum_Layer Maximum Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "648 aio:Layer Layer \n",
- "649 aio:Maximum_Layer Maximum Layer \n",
- "650 aio:Merging_Layer Merging Layer \n",
- "parent aio:Layer\n",
- "aio:Maximum_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "648 aio:Maximum_Layer Maximum Layer rdfs:subClassOf None \n",
- "649 aio:Maximum_Layer Maximum Layer rdfs:subClassOf None \n",
- "650 aio:Maximum_Layer Maximum Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "648 aio:Layer Layer \n",
- "649 aio:Maximum_Layer Maximum Layer \n",
- "650 aio:Merging_Layer Merging Layer \n",
- "parent aio:Layer\n",
- "aio:Maximum_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "648 aio:Maximum_Layer Maximum Layer rdfs:subClassOf None \n",
- "649 aio:Maximum_Layer Maximum Layer rdfs:subClassOf None \n",
- "650 aio:Maximum_Layer Maximum Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "648 aio:Layer Layer \n",
- "649 aio:Maximum_Layer Maximum Layer \n",
- "650 aio:Merging_Layer Merging Layer \n",
- "parent aio:Layer\n",
- "aio:Measurement_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "651 aio:Measurement_Bias Measurement Bias rdfs:subClassOf None \n",
- "652 aio:Measurement_Bias Measurement Bias rdfs:subClassOf None \n",
- "653 aio:Measurement_Bias Measurement Bias rdfs:subClassOf None \n",
- "654 aio:Measurement_Bias Measurement Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "651 aio:Bias Bias \n",
- "652 aio:Computational_Bias Computational Bias \n",
- "653 aio:Measurement_Bias Measurement Bias \n",
- "654 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Measurement_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "651 aio:Measurement_Bias Measurement Bias rdfs:subClassOf None \n",
- "652 aio:Measurement_Bias Measurement Bias rdfs:subClassOf None \n",
- "653 aio:Measurement_Bias Measurement Bias rdfs:subClassOf None \n",
- "654 aio:Measurement_Bias Measurement Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "651 aio:Bias Bias \n",
- "652 aio:Computational_Bias Computational Bias \n",
- "653 aio:Measurement_Bias Measurement Bias \n",
- "654 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Measurement_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "651 aio:Measurement_Bias Measurement Bias rdfs:subClassOf None \n",
- "652 aio:Measurement_Bias Measurement Bias rdfs:subClassOf None \n",
- "653 aio:Measurement_Bias Measurement Bias rdfs:subClassOf None \n",
- "654 aio:Measurement_Bias Measurement Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "651 aio:Bias Bias \n",
- "652 aio:Computational_Bias Computational Bias \n",
- "653 aio:Measurement_Bias Measurement Bias \n",
- "654 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Measurement_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "651 aio:Measurement_Bias Measurement Bias rdfs:subClassOf None \n",
- "652 aio:Measurement_Bias Measurement Bias rdfs:subClassOf None \n",
- "653 aio:Measurement_Bias Measurement Bias rdfs:subClassOf None \n",
- "654 aio:Measurement_Bias Measurement Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "651 aio:Bias Bias \n",
- "652 aio:Computational_Bias Computational Bias \n",
- "653 aio:Measurement_Bias Measurement Bias \n",
- "654 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Merging_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "655 aio:Merging_Layer Merging Layer rdfs:subClassOf None \n",
- "656 aio:Merging_Layer Merging Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "655 aio:Layer Layer \n",
- "656 aio:Merging_Layer Merging Layer \n",
- "parent aio:Layer\n",
- "aio:Merging_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "655 aio:Merging_Layer Merging Layer rdfs:subClassOf None \n",
- "656 aio:Merging_Layer Merging Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "655 aio:Layer Layer \n",
- "656 aio:Merging_Layer Merging Layer \n",
- "parent aio:Layer\n",
- "aio:Meta-Learning\n",
- " subject subject_label predicate predicate_label \\\n",
- "657 aio:Meta-Learning Meta-Learning rdfs:subClassOf None \n",
- "658 aio:Meta-Learning Meta-Learning rdfs:subClassOf None \n",
- "659 aio:Meta-Learning Meta-Learning rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "657 aio:Machine_Learning Machine Learning \n",
- "658 aio:Meta-Learning Meta-Learning \n",
- "659 aio:Method Method \n",
- "parent aio:Machine_Learning\n",
- "aio:Meta-Learning\n",
- " subject subject_label predicate predicate_label \\\n",
- "657 aio:Meta-Learning Meta-Learning rdfs:subClassOf None \n",
- "658 aio:Meta-Learning Meta-Learning rdfs:subClassOf None \n",
- "659 aio:Meta-Learning Meta-Learning rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "657 aio:Machine_Learning Machine Learning \n",
- "658 aio:Meta-Learning Meta-Learning \n",
- "659 aio:Method Method \n",
- "parent aio:Machine_Learning\n",
- "aio:Meta-Learning\n",
- " subject subject_label predicate predicate_label \\\n",
- "657 aio:Meta-Learning Meta-Learning rdfs:subClassOf None \n",
- "658 aio:Meta-Learning Meta-Learning rdfs:subClassOf None \n",
- "659 aio:Meta-Learning Meta-Learning rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "657 aio:Machine_Learning Machine Learning \n",
- "658 aio:Meta-Learning Meta-Learning \n",
- "659 aio:Method Method \n",
- "parent aio:Machine_Learning\n",
- "aio:Method\n",
- " subject subject_label predicate predicate_label object \\\n",
- "660 aio:Method Method rdfs:subClassOf None aio:Method \n",
- "\n",
- " object_label \n",
- "660 Method \n",
- "parent aio:Method\n",
- "aio:Metric_Learning\n",
- " subject subject_label predicate predicate_label \\\n",
- "661 aio:Metric_Learning Metric Learning rdfs:subClassOf None \n",
- "662 aio:Metric_Learning Metric Learning rdfs:subClassOf None \n",
- "663 aio:Metric_Learning Metric Learning rdfs:subClassOf None \n",
- "664 aio:Metric_Learning Metric Learning rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "661 aio:ANN Artificial Neural Network \n",
- "662 aio:DNN Deep Neural Network \n",
- "663 aio:Metric_Learning Metric Learning \n",
- "664 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Metric_Learning\n",
- " subject subject_label predicate predicate_label \\\n",
- "661 aio:Metric_Learning Metric Learning rdfs:subClassOf None \n",
- "662 aio:Metric_Learning Metric Learning rdfs:subClassOf None \n",
- "663 aio:Metric_Learning Metric Learning rdfs:subClassOf None \n",
- "664 aio:Metric_Learning Metric Learning rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "661 aio:ANN Artificial Neural Network \n",
- "662 aio:DNN Deep Neural Network \n",
- "663 aio:Metric_Learning Metric Learning \n",
- "664 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Metric_Learning\n",
- " subject subject_label predicate predicate_label \\\n",
- "661 aio:Metric_Learning Metric Learning rdfs:subClassOf None \n",
- "662 aio:Metric_Learning Metric Learning rdfs:subClassOf None \n",
- "663 aio:Metric_Learning Metric Learning rdfs:subClassOf None \n",
- "664 aio:Metric_Learning Metric Learning rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "661 aio:ANN Artificial Neural Network \n",
- "662 aio:DNN Deep Neural Network \n",
- "663 aio:Metric_Learning Metric Learning \n",
- "664 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Metric_Learning\n",
- " subject subject_label predicate predicate_label \\\n",
- "661 aio:Metric_Learning Metric Learning rdfs:subClassOf None \n",
- "662 aio:Metric_Learning Metric Learning rdfs:subClassOf None \n",
- "663 aio:Metric_Learning Metric Learning rdfs:subClassOf None \n",
- "664 aio:Metric_Learning Metric Learning rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "661 aio:ANN Artificial Neural Network \n",
- "662 aio:DNN Deep Neural Network \n",
- "663 aio:Metric_Learning Metric Learning \n",
- "664 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Minimum_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "665 aio:Minimum_Layer Minimum Layer rdfs:subClassOf None \n",
- "666 aio:Minimum_Layer Minimum Layer rdfs:subClassOf None \n",
- "667 aio:Minimum_Layer Minimum Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "665 aio:Layer Layer \n",
- "666 aio:Merging_Layer Merging Layer \n",
- "667 aio:Minimum_Layer Minimum Layer \n",
- "parent aio:Layer\n",
- "aio:Minimum_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "665 aio:Minimum_Layer Minimum Layer rdfs:subClassOf None \n",
- "666 aio:Minimum_Layer Minimum Layer rdfs:subClassOf None \n",
- "667 aio:Minimum_Layer Minimum Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "665 aio:Layer Layer \n",
- "666 aio:Merging_Layer Merging Layer \n",
- "667 aio:Minimum_Layer Minimum Layer \n",
- "parent aio:Layer\n",
- "aio:Minimum_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "665 aio:Minimum_Layer Minimum Layer rdfs:subClassOf None \n",
- "666 aio:Minimum_Layer Minimum Layer rdfs:subClassOf None \n",
- "667 aio:Minimum_Layer Minimum Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "665 aio:Layer Layer \n",
- "666 aio:Merging_Layer Merging Layer \n",
- "667 aio:Minimum_Layer Minimum Layer \n",
- "parent aio:Layer\n",
- "aio:Mode_Confusion_Bias\n",
- " subject subject_label predicate \\\n",
- "668 aio:Mode_Confusion_Bias Mode Confusion Bias rdfs:subClassOf \n",
- "669 aio:Mode_Confusion_Bias Mode Confusion Bias rdfs:subClassOf \n",
- "670 aio:Mode_Confusion_Bias Mode Confusion Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "668 None aio:Bias Bias \n",
- "669 None aio:Individual_Bias Individual Bias \n",
- "670 None aio:Mode_Confusion_Bias Mode Confusion Bias \n",
- "parent aio:Bias\n",
- "aio:Mode_Confusion_Bias\n",
- " subject subject_label predicate \\\n",
- "668 aio:Mode_Confusion_Bias Mode Confusion Bias rdfs:subClassOf \n",
- "669 aio:Mode_Confusion_Bias Mode Confusion Bias rdfs:subClassOf \n",
- "670 aio:Mode_Confusion_Bias Mode Confusion Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "668 None aio:Bias Bias \n",
- "669 None aio:Individual_Bias Individual Bias \n",
- "670 None aio:Mode_Confusion_Bias Mode Confusion Bias \n",
- "parent aio:Bias\n",
- "aio:Mode_Confusion_Bias\n",
- " subject subject_label predicate \\\n",
- "668 aio:Mode_Confusion_Bias Mode Confusion Bias rdfs:subClassOf \n",
- "669 aio:Mode_Confusion_Bias Mode Confusion Bias rdfs:subClassOf \n",
- "670 aio:Mode_Confusion_Bias Mode Confusion Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "668 None aio:Bias Bias \n",
- "669 None aio:Individual_Bias Individual Bias \n",
- "670 None aio:Mode_Confusion_Bias Mode Confusion Bias \n",
- "parent aio:Bias\n",
- "aio:Model_Selection_Bias\n",
- " subject subject_label predicate \\\n",
- "671 aio:Model_Selection_Bias Model Selection Bias rdfs:subClassOf \n",
- "672 aio:Model_Selection_Bias Model Selection Bias rdfs:subClassOf \n",
- "673 aio:Model_Selection_Bias Model Selection Bias rdfs:subClassOf \n",
- "674 aio:Model_Selection_Bias Model Selection Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "671 None aio:Bias Bias \n",
- "672 None aio:Computational_Bias Computational Bias \n",
- "673 None aio:Model_Selection_Bias Model Selection Bias \n",
- "674 None aio:Processing_Bias Processing Bias \n",
- "parent aio:Bias\n",
- "aio:Model_Selection_Bias\n",
- " subject subject_label predicate \\\n",
- "671 aio:Model_Selection_Bias Model Selection Bias rdfs:subClassOf \n",
- "672 aio:Model_Selection_Bias Model Selection Bias rdfs:subClassOf \n",
- "673 aio:Model_Selection_Bias Model Selection Bias rdfs:subClassOf \n",
- "674 aio:Model_Selection_Bias Model Selection Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "671 None aio:Bias Bias \n",
- "672 None aio:Computational_Bias Computational Bias \n",
- "673 None aio:Model_Selection_Bias Model Selection Bias \n",
- "674 None aio:Processing_Bias Processing Bias \n",
- "parent aio:Bias\n",
- "aio:Model_Selection_Bias\n",
- " subject subject_label predicate \\\n",
- "671 aio:Model_Selection_Bias Model Selection Bias rdfs:subClassOf \n",
- "672 aio:Model_Selection_Bias Model Selection Bias rdfs:subClassOf \n",
- "673 aio:Model_Selection_Bias Model Selection Bias rdfs:subClassOf \n",
- "674 aio:Model_Selection_Bias Model Selection Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "671 None aio:Bias Bias \n",
- "672 None aio:Computational_Bias Computational Bias \n",
- "673 None aio:Model_Selection_Bias Model Selection Bias \n",
- "674 None aio:Processing_Bias Processing Bias \n",
- "parent aio:Bias\n",
- "aio:Model_Selection_Bias\n",
- " subject subject_label predicate \\\n",
- "671 aio:Model_Selection_Bias Model Selection Bias rdfs:subClassOf \n",
- "672 aio:Model_Selection_Bias Model Selection Bias rdfs:subClassOf \n",
- "673 aio:Model_Selection_Bias Model Selection Bias rdfs:subClassOf \n",
- "674 aio:Model_Selection_Bias Model Selection Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "671 None aio:Bias Bias \n",
- "672 None aio:Computational_Bias Computational Bias \n",
- "673 None aio:Model_Selection_Bias Model Selection Bias \n",
- "674 None aio:Processing_Bias Processing Bias \n",
- "parent aio:Bias\n",
- "aio:MultiHeadAttention_Layer\n",
- " subject subject_label predicate \\\n",
- "675 aio:MultiHeadAttention_Layer MultiHeadAttention Layer rdfs:subClassOf \n",
- "676 aio:MultiHeadAttention_Layer MultiHeadAttention Layer rdfs:subClassOf \n",
- "677 aio:MultiHeadAttention_Layer MultiHeadAttention Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "675 None aio:Attention_Layer Attention Layer \n",
- "676 None aio:Layer Layer \n",
- "677 None aio:MultiHeadAttention_Layer MultiHeadAttention Layer \n",
- "parent aio:Attention_Layer\n",
- "aio:Attention_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "60 aio:Attention_Layer Attention Layer rdfs:subClassOf None \n",
- "61 aio:Attention_Layer Attention Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "60 aio:Attention_Layer Attention Layer \n",
- "61 aio:Layer Layer \n",
- "parent aio:Attention_Layer\n",
- "aio:MultiHeadAttention_Layer\n",
- " subject subject_label predicate \\\n",
- "675 aio:MultiHeadAttention_Layer MultiHeadAttention Layer rdfs:subClassOf \n",
- "676 aio:MultiHeadAttention_Layer MultiHeadAttention Layer rdfs:subClassOf \n",
- "677 aio:MultiHeadAttention_Layer MultiHeadAttention Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "675 None aio:Attention_Layer Attention Layer \n",
- "676 None aio:Layer Layer \n",
- "677 None aio:MultiHeadAttention_Layer MultiHeadAttention Layer \n",
- "parent aio:Attention_Layer\n",
- "aio:Attention_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "60 aio:Attention_Layer Attention Layer rdfs:subClassOf None \n",
- "61 aio:Attention_Layer Attention Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "60 aio:Attention_Layer Attention Layer \n",
- "61 aio:Layer Layer \n",
- "parent aio:Attention_Layer\n",
- "aio:MultiHeadAttention_Layer\n",
- " subject subject_label predicate \\\n",
- "675 aio:MultiHeadAttention_Layer MultiHeadAttention Layer rdfs:subClassOf \n",
- "676 aio:MultiHeadAttention_Layer MultiHeadAttention Layer rdfs:subClassOf \n",
- "677 aio:MultiHeadAttention_Layer MultiHeadAttention Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "675 None aio:Attention_Layer Attention Layer \n",
- "676 None aio:Layer Layer \n",
- "677 None aio:MultiHeadAttention_Layer MultiHeadAttention Layer \n",
- "parent aio:Attention_Layer\n",
- "aio:Attention_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "60 aio:Attention_Layer Attention Layer rdfs:subClassOf None \n",
- "61 aio:Attention_Layer Attention Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "60 aio:Attention_Layer Attention Layer \n",
- "61 aio:Layer Layer \n",
- "parent aio:Attention_Layer\n",
- "aio:Multiclass_Classification\n",
- " subject subject_label \\\n",
- "678 aio:Multiclass_Classification Multiclass Classification \n",
- "679 aio:Multiclass_Classification Multiclass Classification \n",
- "680 aio:Multiclass_Classification Multiclass Classification \n",
- "681 aio:Multiclass_Classification Multiclass Classification \n",
- "682 aio:Multiclass_Classification Multiclass Classification \n",
- "\n",
- " predicate predicate_label object \\\n",
- "678 rdfs:subClassOf None aio:Classification \n",
- "679 rdfs:subClassOf None aio:Machine_Learning \n",
- "680 rdfs:subClassOf None aio:Method \n",
- "681 rdfs:subClassOf None aio:Multiclass_Classification \n",
- "682 rdfs:subClassOf None aio:Supervised_Learning \n",
- "\n",
- " object_label \n",
- "678 Classification \n",
- "679 Machine Learning \n",
- "680 Method \n",
- "681 Multiclass Classification \n",
- "682 Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Classification\n",
- " subject subject_label predicate predicate_label \\\n",
- "140 aio:Classification Classification rdfs:subClassOf None \n",
- "141 aio:Classification Classification rdfs:subClassOf None \n",
- "142 aio:Classification Classification rdfs:subClassOf None \n",
- "143 aio:Classification Classification rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "140 aio:Classification Classification \n",
- "141 aio:Machine_Learning Machine Learning \n",
- "142 aio:Method Method \n",
- "143 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Multiclass_Classification\n",
- " subject subject_label \\\n",
- "678 aio:Multiclass_Classification Multiclass Classification \n",
- "679 aio:Multiclass_Classification Multiclass Classification \n",
- "680 aio:Multiclass_Classification Multiclass Classification \n",
- "681 aio:Multiclass_Classification Multiclass Classification \n",
- "682 aio:Multiclass_Classification Multiclass Classification \n",
- "\n",
- " predicate predicate_label object \\\n",
- "678 rdfs:subClassOf None aio:Classification \n",
- "679 rdfs:subClassOf None aio:Machine_Learning \n",
- "680 rdfs:subClassOf None aio:Method \n",
- "681 rdfs:subClassOf None aio:Multiclass_Classification \n",
- "682 rdfs:subClassOf None aio:Supervised_Learning \n",
- "\n",
- " object_label \n",
- "678 Classification \n",
- "679 Machine Learning \n",
- "680 Method \n",
- "681 Multiclass Classification \n",
- "682 Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Classification\n",
- " subject subject_label predicate predicate_label \\\n",
- "140 aio:Classification Classification rdfs:subClassOf None \n",
- "141 aio:Classification Classification rdfs:subClassOf None \n",
- "142 aio:Classification Classification rdfs:subClassOf None \n",
- "143 aio:Classification Classification rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "140 aio:Classification Classification \n",
- "141 aio:Machine_Learning Machine Learning \n",
- "142 aio:Method Method \n",
- "143 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Multiclass_Classification\n",
- " subject subject_label \\\n",
- "678 aio:Multiclass_Classification Multiclass Classification \n",
- "679 aio:Multiclass_Classification Multiclass Classification \n",
- "680 aio:Multiclass_Classification Multiclass Classification \n",
- "681 aio:Multiclass_Classification Multiclass Classification \n",
- "682 aio:Multiclass_Classification Multiclass Classification \n",
- "\n",
- " predicate predicate_label object \\\n",
- "678 rdfs:subClassOf None aio:Classification \n",
- "679 rdfs:subClassOf None aio:Machine_Learning \n",
- "680 rdfs:subClassOf None aio:Method \n",
- "681 rdfs:subClassOf None aio:Multiclass_Classification \n",
- "682 rdfs:subClassOf None aio:Supervised_Learning \n",
- "\n",
- " object_label \n",
- "678 Classification \n",
- "679 Machine Learning \n",
- "680 Method \n",
- "681 Multiclass Classification \n",
- "682 Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Classification\n",
- " subject subject_label predicate predicate_label \\\n",
- "140 aio:Classification Classification rdfs:subClassOf None \n",
- "141 aio:Classification Classification rdfs:subClassOf None \n",
- "142 aio:Classification Classification rdfs:subClassOf None \n",
- "143 aio:Classification Classification rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "140 aio:Classification Classification \n",
- "141 aio:Machine_Learning Machine Learning \n",
- "142 aio:Method Method \n",
- "143 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Multiclass_Classification\n",
- " subject subject_label \\\n",
- "678 aio:Multiclass_Classification Multiclass Classification \n",
- "679 aio:Multiclass_Classification Multiclass Classification \n",
- "680 aio:Multiclass_Classification Multiclass Classification \n",
- "681 aio:Multiclass_Classification Multiclass Classification \n",
- "682 aio:Multiclass_Classification Multiclass Classification \n",
- "\n",
- " predicate predicate_label object \\\n",
- "678 rdfs:subClassOf None aio:Classification \n",
- "679 rdfs:subClassOf None aio:Machine_Learning \n",
- "680 rdfs:subClassOf None aio:Method \n",
- "681 rdfs:subClassOf None aio:Multiclass_Classification \n",
- "682 rdfs:subClassOf None aio:Supervised_Learning \n",
- "\n",
- " object_label \n",
- "678 Classification \n",
- "679 Machine Learning \n",
- "680 Method \n",
- "681 Multiclass Classification \n",
- "682 Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Classification\n",
- " subject subject_label predicate predicate_label \\\n",
- "140 aio:Classification Classification rdfs:subClassOf None \n",
- "141 aio:Classification Classification rdfs:subClassOf None \n",
- "142 aio:Classification Classification rdfs:subClassOf None \n",
- "143 aio:Classification Classification rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "140 aio:Classification Classification \n",
- "141 aio:Machine_Learning Machine Learning \n",
- "142 aio:Method Method \n",
- "143 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Multiclass_Classification\n",
- " subject subject_label \\\n",
- "678 aio:Multiclass_Classification Multiclass Classification \n",
- "679 aio:Multiclass_Classification Multiclass Classification \n",
- "680 aio:Multiclass_Classification Multiclass Classification \n",
- "681 aio:Multiclass_Classification Multiclass Classification \n",
- "682 aio:Multiclass_Classification Multiclass Classification \n",
- "\n",
- " predicate predicate_label object \\\n",
- "678 rdfs:subClassOf None aio:Classification \n",
- "679 rdfs:subClassOf None aio:Machine_Learning \n",
- "680 rdfs:subClassOf None aio:Method \n",
- "681 rdfs:subClassOf None aio:Multiclass_Classification \n",
- "682 rdfs:subClassOf None aio:Supervised_Learning \n",
- "\n",
- " object_label \n",
- "678 Classification \n",
- "679 Machine Learning \n",
- "680 Method \n",
- "681 Multiclass Classification \n",
- "682 Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Classification\n",
- " subject subject_label predicate predicate_label \\\n",
- "140 aio:Classification Classification rdfs:subClassOf None \n",
- "141 aio:Classification Classification rdfs:subClassOf None \n",
- "142 aio:Classification Classification rdfs:subClassOf None \n",
- "143 aio:Classification Classification rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "140 aio:Classification Classification \n",
- "141 aio:Machine_Learning Machine Learning \n",
- "142 aio:Method Method \n",
- "143 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Classification\n",
- "aio:Multidimensional_Scaling\n",
- " subject subject_label predicate \\\n",
- "683 aio:Multidimensional_Scaling Multidimensional Scaling rdfs:subClassOf \n",
- "684 aio:Multidimensional_Scaling Multidimensional Scaling rdfs:subClassOf \n",
- "685 aio:Multidimensional_Scaling Multidimensional Scaling rdfs:subClassOf \n",
- "686 aio:Multidimensional_Scaling Multidimensional Scaling rdfs:subClassOf \n",
- "687 aio:Multidimensional_Scaling Multidimensional Scaling rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "683 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "684 None aio:Machine_Learning Machine Learning \n",
- "685 None aio:Method Method \n",
- "686 None aio:Multidimensional_Scaling Multidimensional Scaling \n",
- "687 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Dimensionality_Reduction\n",
- " subject subject_label predicate \\\n",
- "275 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "276 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "277 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "278 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "275 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "276 None aio:Machine_Learning Machine Learning \n",
- "277 None aio:Method Method \n",
- "278 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Multidimensional_Scaling\n",
- " subject subject_label predicate \\\n",
- "683 aio:Multidimensional_Scaling Multidimensional Scaling rdfs:subClassOf \n",
- "684 aio:Multidimensional_Scaling Multidimensional Scaling rdfs:subClassOf \n",
- "685 aio:Multidimensional_Scaling Multidimensional Scaling rdfs:subClassOf \n",
- "686 aio:Multidimensional_Scaling Multidimensional Scaling rdfs:subClassOf \n",
- "687 aio:Multidimensional_Scaling Multidimensional Scaling rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "683 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "684 None aio:Machine_Learning Machine Learning \n",
- "685 None aio:Method Method \n",
- "686 None aio:Multidimensional_Scaling Multidimensional Scaling \n",
- "687 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Dimensionality_Reduction\n",
- " subject subject_label predicate \\\n",
- "275 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "276 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "277 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "278 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "275 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "276 None aio:Machine_Learning Machine Learning \n",
- "277 None aio:Method Method \n",
- "278 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Multidimensional_Scaling\n",
- " subject subject_label predicate \\\n",
- "683 aio:Multidimensional_Scaling Multidimensional Scaling rdfs:subClassOf \n",
- "684 aio:Multidimensional_Scaling Multidimensional Scaling rdfs:subClassOf \n",
- "685 aio:Multidimensional_Scaling Multidimensional Scaling rdfs:subClassOf \n",
- "686 aio:Multidimensional_Scaling Multidimensional Scaling rdfs:subClassOf \n",
- "687 aio:Multidimensional_Scaling Multidimensional Scaling rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "683 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "684 None aio:Machine_Learning Machine Learning \n",
- "685 None aio:Method Method \n",
- "686 None aio:Multidimensional_Scaling Multidimensional Scaling \n",
- "687 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Dimensionality_Reduction\n",
- " subject subject_label predicate \\\n",
- "275 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "276 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "277 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "278 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "275 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "276 None aio:Machine_Learning Machine Learning \n",
- "277 None aio:Method Method \n",
- "278 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Multidimensional_Scaling\n",
- " subject subject_label predicate \\\n",
- "683 aio:Multidimensional_Scaling Multidimensional Scaling rdfs:subClassOf \n",
- "684 aio:Multidimensional_Scaling Multidimensional Scaling rdfs:subClassOf \n",
- "685 aio:Multidimensional_Scaling Multidimensional Scaling rdfs:subClassOf \n",
- "686 aio:Multidimensional_Scaling Multidimensional Scaling rdfs:subClassOf \n",
- "687 aio:Multidimensional_Scaling Multidimensional Scaling rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "683 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "684 None aio:Machine_Learning Machine Learning \n",
- "685 None aio:Method Method \n",
- "686 None aio:Multidimensional_Scaling Multidimensional Scaling \n",
- "687 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Dimensionality_Reduction\n",
- " subject subject_label predicate \\\n",
- "275 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "276 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "277 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "278 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "275 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "276 None aio:Machine_Learning Machine Learning \n",
- "277 None aio:Method Method \n",
- "278 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Multidimensional_Scaling\n",
- " subject subject_label predicate \\\n",
- "683 aio:Multidimensional_Scaling Multidimensional Scaling rdfs:subClassOf \n",
- "684 aio:Multidimensional_Scaling Multidimensional Scaling rdfs:subClassOf \n",
- "685 aio:Multidimensional_Scaling Multidimensional Scaling rdfs:subClassOf \n",
- "686 aio:Multidimensional_Scaling Multidimensional Scaling rdfs:subClassOf \n",
- "687 aio:Multidimensional_Scaling Multidimensional Scaling rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "683 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "684 None aio:Machine_Learning Machine Learning \n",
- "685 None aio:Method Method \n",
- "686 None aio:Multidimensional_Scaling Multidimensional Scaling \n",
- "687 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Dimensionality_Reduction\n",
- " subject subject_label predicate \\\n",
- "275 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "276 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "277 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "278 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "275 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "276 None aio:Machine_Learning Machine Learning \n",
- "277 None aio:Method Method \n",
- "278 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Multimodal_Deep_Learning\n",
- " subject subject_label predicate \\\n",
- "688 aio:Multimodal_Deep_Learning Multimodal Deep Learning rdfs:subClassOf \n",
- "689 aio:Multimodal_Deep_Learning Multimodal Deep Learning rdfs:subClassOf \n",
- "690 aio:Multimodal_Deep_Learning Multimodal Deep Learning rdfs:subClassOf \n",
- "691 aio:Multimodal_Deep_Learning Multimodal Deep Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "688 None aio:ANN Artificial Neural Network \n",
- "689 None aio:DNN Deep Neural Network \n",
- "690 None aio:Multimodal_Deep_Learning Multimodal Deep Learning \n",
- "691 None aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Multimodal_Deep_Learning\n",
- " subject subject_label predicate \\\n",
- "688 aio:Multimodal_Deep_Learning Multimodal Deep Learning rdfs:subClassOf \n",
- "689 aio:Multimodal_Deep_Learning Multimodal Deep Learning rdfs:subClassOf \n",
- "690 aio:Multimodal_Deep_Learning Multimodal Deep Learning rdfs:subClassOf \n",
- "691 aio:Multimodal_Deep_Learning Multimodal Deep Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "688 None aio:ANN Artificial Neural Network \n",
- "689 None aio:DNN Deep Neural Network \n",
- "690 None aio:Multimodal_Deep_Learning Multimodal Deep Learning \n",
- "691 None aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Multimodal_Deep_Learning\n",
- " subject subject_label predicate \\\n",
- "688 aio:Multimodal_Deep_Learning Multimodal Deep Learning rdfs:subClassOf \n",
- "689 aio:Multimodal_Deep_Learning Multimodal Deep Learning rdfs:subClassOf \n",
- "690 aio:Multimodal_Deep_Learning Multimodal Deep Learning rdfs:subClassOf \n",
- "691 aio:Multimodal_Deep_Learning Multimodal Deep Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "688 None aio:ANN Artificial Neural Network \n",
- "689 None aio:DNN Deep Neural Network \n",
- "690 None aio:Multimodal_Deep_Learning Multimodal Deep Learning \n",
- "691 None aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Multimodal_Deep_Learning\n",
- " subject subject_label predicate \\\n",
- "688 aio:Multimodal_Deep_Learning Multimodal Deep Learning rdfs:subClassOf \n",
- "689 aio:Multimodal_Deep_Learning Multimodal Deep Learning rdfs:subClassOf \n",
- "690 aio:Multimodal_Deep_Learning Multimodal Deep Learning rdfs:subClassOf \n",
- "691 aio:Multimodal_Deep_Learning Multimodal Deep Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "688 None aio:ANN Artificial Neural Network \n",
- "689 None aio:DNN Deep Neural Network \n",
- "690 None aio:Multimodal_Deep_Learning Multimodal Deep Learning \n",
- "691 None aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Multimodal_Learning\n",
- " subject subject_label predicate \\\n",
- "692 aio:Multimodal_Learning Multimodal Learning rdfs:subClassOf \n",
- "693 aio:Multimodal_Learning Multimodal Learning rdfs:subClassOf \n",
- "694 aio:Multimodal_Learning Multimodal Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "692 None aio:Machine_Learning Machine Learning \n",
- "693 None aio:Method Method \n",
- "694 None aio:Multimodal_Learning Multimodal Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Multimodal_Learning\n",
- " subject subject_label predicate \\\n",
- "692 aio:Multimodal_Learning Multimodal Learning rdfs:subClassOf \n",
- "693 aio:Multimodal_Learning Multimodal Learning rdfs:subClassOf \n",
- "694 aio:Multimodal_Learning Multimodal Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "692 None aio:Machine_Learning Machine Learning \n",
- "693 None aio:Method Method \n",
- "694 None aio:Multimodal_Learning Multimodal Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Multimodal_Learning\n",
- " subject subject_label predicate \\\n",
- "692 aio:Multimodal_Learning Multimodal Learning rdfs:subClassOf \n",
- "693 aio:Multimodal_Learning Multimodal Learning rdfs:subClassOf \n",
- "694 aio:Multimodal_Learning Multimodal Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "692 None aio:Machine_Learning Machine Learning \n",
- "693 None aio:Method Method \n",
- "694 None aio:Multimodal_Learning Multimodal Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Multiply_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "695 aio:Multiply_Layer Multiply Layer rdfs:subClassOf None \n",
- "696 aio:Multiply_Layer Multiply Layer rdfs:subClassOf None \n",
- "697 aio:Multiply_Layer Multiply Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "695 aio:Layer Layer \n",
- "696 aio:Merging_Layer Merging Layer \n",
- "697 aio:Multiply_Layer Multiply Layer \n",
- "parent aio:Layer\n",
- "aio:Multiply_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "695 aio:Multiply_Layer Multiply Layer rdfs:subClassOf None \n",
- "696 aio:Multiply_Layer Multiply Layer rdfs:subClassOf None \n",
- "697 aio:Multiply_Layer Multiply Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "695 aio:Layer Layer \n",
- "696 aio:Merging_Layer Merging Layer \n",
- "697 aio:Multiply_Layer Multiply Layer \n",
- "parent aio:Layer\n",
- "aio:Multiply_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "695 aio:Multiply_Layer Multiply Layer rdfs:subClassOf None \n",
- "696 aio:Multiply_Layer Multiply Layer rdfs:subClassOf None \n",
- "697 aio:Multiply_Layer Multiply Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "695 aio:Layer Layer \n",
- "696 aio:Merging_Layer Merging Layer \n",
- "697 aio:Multiply_Layer Multiply Layer \n",
- "parent aio:Layer\n",
- "aio:N2V_CBOW\n",
- " subject subject_label predicate predicate_label \\\n",
- "698 aio:N2V_CBOW node2vec-CBOW rdfs:subClassOf None \n",
- "699 aio:N2V_CBOW node2vec-CBOW rdfs:subClassOf None \n",
- "700 aio:N2V_CBOW node2vec-CBOW rdfs:subClassOf None \n",
- "701 aio:N2V_CBOW node2vec-CBOW rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "698 aio:ANN Artificial Neural Network \n",
- "699 aio:N2V_CBOW node2vec-CBOW \n",
- "700 aio:Network Network \n",
- "701 aio:W2V_CBOW word2vec-CBOW \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:N2V_CBOW\n",
- " subject subject_label predicate predicate_label \\\n",
- "698 aio:N2V_CBOW node2vec-CBOW rdfs:subClassOf None \n",
- "699 aio:N2V_CBOW node2vec-CBOW rdfs:subClassOf None \n",
- "700 aio:N2V_CBOW node2vec-CBOW rdfs:subClassOf None \n",
- "701 aio:N2V_CBOW node2vec-CBOW rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "698 aio:ANN Artificial Neural Network \n",
- "699 aio:N2V_CBOW node2vec-CBOW \n",
- "700 aio:Network Network \n",
- "701 aio:W2V_CBOW word2vec-CBOW \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:N2V_CBOW\n",
- " subject subject_label predicate predicate_label \\\n",
- "698 aio:N2V_CBOW node2vec-CBOW rdfs:subClassOf None \n",
- "699 aio:N2V_CBOW node2vec-CBOW rdfs:subClassOf None \n",
- "700 aio:N2V_CBOW node2vec-CBOW rdfs:subClassOf None \n",
- "701 aio:N2V_CBOW node2vec-CBOW rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "698 aio:ANN Artificial Neural Network \n",
- "699 aio:N2V_CBOW node2vec-CBOW \n",
- "700 aio:Network Network \n",
- "701 aio:W2V_CBOW word2vec-CBOW \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:N2V_CBOW\n",
- " subject subject_label predicate predicate_label \\\n",
- "698 aio:N2V_CBOW node2vec-CBOW rdfs:subClassOf None \n",
- "699 aio:N2V_CBOW node2vec-CBOW rdfs:subClassOf None \n",
- "700 aio:N2V_CBOW node2vec-CBOW rdfs:subClassOf None \n",
- "701 aio:N2V_CBOW node2vec-CBOW rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "698 aio:ANN Artificial Neural Network \n",
- "699 aio:N2V_CBOW node2vec-CBOW \n",
- "700 aio:Network Network \n",
- "701 aio:W2V_CBOW word2vec-CBOW \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:N2V_SkipGram\n",
- " subject subject_label predicate predicate_label \\\n",
- "702 aio:N2V_SkipGram node2vec-SkipGram rdfs:subClassOf None \n",
- "703 aio:N2V_SkipGram node2vec-SkipGram rdfs:subClassOf None \n",
- "704 aio:N2V_SkipGram node2vec-SkipGram rdfs:subClassOf None \n",
- "705 aio:N2V_SkipGram node2vec-SkipGram rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "702 aio:ANN Artificial Neural Network \n",
- "703 aio:N2V_SkipGram node2vec-SkipGram \n",
- "704 aio:Network Network \n",
- "705 aio:W2V_SkipGram word2vec-SkipGram \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:N2V_SkipGram\n",
- " subject subject_label predicate predicate_label \\\n",
- "702 aio:N2V_SkipGram node2vec-SkipGram rdfs:subClassOf None \n",
- "703 aio:N2V_SkipGram node2vec-SkipGram rdfs:subClassOf None \n",
- "704 aio:N2V_SkipGram node2vec-SkipGram rdfs:subClassOf None \n",
- "705 aio:N2V_SkipGram node2vec-SkipGram rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "702 aio:ANN Artificial Neural Network \n",
- "703 aio:N2V_SkipGram node2vec-SkipGram \n",
- "704 aio:Network Network \n",
- "705 aio:W2V_SkipGram word2vec-SkipGram \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:N2V_SkipGram\n",
- " subject subject_label predicate predicate_label \\\n",
- "702 aio:N2V_SkipGram node2vec-SkipGram rdfs:subClassOf None \n",
- "703 aio:N2V_SkipGram node2vec-SkipGram rdfs:subClassOf None \n",
- "704 aio:N2V_SkipGram node2vec-SkipGram rdfs:subClassOf None \n",
- "705 aio:N2V_SkipGram node2vec-SkipGram rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "702 aio:ANN Artificial Neural Network \n",
- "703 aio:N2V_SkipGram node2vec-SkipGram \n",
- "704 aio:Network Network \n",
- "705 aio:W2V_SkipGram word2vec-SkipGram \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:N2V_SkipGram\n",
- " subject subject_label predicate predicate_label \\\n",
- "702 aio:N2V_SkipGram node2vec-SkipGram rdfs:subClassOf None \n",
- "703 aio:N2V_SkipGram node2vec-SkipGram rdfs:subClassOf None \n",
- "704 aio:N2V_SkipGram node2vec-SkipGram rdfs:subClassOf None \n",
- "705 aio:N2V_SkipGram node2vec-SkipGram rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "702 aio:ANN Artificial Neural Network \n",
- "703 aio:N2V_SkipGram node2vec-SkipGram \n",
- "704 aio:Network Network \n",
- "705 aio:W2V_SkipGram word2vec-SkipGram \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:NTM\n",
- " subject subject_label predicate predicate_label \\\n",
- "706 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "707 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "708 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "709 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "710 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "711 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "712 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "706 aio:ANN Artificial Neural Network \n",
- "707 aio:DFF Deep FeedFoward \n",
- "708 aio:DNN Deep Neural Network \n",
- "709 aio:LSTM Long Short Term Memory \n",
- "710 aio:NTM Neural Turing Machine Network \n",
- "711 aio:Network Network \n",
- "712 aio:RecNN Recurrent Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:NTM\n",
- " subject subject_label predicate predicate_label \\\n",
- "706 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "707 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "708 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "709 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "710 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "711 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "712 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "706 aio:ANN Artificial Neural Network \n",
- "707 aio:DFF Deep FeedFoward \n",
- "708 aio:DNN Deep Neural Network \n",
- "709 aio:LSTM Long Short Term Memory \n",
- "710 aio:NTM Neural Turing Machine Network \n",
- "711 aio:Network Network \n",
- "712 aio:RecNN Recurrent Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:NTM\n",
- " subject subject_label predicate predicate_label \\\n",
- "706 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "707 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "708 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "709 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "710 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "711 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "712 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "706 aio:ANN Artificial Neural Network \n",
- "707 aio:DFF Deep FeedFoward \n",
- "708 aio:DNN Deep Neural Network \n",
- "709 aio:LSTM Long Short Term Memory \n",
- "710 aio:NTM Neural Turing Machine Network \n",
- "711 aio:Network Network \n",
- "712 aio:RecNN Recurrent Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:NTM\n",
- " subject subject_label predicate predicate_label \\\n",
- "706 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "707 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "708 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "709 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "710 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "711 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "712 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "706 aio:ANN Artificial Neural Network \n",
- "707 aio:DFF Deep FeedFoward \n",
- "708 aio:DNN Deep Neural Network \n",
- "709 aio:LSTM Long Short Term Memory \n",
- "710 aio:NTM Neural Turing Machine Network \n",
- "711 aio:Network Network \n",
- "712 aio:RecNN Recurrent Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:NTM\n",
- " subject subject_label predicate predicate_label \\\n",
- "706 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "707 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "708 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "709 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "710 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "711 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "712 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "706 aio:ANN Artificial Neural Network \n",
- "707 aio:DFF Deep FeedFoward \n",
- "708 aio:DNN Deep Neural Network \n",
- "709 aio:LSTM Long Short Term Memory \n",
- "710 aio:NTM Neural Turing Machine Network \n",
- "711 aio:Network Network \n",
- "712 aio:RecNN Recurrent Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:NTM\n",
- " subject subject_label predicate predicate_label \\\n",
- "706 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "707 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "708 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "709 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "710 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "711 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "712 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "706 aio:ANN Artificial Neural Network \n",
- "707 aio:DFF Deep FeedFoward \n",
- "708 aio:DNN Deep Neural Network \n",
- "709 aio:LSTM Long Short Term Memory \n",
- "710 aio:NTM Neural Turing Machine Network \n",
- "711 aio:Network Network \n",
- "712 aio:RecNN Recurrent Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:NTM\n",
- " subject subject_label predicate predicate_label \\\n",
- "706 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "707 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "708 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "709 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "710 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "711 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "712 aio:NTM Neural Turing Machine Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "706 aio:ANN Artificial Neural Network \n",
- "707 aio:DFF Deep FeedFoward \n",
- "708 aio:DNN Deep Neural Network \n",
- "709 aio:LSTM Long Short Term Memory \n",
- "710 aio:NTM Neural Turing Machine Network \n",
- "711 aio:Network Network \n",
- "712 aio:RecNN Recurrent Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Natural_Language_Processing\n",
- " subject subject_label \\\n",
- "713 aio:Natural_Language_Processing Natural Language Processing \n",
- "714 aio:Natural_Language_Processing Natural Language Processing \n",
- "715 aio:Natural_Language_Processing Natural Language Processing \n",
- "\n",
- " predicate predicate_label object \\\n",
- "713 rdfs:subClassOf None aio:Machine_Learning \n",
- "714 rdfs:subClassOf None aio:Method \n",
- "715 rdfs:subClassOf None aio:Natural_Language_Processing \n",
- "\n",
- " object_label \n",
- "713 Machine Learning \n",
- "714 Method \n",
- "715 Natural Language Processing \n",
- "parent aio:Machine_Learning\n",
- "aio:Natural_Language_Processing\n",
- " subject subject_label \\\n",
- "713 aio:Natural_Language_Processing Natural Language Processing \n",
- "714 aio:Natural_Language_Processing Natural Language Processing \n",
- "715 aio:Natural_Language_Processing Natural Language Processing \n",
- "\n",
- " predicate predicate_label object \\\n",
- "713 rdfs:subClassOf None aio:Machine_Learning \n",
- "714 rdfs:subClassOf None aio:Method \n",
- "715 rdfs:subClassOf None aio:Natural_Language_Processing \n",
- "\n",
- " object_label \n",
- "713 Machine Learning \n",
- "714 Method \n",
- "715 Natural Language Processing \n",
- "parent aio:Machine_Learning\n",
- "aio:Natural_Language_Processing\n",
- " subject subject_label \\\n",
- "713 aio:Natural_Language_Processing Natural Language Processing \n",
- "714 aio:Natural_Language_Processing Natural Language Processing \n",
- "715 aio:Natural_Language_Processing Natural Language Processing \n",
- "\n",
- " predicate predicate_label object \\\n",
- "713 rdfs:subClassOf None aio:Machine_Learning \n",
- "714 rdfs:subClassOf None aio:Method \n",
- "715 rdfs:subClassOf None aio:Natural_Language_Processing \n",
- "\n",
- " object_label \n",
- "713 Machine Learning \n",
- "714 Method \n",
- "715 Natural Language Processing \n",
- "parent aio:Machine_Learning\n",
- "aio:Network\n",
- " subject subject_label predicate predicate_label object \\\n",
- "716 aio:Network Network rdfs:subClassOf None aio:Network \n",
- "\n",
- " object_label \n",
- "716 Network \n",
- "parent aio:Network\n",
- "aio:Noise_Dense_Layer\n",
- " subject subject_label predicate \\\n",
- "717 aio:Noise_Dense_Layer Noise Dense Layer rdfs:subClassOf \n",
- "718 aio:Noise_Dense_Layer Noise Dense Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "717 None aio:Layer Layer \n",
- "718 None aio:Noise_Dense_Layer Noise Dense Layer \n",
- "parent aio:Layer\n",
- "aio:Noise_Dense_Layer\n",
- " subject subject_label predicate \\\n",
- "717 aio:Noise_Dense_Layer Noise Dense Layer rdfs:subClassOf \n",
- "718 aio:Noise_Dense_Layer Noise Dense Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "717 None aio:Layer Layer \n",
- "718 None aio:Noise_Dense_Layer Noise Dense Layer \n",
- "parent aio:Layer\n",
- "aio:Normalization_Layer\n",
- " subject subject_label predicate \\\n",
- "719 aio:Normalization_Layer Normalization Layer rdfs:subClassOf \n",
- "720 aio:Normalization_Layer Normalization Layer rdfs:subClassOf \n",
- "721 aio:Normalization_Layer Normalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "719 None aio:Layer \n",
- "720 None aio:Normalization_Layer \n",
- "721 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "719 Layer \n",
- "720 Normalization Layer \n",
- "721 Numerical Features Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:Normalization_Layer\n",
- " subject subject_label predicate \\\n",
- "719 aio:Normalization_Layer Normalization Layer rdfs:subClassOf \n",
- "720 aio:Normalization_Layer Normalization Layer rdfs:subClassOf \n",
- "721 aio:Normalization_Layer Normalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "719 None aio:Layer \n",
- "720 None aio:Normalization_Layer \n",
- "721 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "719 Layer \n",
- "720 Normalization Layer \n",
- "721 Numerical Features Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:Normalization_Layer\n",
- " subject subject_label predicate \\\n",
- "719 aio:Normalization_Layer Normalization Layer rdfs:subClassOf \n",
- "720 aio:Normalization_Layer Normalization Layer rdfs:subClassOf \n",
- "721 aio:Normalization_Layer Normalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "719 None aio:Layer \n",
- "720 None aio:Normalization_Layer \n",
- "721 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "719 Layer \n",
- "720 Normalization Layer \n",
- "721 Numerical Features Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:Numerical_Features_Preprocessing_Layer\n",
- " subject \\\n",
- "722 aio:Numerical_Features_Preprocessing_Layer \n",
- "723 aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " subject_label predicate predicate_label \\\n",
- "722 Numerical Features Preprocessing Layer rdfs:subClassOf None \n",
- "723 Numerical Features Preprocessing Layer rdfs:subClassOf None \n",
- "\n",
- " object \\\n",
- "722 aio:Layer \n",
- "723 aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "722 Layer \n",
- "723 Numerical Features Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:Numerical_Features_Preprocessing_Layer\n",
- " subject \\\n",
- "722 aio:Numerical_Features_Preprocessing_Layer \n",
- "723 aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " subject_label predicate predicate_label \\\n",
- "722 Numerical Features Preprocessing Layer rdfs:subClassOf None \n",
- "723 Numerical Features Preprocessing Layer rdfs:subClassOf None \n",
- "\n",
- " object \\\n",
- "722 aio:Layer \n",
- "723 aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "722 Layer \n",
- "723 Numerical Features Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:One-shot_Learning\n",
- " subject subject_label predicate \\\n",
- "724 aio:One-shot_Learning One-shot Learning rdfs:subClassOf \n",
- "725 aio:One-shot_Learning One-shot Learning rdfs:subClassOf \n",
- "726 aio:One-shot_Learning One-shot Learning rdfs:subClassOf \n",
- "727 aio:One-shot_Learning One-shot Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "724 None aio:ANN Artificial Neural Network \n",
- "725 None aio:DNN Deep Neural Network \n",
- "726 None aio:Network Network \n",
- "727 None aio:One-shot_Learning One-shot Learning \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:One-shot_Learning\n",
- " subject subject_label predicate \\\n",
- "724 aio:One-shot_Learning One-shot Learning rdfs:subClassOf \n",
- "725 aio:One-shot_Learning One-shot Learning rdfs:subClassOf \n",
- "726 aio:One-shot_Learning One-shot Learning rdfs:subClassOf \n",
- "727 aio:One-shot_Learning One-shot Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "724 None aio:ANN Artificial Neural Network \n",
- "725 None aio:DNN Deep Neural Network \n",
- "726 None aio:Network Network \n",
- "727 None aio:One-shot_Learning One-shot Learning \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:One-shot_Learning\n",
- " subject subject_label predicate \\\n",
- "724 aio:One-shot_Learning One-shot Learning rdfs:subClassOf \n",
- "725 aio:One-shot_Learning One-shot Learning rdfs:subClassOf \n",
- "726 aio:One-shot_Learning One-shot Learning rdfs:subClassOf \n",
- "727 aio:One-shot_Learning One-shot Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "724 None aio:ANN Artificial Neural Network \n",
- "725 None aio:DNN Deep Neural Network \n",
- "726 None aio:Network Network \n",
- "727 None aio:One-shot_Learning One-shot Learning \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:One-shot_Learning\n",
- " subject subject_label predicate \\\n",
- "724 aio:One-shot_Learning One-shot Learning rdfs:subClassOf \n",
- "725 aio:One-shot_Learning One-shot Learning rdfs:subClassOf \n",
- "726 aio:One-shot_Learning One-shot Learning rdfs:subClassOf \n",
- "727 aio:One-shot_Learning One-shot Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "724 None aio:ANN Artificial Neural Network \n",
- "725 None aio:DNN Deep Neural Network \n",
- "726 None aio:Network Network \n",
- "727 None aio:One-shot_Learning One-shot Learning \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Output_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "728 aio:Output_Layer Output Layer rdfs:subClassOf None \n",
- "729 aio:Output_Layer Output Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "728 aio:Layer Layer \n",
- "729 aio:Output_Layer Output Layer \n",
- "parent aio:Layer\n",
- "aio:Output_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "728 aio:Output_Layer Output Layer rdfs:subClassOf None \n",
- "729 aio:Output_Layer Output Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "728 aio:Layer Layer \n",
- "729 aio:Output_Layer Output Layer \n",
- "parent aio:Layer\n",
- "aio:P\n",
- " subject subject_label predicate predicate_label object \\\n",
- "730 aio:P Perceptron rdfs:subClassOf None aio:ANN \n",
- "731 aio:P Perceptron rdfs:subClassOf None aio:Network \n",
- "732 aio:P Perceptron rdfs:subClassOf None aio:P \n",
- "\n",
- " object_label \n",
- "730 Artificial Neural Network \n",
- "731 Network \n",
- "732 Perceptron \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:P\n",
- " subject subject_label predicate predicate_label object \\\n",
- "730 aio:P Perceptron rdfs:subClassOf None aio:ANN \n",
- "731 aio:P Perceptron rdfs:subClassOf None aio:Network \n",
- "732 aio:P Perceptron rdfs:subClassOf None aio:P \n",
- "\n",
- " object_label \n",
- "730 Artificial Neural Network \n",
- "731 Network \n",
- "732 Perceptron \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:P\n",
- " subject subject_label predicate predicate_label object \\\n",
- "730 aio:P Perceptron rdfs:subClassOf None aio:ANN \n",
- "731 aio:P Perceptron rdfs:subClassOf None aio:Network \n",
- "732 aio:P Perceptron rdfs:subClassOf None aio:P \n",
- "\n",
- " object_label \n",
- "730 Artificial Neural Network \n",
- "731 Network \n",
- "732 Perceptron \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:PReLU_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "733 aio:PReLU_Layer PReLU Layer rdfs:subClassOf None \n",
- "734 aio:PReLU_Layer PReLU Layer rdfs:subClassOf None \n",
- "735 aio:PReLU_Layer PReLU Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "733 aio:Activation_Layer Activation Layer \n",
- "734 aio:Layer Layer \n",
- "735 aio:PReLU_Layer PReLU Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:Activation_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "7 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "8 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "7 aio:Activation_Layer Activation Layer \n",
- "8 aio:Layer Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:PReLU_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "733 aio:PReLU_Layer PReLU Layer rdfs:subClassOf None \n",
- "734 aio:PReLU_Layer PReLU Layer rdfs:subClassOf None \n",
- "735 aio:PReLU_Layer PReLU Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "733 aio:Activation_Layer Activation Layer \n",
- "734 aio:Layer Layer \n",
- "735 aio:PReLU_Layer PReLU Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:Activation_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "7 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "8 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "7 aio:Activation_Layer Activation Layer \n",
- "8 aio:Layer Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:PReLU_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "733 aio:PReLU_Layer PReLU Layer rdfs:subClassOf None \n",
- "734 aio:PReLU_Layer PReLU Layer rdfs:subClassOf None \n",
- "735 aio:PReLU_Layer PReLU Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "733 aio:Activation_Layer Activation Layer \n",
- "734 aio:Layer Layer \n",
- "735 aio:PReLU_Layer PReLU Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:Activation_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "7 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "8 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "7 aio:Activation_Layer Activation Layer \n",
- "8 aio:Layer Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:Permute_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "736 aio:Permute_Layer Permute Layer rdfs:subClassOf None \n",
- "737 aio:Permute_Layer Permute Layer rdfs:subClassOf None \n",
- "738 aio:Permute_Layer Permute Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "736 aio:Layer Layer \n",
- "737 aio:Permute_Layer Permute Layer \n",
- "738 aio:Reshaping_Layer Reshaping Layer \n",
- "parent aio:Layer\n",
- "aio:Permute_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "736 aio:Permute_Layer Permute Layer rdfs:subClassOf None \n",
- "737 aio:Permute_Layer Permute Layer rdfs:subClassOf None \n",
- "738 aio:Permute_Layer Permute Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "736 aio:Layer Layer \n",
- "737 aio:Permute_Layer Permute Layer \n",
- "738 aio:Reshaping_Layer Reshaping Layer \n",
- "parent aio:Layer\n",
- "aio:Permute_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "736 aio:Permute_Layer Permute Layer rdfs:subClassOf None \n",
- "737 aio:Permute_Layer Permute Layer rdfs:subClassOf None \n",
- "738 aio:Permute_Layer Permute Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "736 aio:Layer Layer \n",
- "737 aio:Permute_Layer Permute Layer \n",
- "738 aio:Reshaping_Layer Reshaping Layer \n",
- "parent aio:Layer\n",
- "aio:Pooling_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "739 aio:Pooling_Layer Pooling Layer rdfs:subClassOf None \n",
- "740 aio:Pooling_Layer Pooling Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "739 aio:Layer Layer \n",
- "740 aio:Pooling_Layer Pooling Layer \n",
- "parent aio:Layer\n",
- "aio:Pooling_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "739 aio:Pooling_Layer Pooling Layer rdfs:subClassOf None \n",
- "740 aio:Pooling_Layer Pooling Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "739 aio:Layer Layer \n",
- "740 aio:Pooling_Layer Pooling Layer \n",
- "parent aio:Layer\n",
- "aio:Popularity_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "741 aio:Popularity_Bias Popularity Bias rdfs:subClassOf None \n",
- "742 aio:Popularity_Bias Popularity Bias rdfs:subClassOf None \n",
- "743 aio:Popularity_Bias Popularity Bias rdfs:subClassOf None \n",
- "744 aio:Popularity_Bias Popularity Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "741 aio:Bias Bias \n",
- "742 aio:Computational_Bias Computational Bias \n",
- "743 aio:Popularity_Bias Popularity Bias \n",
- "744 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Popularity_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "741 aio:Popularity_Bias Popularity Bias rdfs:subClassOf None \n",
- "742 aio:Popularity_Bias Popularity Bias rdfs:subClassOf None \n",
- "743 aio:Popularity_Bias Popularity Bias rdfs:subClassOf None \n",
- "744 aio:Popularity_Bias Popularity Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "741 aio:Bias Bias \n",
- "742 aio:Computational_Bias Computational Bias \n",
- "743 aio:Popularity_Bias Popularity Bias \n",
- "744 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Popularity_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "741 aio:Popularity_Bias Popularity Bias rdfs:subClassOf None \n",
- "742 aio:Popularity_Bias Popularity Bias rdfs:subClassOf None \n",
- "743 aio:Popularity_Bias Popularity Bias rdfs:subClassOf None \n",
- "744 aio:Popularity_Bias Popularity Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "741 aio:Bias Bias \n",
- "742 aio:Computational_Bias Computational Bias \n",
- "743 aio:Popularity_Bias Popularity Bias \n",
- "744 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Popularity_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "741 aio:Popularity_Bias Popularity Bias rdfs:subClassOf None \n",
- "742 aio:Popularity_Bias Popularity Bias rdfs:subClassOf None \n",
- "743 aio:Popularity_Bias Popularity Bias rdfs:subClassOf None \n",
- "744 aio:Popularity_Bias Popularity Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "741 aio:Bias Bias \n",
- "742 aio:Computational_Bias Computational Bias \n",
- "743 aio:Popularity_Bias Popularity Bias \n",
- "744 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Population_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "745 aio:Population_Bias Population Bias rdfs:subClassOf None \n",
- "746 aio:Population_Bias Population Bias rdfs:subClassOf None \n",
- "747 aio:Population_Bias Population Bias rdfs:subClassOf None \n",
- "748 aio:Population_Bias Population Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "745 aio:Bias Bias \n",
- "746 aio:Computational_Bias Computational Bias \n",
- "747 aio:Population_Bias Population Bias \n",
- "748 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Population_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "745 aio:Population_Bias Population Bias rdfs:subClassOf None \n",
- "746 aio:Population_Bias Population Bias rdfs:subClassOf None \n",
- "747 aio:Population_Bias Population Bias rdfs:subClassOf None \n",
- "748 aio:Population_Bias Population Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "745 aio:Bias Bias \n",
- "746 aio:Computational_Bias Computational Bias \n",
- "747 aio:Population_Bias Population Bias \n",
- "748 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Population_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "745 aio:Population_Bias Population Bias rdfs:subClassOf None \n",
- "746 aio:Population_Bias Population Bias rdfs:subClassOf None \n",
- "747 aio:Population_Bias Population Bias rdfs:subClassOf None \n",
- "748 aio:Population_Bias Population Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "745 aio:Bias Bias \n",
- "746 aio:Computational_Bias Computational Bias \n",
- "747 aio:Population_Bias Population Bias \n",
- "748 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Population_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "745 aio:Population_Bias Population Bias rdfs:subClassOf None \n",
- "746 aio:Population_Bias Population Bias rdfs:subClassOf None \n",
- "747 aio:Population_Bias Population Bias rdfs:subClassOf None \n",
- "748 aio:Population_Bias Population Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "745 aio:Bias Bias \n",
- "746 aio:Computational_Bias Computational Bias \n",
- "747 aio:Population_Bias Population Bias \n",
- "748 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Preprocessing_Layer\n",
- " subject subject_label predicate \\\n",
- "749 aio:Preprocessing_Layer Preprocessing Layer rdfs:subClassOf \n",
- "750 aio:Preprocessing_Layer Preprocessing Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "749 None aio:Layer Layer \n",
- "750 None aio:Preprocessing_Layer Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:Preprocessing_Layer\n",
- " subject subject_label predicate \\\n",
- "749 aio:Preprocessing_Layer Preprocessing Layer rdfs:subClassOf \n",
- "750 aio:Preprocessing_Layer Preprocessing Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "749 None aio:Layer Layer \n",
- "750 None aio:Preprocessing_Layer Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:Presentation_Bias\n",
- " subject subject_label predicate \\\n",
- "751 aio:Presentation_Bias Presentation Bias rdfs:subClassOf \n",
- "752 aio:Presentation_Bias Presentation Bias rdfs:subClassOf \n",
- "753 aio:Presentation_Bias Presentation Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "751 None aio:Bias Bias \n",
- "752 None aio:Individual_Bias Individual Bias \n",
- "753 None aio:Presentation_Bias Presentation Bias \n",
- "parent aio:Bias\n",
- "aio:Presentation_Bias\n",
- " subject subject_label predicate \\\n",
- "751 aio:Presentation_Bias Presentation Bias rdfs:subClassOf \n",
- "752 aio:Presentation_Bias Presentation Bias rdfs:subClassOf \n",
- "753 aio:Presentation_Bias Presentation Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "751 None aio:Bias Bias \n",
- "752 None aio:Individual_Bias Individual Bias \n",
- "753 None aio:Presentation_Bias Presentation Bias \n",
- "parent aio:Bias\n",
- "aio:Presentation_Bias\n",
- " subject subject_label predicate \\\n",
- "751 aio:Presentation_Bias Presentation Bias rdfs:subClassOf \n",
- "752 aio:Presentation_Bias Presentation Bias rdfs:subClassOf \n",
- "753 aio:Presentation_Bias Presentation Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "751 None aio:Bias Bias \n",
- "752 None aio:Individual_Bias Individual Bias \n",
- "753 None aio:Presentation_Bias Presentation Bias \n",
- "parent aio:Bias\n",
- "aio:Principal_Component_Analysis\n",
- " subject subject_label \\\n",
- "754 aio:Principal_Component_Analysis Principal Component Analysis \n",
- "755 aio:Principal_Component_Analysis Principal Component Analysis \n",
- "756 aio:Principal_Component_Analysis Principal Component Analysis \n",
- "757 aio:Principal_Component_Analysis Principal Component Analysis \n",
- "758 aio:Principal_Component_Analysis Principal Component Analysis \n",
- "\n",
- " predicate predicate_label object \\\n",
- "754 rdfs:subClassOf None aio:Dimensionality_Reduction \n",
- "755 rdfs:subClassOf None aio:Machine_Learning \n",
- "756 rdfs:subClassOf None aio:Method \n",
- "757 rdfs:subClassOf None aio:Principal_Component_Analysis \n",
- "758 rdfs:subClassOf None aio:Unsupervised_Learning \n",
- "\n",
- " object_label \n",
- "754 Dimensionality Reduction \n",
- "755 Machine Learning \n",
- "756 Method \n",
- "757 Principal Component Analysis \n",
- "758 Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Dimensionality_Reduction\n",
- " subject subject_label predicate \\\n",
- "275 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "276 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "277 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "278 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "275 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "276 None aio:Machine_Learning Machine Learning \n",
- "277 None aio:Method Method \n",
- "278 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Principal_Component_Analysis\n",
- " subject subject_label \\\n",
- "754 aio:Principal_Component_Analysis Principal Component Analysis \n",
- "755 aio:Principal_Component_Analysis Principal Component Analysis \n",
- "756 aio:Principal_Component_Analysis Principal Component Analysis \n",
- "757 aio:Principal_Component_Analysis Principal Component Analysis \n",
- "758 aio:Principal_Component_Analysis Principal Component Analysis \n",
- "\n",
- " predicate predicate_label object \\\n",
- "754 rdfs:subClassOf None aio:Dimensionality_Reduction \n",
- "755 rdfs:subClassOf None aio:Machine_Learning \n",
- "756 rdfs:subClassOf None aio:Method \n",
- "757 rdfs:subClassOf None aio:Principal_Component_Analysis \n",
- "758 rdfs:subClassOf None aio:Unsupervised_Learning \n",
- "\n",
- " object_label \n",
- "754 Dimensionality Reduction \n",
- "755 Machine Learning \n",
- "756 Method \n",
- "757 Principal Component Analysis \n",
- "758 Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Dimensionality_Reduction\n",
- " subject subject_label predicate \\\n",
- "275 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "276 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "277 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "278 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "275 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "276 None aio:Machine_Learning Machine Learning \n",
- "277 None aio:Method Method \n",
- "278 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Principal_Component_Analysis\n",
- " subject subject_label \\\n",
- "754 aio:Principal_Component_Analysis Principal Component Analysis \n",
- "755 aio:Principal_Component_Analysis Principal Component Analysis \n",
- "756 aio:Principal_Component_Analysis Principal Component Analysis \n",
- "757 aio:Principal_Component_Analysis Principal Component Analysis \n",
- "758 aio:Principal_Component_Analysis Principal Component Analysis \n",
- "\n",
- " predicate predicate_label object \\\n",
- "754 rdfs:subClassOf None aio:Dimensionality_Reduction \n",
- "755 rdfs:subClassOf None aio:Machine_Learning \n",
- "756 rdfs:subClassOf None aio:Method \n",
- "757 rdfs:subClassOf None aio:Principal_Component_Analysis \n",
- "758 rdfs:subClassOf None aio:Unsupervised_Learning \n",
- "\n",
- " object_label \n",
- "754 Dimensionality Reduction \n",
- "755 Machine Learning \n",
- "756 Method \n",
- "757 Principal Component Analysis \n",
- "758 Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Dimensionality_Reduction\n",
- " subject subject_label predicate \\\n",
- "275 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "276 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "277 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "278 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "275 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "276 None aio:Machine_Learning Machine Learning \n",
- "277 None aio:Method Method \n",
- "278 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Principal_Component_Analysis\n",
- " subject subject_label \\\n",
- "754 aio:Principal_Component_Analysis Principal Component Analysis \n",
- "755 aio:Principal_Component_Analysis Principal Component Analysis \n",
- "756 aio:Principal_Component_Analysis Principal Component Analysis \n",
- "757 aio:Principal_Component_Analysis Principal Component Analysis \n",
- "758 aio:Principal_Component_Analysis Principal Component Analysis \n",
- "\n",
- " predicate predicate_label object \\\n",
- "754 rdfs:subClassOf None aio:Dimensionality_Reduction \n",
- "755 rdfs:subClassOf None aio:Machine_Learning \n",
- "756 rdfs:subClassOf None aio:Method \n",
- "757 rdfs:subClassOf None aio:Principal_Component_Analysis \n",
- "758 rdfs:subClassOf None aio:Unsupervised_Learning \n",
- "\n",
- " object_label \n",
- "754 Dimensionality Reduction \n",
- "755 Machine Learning \n",
- "756 Method \n",
- "757 Principal Component Analysis \n",
- "758 Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Dimensionality_Reduction\n",
- " subject subject_label predicate \\\n",
- "275 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "276 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "277 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "278 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "275 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "276 None aio:Machine_Learning Machine Learning \n",
- "277 None aio:Method Method \n",
- "278 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Principal_Component_Analysis\n",
- " subject subject_label \\\n",
- "754 aio:Principal_Component_Analysis Principal Component Analysis \n",
- "755 aio:Principal_Component_Analysis Principal Component Analysis \n",
- "756 aio:Principal_Component_Analysis Principal Component Analysis \n",
- "757 aio:Principal_Component_Analysis Principal Component Analysis \n",
- "758 aio:Principal_Component_Analysis Principal Component Analysis \n",
- "\n",
- " predicate predicate_label object \\\n",
- "754 rdfs:subClassOf None aio:Dimensionality_Reduction \n",
- "755 rdfs:subClassOf None aio:Machine_Learning \n",
- "756 rdfs:subClassOf None aio:Method \n",
- "757 rdfs:subClassOf None aio:Principal_Component_Analysis \n",
- "758 rdfs:subClassOf None aio:Unsupervised_Learning \n",
- "\n",
- " object_label \n",
- "754 Dimensionality Reduction \n",
- "755 Machine Learning \n",
- "756 Method \n",
- "757 Principal Component Analysis \n",
- "758 Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Dimensionality_Reduction\n",
- " subject subject_label predicate \\\n",
- "275 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "276 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "277 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "278 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "275 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "276 None aio:Machine_Learning Machine Learning \n",
- "277 None aio:Method Method \n",
- "278 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Probabilistic_Graphical_Model\n",
- " subject subject_label \\\n",
- "759 aio:Probabilistic_Graphical_Model Probabilistic Graphical Model \n",
- "760 aio:Probabilistic_Graphical_Model Probabilistic Graphical Model \n",
- "761 aio:Probabilistic_Graphical_Model Probabilistic Graphical Model \n",
- "\n",
- " predicate predicate_label object \\\n",
- "759 rdfs:subClassOf None aio:Machine_Learning \n",
- "760 rdfs:subClassOf None aio:Method \n",
- "761 rdfs:subClassOf None aio:Probabilistic_Graphical_Model \n",
- "\n",
- " object_label \n",
- "759 Machine Learning \n",
- "760 Method \n",
- "761 Probabilistic Graphical Model \n",
- "parent aio:Machine_Learning\n",
- "aio:Probabilistic_Graphical_Model\n",
- " subject subject_label \\\n",
- "759 aio:Probabilistic_Graphical_Model Probabilistic Graphical Model \n",
- "760 aio:Probabilistic_Graphical_Model Probabilistic Graphical Model \n",
- "761 aio:Probabilistic_Graphical_Model Probabilistic Graphical Model \n",
- "\n",
- " predicate predicate_label object \\\n",
- "759 rdfs:subClassOf None aio:Machine_Learning \n",
- "760 rdfs:subClassOf None aio:Method \n",
- "761 rdfs:subClassOf None aio:Probabilistic_Graphical_Model \n",
- "\n",
- " object_label \n",
- "759 Machine Learning \n",
- "760 Method \n",
- "761 Probabilistic Graphical Model \n",
- "parent aio:Machine_Learning\n",
- "aio:Probabilistic_Graphical_Model\n",
- " subject subject_label \\\n",
- "759 aio:Probabilistic_Graphical_Model Probabilistic Graphical Model \n",
- "760 aio:Probabilistic_Graphical_Model Probabilistic Graphical Model \n",
- "761 aio:Probabilistic_Graphical_Model Probabilistic Graphical Model \n",
- "\n",
- " predicate predicate_label object \\\n",
- "759 rdfs:subClassOf None aio:Machine_Learning \n",
- "760 rdfs:subClassOf None aio:Method \n",
- "761 rdfs:subClassOf None aio:Probabilistic_Graphical_Model \n",
- "\n",
- " object_label \n",
- "759 Machine Learning \n",
- "760 Method \n",
- "761 Probabilistic Graphical Model \n",
- "parent aio:Machine_Learning\n",
- "aio:Probabilistic_Topic_Model\n",
- " subject subject_label \\\n",
- "762 aio:Probabilistic_Topic_Model Probabilistic Topic Model \n",
- "763 aio:Probabilistic_Topic_Model Probabilistic Topic Model \n",
- "764 aio:Probabilistic_Topic_Model Probabilistic Topic Model \n",
- "765 aio:Probabilistic_Topic_Model Probabilistic Topic Model \n",
- "\n",
- " predicate predicate_label object \\\n",
- "762 rdfs:subClassOf None aio:Machine_Learning \n",
- "763 rdfs:subClassOf None aio:Method \n",
- "764 rdfs:subClassOf None aio:Probabilistic_Graphical_Model \n",
- "765 rdfs:subClassOf None aio:Probabilistic_Topic_Model \n",
- "\n",
- " object_label \n",
- "762 Machine Learning \n",
- "763 Method \n",
- "764 Probabilistic Graphical Model \n",
- "765 Probabilistic Topic Model \n",
- "parent aio:Machine_Learning\n",
- "aio:Probabilistic_Topic_Model\n",
- " subject subject_label \\\n",
- "762 aio:Probabilistic_Topic_Model Probabilistic Topic Model \n",
- "763 aio:Probabilistic_Topic_Model Probabilistic Topic Model \n",
- "764 aio:Probabilistic_Topic_Model Probabilistic Topic Model \n",
- "765 aio:Probabilistic_Topic_Model Probabilistic Topic Model \n",
- "\n",
- " predicate predicate_label object \\\n",
- "762 rdfs:subClassOf None aio:Machine_Learning \n",
- "763 rdfs:subClassOf None aio:Method \n",
- "764 rdfs:subClassOf None aio:Probabilistic_Graphical_Model \n",
- "765 rdfs:subClassOf None aio:Probabilistic_Topic_Model \n",
- "\n",
- " object_label \n",
- "762 Machine Learning \n",
- "763 Method \n",
- "764 Probabilistic Graphical Model \n",
- "765 Probabilistic Topic Model \n",
- "parent aio:Machine_Learning\n",
- "aio:Probabilistic_Topic_Model\n",
- " subject subject_label \\\n",
- "762 aio:Probabilistic_Topic_Model Probabilistic Topic Model \n",
- "763 aio:Probabilistic_Topic_Model Probabilistic Topic Model \n",
- "764 aio:Probabilistic_Topic_Model Probabilistic Topic Model \n",
- "765 aio:Probabilistic_Topic_Model Probabilistic Topic Model \n",
- "\n",
- " predicate predicate_label object \\\n",
- "762 rdfs:subClassOf None aio:Machine_Learning \n",
- "763 rdfs:subClassOf None aio:Method \n",
- "764 rdfs:subClassOf None aio:Probabilistic_Graphical_Model \n",
- "765 rdfs:subClassOf None aio:Probabilistic_Topic_Model \n",
- "\n",
- " object_label \n",
- "762 Machine Learning \n",
- "763 Method \n",
- "764 Probabilistic Graphical Model \n",
- "765 Probabilistic Topic Model \n",
- "parent aio:Machine_Learning\n",
- "aio:Probabilistic_Topic_Model\n",
- " subject subject_label \\\n",
- "762 aio:Probabilistic_Topic_Model Probabilistic Topic Model \n",
- "763 aio:Probabilistic_Topic_Model Probabilistic Topic Model \n",
- "764 aio:Probabilistic_Topic_Model Probabilistic Topic Model \n",
- "765 aio:Probabilistic_Topic_Model Probabilistic Topic Model \n",
- "\n",
- " predicate predicate_label object \\\n",
- "762 rdfs:subClassOf None aio:Machine_Learning \n",
- "763 rdfs:subClassOf None aio:Method \n",
- "764 rdfs:subClassOf None aio:Probabilistic_Graphical_Model \n",
- "765 rdfs:subClassOf None aio:Probabilistic_Topic_Model \n",
- "\n",
- " object_label \n",
- "762 Machine Learning \n",
- "763 Method \n",
- "764 Probabilistic Graphical Model \n",
- "765 Probabilistic Topic Model \n",
- "parent aio:Machine_Learning\n",
- "aio:Processing_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "766 aio:Processing_Bias Processing Bias rdfs:subClassOf None \n",
- "767 aio:Processing_Bias Processing Bias rdfs:subClassOf None \n",
- "768 aio:Processing_Bias Processing Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "766 aio:Bias Bias \n",
- "767 aio:Computational_Bias Computational Bias \n",
- "768 aio:Processing_Bias Processing Bias \n",
- "parent aio:Bias\n",
- "aio:Processing_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "766 aio:Processing_Bias Processing Bias rdfs:subClassOf None \n",
- "767 aio:Processing_Bias Processing Bias rdfs:subClassOf None \n",
- "768 aio:Processing_Bias Processing Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "766 aio:Bias Bias \n",
- "767 aio:Computational_Bias Computational Bias \n",
- "768 aio:Processing_Bias Processing Bias \n",
- "parent aio:Bias\n",
- "aio:Processing_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "766 aio:Processing_Bias Processing Bias rdfs:subClassOf None \n",
- "767 aio:Processing_Bias Processing Bias rdfs:subClassOf None \n",
- "768 aio:Processing_Bias Processing Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "766 aio:Bias Bias \n",
- "767 aio:Computational_Bias Computational Bias \n",
- "768 aio:Processing_Bias Processing Bias \n",
- "parent aio:Bias\n",
- "aio:Proportional_Hazards_Model\n",
- " subject subject_label \\\n",
- "769 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "770 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "771 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "772 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "773 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "774 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "\n",
- " predicate predicate_label object \\\n",
- "769 rdfs:subClassOf None aio:Machine_Learning \n",
- "770 rdfs:subClassOf None aio:Method \n",
- "771 rdfs:subClassOf None aio:Proportional_Hazards_Model \n",
- "772 rdfs:subClassOf None aio:Regression_Analysis \n",
- "773 rdfs:subClassOf None aio:Supervised_Learning \n",
- "774 rdfs:subClassOf None aio:Survival_Analysis \n",
- "\n",
- " object_label \n",
- "769 Machine Learning \n",
- "770 Method \n",
- "771 Proportional Hazards Model \n",
- "772 Regression Analysis \n",
- "773 Supervised Learning \n",
- "774 Survival Analysis \n",
- "parent aio:Machine_Learning\n",
- "aio:Proportional_Hazards_Model\n",
- " subject subject_label \\\n",
- "769 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "770 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "771 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "772 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "773 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "774 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "\n",
- " predicate predicate_label object \\\n",
- "769 rdfs:subClassOf None aio:Machine_Learning \n",
- "770 rdfs:subClassOf None aio:Method \n",
- "771 rdfs:subClassOf None aio:Proportional_Hazards_Model \n",
- "772 rdfs:subClassOf None aio:Regression_Analysis \n",
- "773 rdfs:subClassOf None aio:Supervised_Learning \n",
- "774 rdfs:subClassOf None aio:Survival_Analysis \n",
- "\n",
- " object_label \n",
- "769 Machine Learning \n",
- "770 Method \n",
- "771 Proportional Hazards Model \n",
- "772 Regression Analysis \n",
- "773 Supervised Learning \n",
- "774 Survival Analysis \n",
- "parent aio:Machine_Learning\n",
- "aio:Proportional_Hazards_Model\n",
- " subject subject_label \\\n",
- "769 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "770 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "771 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "772 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "773 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "774 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "\n",
- " predicate predicate_label object \\\n",
- "769 rdfs:subClassOf None aio:Machine_Learning \n",
- "770 rdfs:subClassOf None aio:Method \n",
- "771 rdfs:subClassOf None aio:Proportional_Hazards_Model \n",
- "772 rdfs:subClassOf None aio:Regression_Analysis \n",
- "773 rdfs:subClassOf None aio:Supervised_Learning \n",
- "774 rdfs:subClassOf None aio:Survival_Analysis \n",
- "\n",
- " object_label \n",
- "769 Machine Learning \n",
- "770 Method \n",
- "771 Proportional Hazards Model \n",
- "772 Regression Analysis \n",
- "773 Supervised Learning \n",
- "774 Survival Analysis \n",
- "parent aio:Machine_Learning\n",
- "aio:Proportional_Hazards_Model\n",
- " subject subject_label \\\n",
- "769 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "770 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "771 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "772 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "773 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "774 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "\n",
- " predicate predicate_label object \\\n",
- "769 rdfs:subClassOf None aio:Machine_Learning \n",
- "770 rdfs:subClassOf None aio:Method \n",
- "771 rdfs:subClassOf None aio:Proportional_Hazards_Model \n",
- "772 rdfs:subClassOf None aio:Regression_Analysis \n",
- "773 rdfs:subClassOf None aio:Supervised_Learning \n",
- "774 rdfs:subClassOf None aio:Survival_Analysis \n",
- "\n",
- " object_label \n",
- "769 Machine Learning \n",
- "770 Method \n",
- "771 Proportional Hazards Model \n",
- "772 Regression Analysis \n",
- "773 Supervised Learning \n",
- "774 Survival Analysis \n",
- "parent aio:Machine_Learning\n",
- "aio:Proportional_Hazards_Model\n",
- " subject subject_label \\\n",
- "769 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "770 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "771 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "772 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "773 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "774 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "\n",
- " predicate predicate_label object \\\n",
- "769 rdfs:subClassOf None aio:Machine_Learning \n",
- "770 rdfs:subClassOf None aio:Method \n",
- "771 rdfs:subClassOf None aio:Proportional_Hazards_Model \n",
- "772 rdfs:subClassOf None aio:Regression_Analysis \n",
- "773 rdfs:subClassOf None aio:Supervised_Learning \n",
- "774 rdfs:subClassOf None aio:Survival_Analysis \n",
- "\n",
- " object_label \n",
- "769 Machine Learning \n",
- "770 Method \n",
- "771 Proportional Hazards Model \n",
- "772 Regression Analysis \n",
- "773 Supervised Learning \n",
- "774 Survival Analysis \n",
- "parent aio:Machine_Learning\n",
- "aio:Proportional_Hazards_Model\n",
- " subject subject_label \\\n",
- "769 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "770 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "771 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "772 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "773 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "774 aio:Proportional_Hazards_Model Proportional Hazards Model \n",
- "\n",
- " predicate predicate_label object \\\n",
- "769 rdfs:subClassOf None aio:Machine_Learning \n",
- "770 rdfs:subClassOf None aio:Method \n",
- "771 rdfs:subClassOf None aio:Proportional_Hazards_Model \n",
- "772 rdfs:subClassOf None aio:Regression_Analysis \n",
- "773 rdfs:subClassOf None aio:Supervised_Learning \n",
- "774 rdfs:subClassOf None aio:Survival_Analysis \n",
- "\n",
- " object_label \n",
- "769 Machine Learning \n",
- "770 Method \n",
- "771 Proportional Hazards Model \n",
- "772 Regression Analysis \n",
- "773 Supervised Learning \n",
- "774 Survival Analysis \n",
- "parent aio:Machine_Learning\n",
- "aio:RBM\n",
- " subject subject_label predicate predicate_label \\\n",
- "775 aio:RBM Restricted Boltzmann Machine rdfs:subClassOf None \n",
- "776 aio:RBM Restricted Boltzmann Machine rdfs:subClassOf None \n",
- "777 aio:RBM Restricted Boltzmann Machine rdfs:subClassOf None \n",
- "778 aio:RBM Restricted Boltzmann Machine rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "775 aio:BM Boltzmann Machine Network \n",
- "776 aio:Network Network \n",
- "777 aio:RBM Restricted Boltzmann Machine \n",
- "778 aio:SCN Symmetrically Connected Network \n",
- "parent aio:BM\n",
- "aio:BM\n",
- " subject subject_label predicate predicate_label \\\n",
- "89 aio:BM Boltzmann Machine Network rdfs:subClassOf None \n",
- "90 aio:BM Boltzmann Machine Network rdfs:subClassOf None \n",
- "91 aio:BM Boltzmann Machine Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "89 aio:BM Boltzmann Machine Network \n",
- "90 aio:Network Network \n",
- "91 aio:SCN Symmetrically Connected Network \n",
- "parent aio:BM\n",
- "aio:RBM\n",
- " subject subject_label predicate predicate_label \\\n",
- "775 aio:RBM Restricted Boltzmann Machine rdfs:subClassOf None \n",
- "776 aio:RBM Restricted Boltzmann Machine rdfs:subClassOf None \n",
- "777 aio:RBM Restricted Boltzmann Machine rdfs:subClassOf None \n",
- "778 aio:RBM Restricted Boltzmann Machine rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "775 aio:BM Boltzmann Machine Network \n",
- "776 aio:Network Network \n",
- "777 aio:RBM Restricted Boltzmann Machine \n",
- "778 aio:SCN Symmetrically Connected Network \n",
- "parent aio:BM\n",
- "aio:BM\n",
- " subject subject_label predicate predicate_label \\\n",
- "89 aio:BM Boltzmann Machine Network rdfs:subClassOf None \n",
- "90 aio:BM Boltzmann Machine Network rdfs:subClassOf None \n",
- "91 aio:BM Boltzmann Machine Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "89 aio:BM Boltzmann Machine Network \n",
- "90 aio:Network Network \n",
- "91 aio:SCN Symmetrically Connected Network \n",
- "parent aio:BM\n",
- "aio:RBM\n",
- " subject subject_label predicate predicate_label \\\n",
- "775 aio:RBM Restricted Boltzmann Machine rdfs:subClassOf None \n",
- "776 aio:RBM Restricted Boltzmann Machine rdfs:subClassOf None \n",
- "777 aio:RBM Restricted Boltzmann Machine rdfs:subClassOf None \n",
- "778 aio:RBM Restricted Boltzmann Machine rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "775 aio:BM Boltzmann Machine Network \n",
- "776 aio:Network Network \n",
- "777 aio:RBM Restricted Boltzmann Machine \n",
- "778 aio:SCN Symmetrically Connected Network \n",
- "parent aio:BM\n",
- "aio:BM\n",
- " subject subject_label predicate predicate_label \\\n",
- "89 aio:BM Boltzmann Machine Network rdfs:subClassOf None \n",
- "90 aio:BM Boltzmann Machine Network rdfs:subClassOf None \n",
- "91 aio:BM Boltzmann Machine Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "89 aio:BM Boltzmann Machine Network \n",
- "90 aio:Network Network \n",
- "91 aio:SCN Symmetrically Connected Network \n",
- "parent aio:BM\n",
- "aio:RBM\n",
- " subject subject_label predicate predicate_label \\\n",
- "775 aio:RBM Restricted Boltzmann Machine rdfs:subClassOf None \n",
- "776 aio:RBM Restricted Boltzmann Machine rdfs:subClassOf None \n",
- "777 aio:RBM Restricted Boltzmann Machine rdfs:subClassOf None \n",
- "778 aio:RBM Restricted Boltzmann Machine rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "775 aio:BM Boltzmann Machine Network \n",
- "776 aio:Network Network \n",
- "777 aio:RBM Restricted Boltzmann Machine \n",
- "778 aio:SCN Symmetrically Connected Network \n",
- "parent aio:BM\n",
- "aio:BM\n",
- " subject subject_label predicate predicate_label \\\n",
- "89 aio:BM Boltzmann Machine Network rdfs:subClassOf None \n",
- "90 aio:BM Boltzmann Machine Network rdfs:subClassOf None \n",
- "91 aio:BM Boltzmann Machine Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "89 aio:BM Boltzmann Machine Network \n",
- "90 aio:Network Network \n",
- "91 aio:SCN Symmetrically Connected Network \n",
- "parent aio:BM\n",
- "aio:RBN\n",
- " subject subject_label predicate predicate_label \\\n",
- "779 aio:RBN Radial Basis Network rdfs:subClassOf None \n",
- "780 aio:RBN Radial Basis Network rdfs:subClassOf None \n",
- "781 aio:RBN Radial Basis Network rdfs:subClassOf None \n",
- "782 aio:RBN Radial Basis Network rdfs:subClassOf None \n",
- "783 aio:RBN Radial Basis Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "779 aio:ANN Artificial Neural Network \n",
- "780 aio:DFF Deep FeedFoward \n",
- "781 aio:DNN Deep Neural Network \n",
- "782 aio:Network Network \n",
- "783 aio:RBN Radial Basis Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:RBN\n",
- " subject subject_label predicate predicate_label \\\n",
- "779 aio:RBN Radial Basis Network rdfs:subClassOf None \n",
- "780 aio:RBN Radial Basis Network rdfs:subClassOf None \n",
- "781 aio:RBN Radial Basis Network rdfs:subClassOf None \n",
- "782 aio:RBN Radial Basis Network rdfs:subClassOf None \n",
- "783 aio:RBN Radial Basis Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "779 aio:ANN Artificial Neural Network \n",
- "780 aio:DFF Deep FeedFoward \n",
- "781 aio:DNN Deep Neural Network \n",
- "782 aio:Network Network \n",
- "783 aio:RBN Radial Basis Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:RBN\n",
- " subject subject_label predicate predicate_label \\\n",
- "779 aio:RBN Radial Basis Network rdfs:subClassOf None \n",
- "780 aio:RBN Radial Basis Network rdfs:subClassOf None \n",
- "781 aio:RBN Radial Basis Network rdfs:subClassOf None \n",
- "782 aio:RBN Radial Basis Network rdfs:subClassOf None \n",
- "783 aio:RBN Radial Basis Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "779 aio:ANN Artificial Neural Network \n",
- "780 aio:DFF Deep FeedFoward \n",
- "781 aio:DNN Deep Neural Network \n",
- "782 aio:Network Network \n",
- "783 aio:RBN Radial Basis Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:RBN\n",
- " subject subject_label predicate predicate_label \\\n",
- "779 aio:RBN Radial Basis Network rdfs:subClassOf None \n",
- "780 aio:RBN Radial Basis Network rdfs:subClassOf None \n",
- "781 aio:RBN Radial Basis Network rdfs:subClassOf None \n",
- "782 aio:RBN Radial Basis Network rdfs:subClassOf None \n",
- "783 aio:RBN Radial Basis Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "779 aio:ANN Artificial Neural Network \n",
- "780 aio:DFF Deep FeedFoward \n",
- "781 aio:DNN Deep Neural Network \n",
- "782 aio:Network Network \n",
- "783 aio:RBN Radial Basis Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:RBN\n",
- " subject subject_label predicate predicate_label \\\n",
- "779 aio:RBN Radial Basis Network rdfs:subClassOf None \n",
- "780 aio:RBN Radial Basis Network rdfs:subClassOf None \n",
- "781 aio:RBN Radial Basis Network rdfs:subClassOf None \n",
- "782 aio:RBN Radial Basis Network rdfs:subClassOf None \n",
- "783 aio:RBN Radial Basis Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "779 aio:ANN Artificial Neural Network \n",
- "780 aio:DFF Deep FeedFoward \n",
- "781 aio:DNN Deep Neural Network \n",
- "782 aio:Network Network \n",
- "783 aio:RBN Radial Basis Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:RNN_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "784 aio:RNN_Layer RNN Layer rdfs:subClassOf None \n",
- "785 aio:RNN_Layer RNN Layer rdfs:subClassOf None \n",
- "786 aio:RNN_Layer RNN Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "784 aio:Layer Layer \n",
- "785 aio:RNN_Layer RNN Layer \n",
- "786 aio:Recurrent_Layer Recurrent Layer \n",
- "parent aio:Layer\n",
- "aio:RNN_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "784 aio:RNN_Layer RNN Layer rdfs:subClassOf None \n",
- "785 aio:RNN_Layer RNN Layer rdfs:subClassOf None \n",
- "786 aio:RNN_Layer RNN Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "784 aio:Layer Layer \n",
- "785 aio:RNN_Layer RNN Layer \n",
- "786 aio:Recurrent_Layer Recurrent Layer \n",
- "parent aio:Layer\n",
- "aio:RNN_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "784 aio:RNN_Layer RNN Layer rdfs:subClassOf None \n",
- "785 aio:RNN_Layer RNN Layer rdfs:subClassOf None \n",
- "786 aio:RNN_Layer RNN Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "784 aio:Layer Layer \n",
- "785 aio:RNN_Layer RNN Layer \n",
- "786 aio:Recurrent_Layer Recurrent Layer \n",
- "parent aio:Layer\n",
- "aio:RandomBrightness_Layer\n",
- " subject subject_label predicate \\\n",
- "787 aio:RandomBrightness_Layer RandomBrightness Layer rdfs:subClassOf \n",
- "788 aio:RandomBrightness_Layer RandomBrightness Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "787 None aio:Layer Layer \n",
- "788 None aio:RandomBrightness_Layer RandomBrightness Layer \n",
- "parent aio:Layer\n",
- "aio:RandomBrightness_Layer\n",
- " subject subject_label predicate \\\n",
- "787 aio:RandomBrightness_Layer RandomBrightness Layer rdfs:subClassOf \n",
- "788 aio:RandomBrightness_Layer RandomBrightness Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "787 None aio:Layer Layer \n",
- "788 None aio:RandomBrightness_Layer RandomBrightness Layer \n",
- "parent aio:Layer\n",
- "aio:RandomContrast_Layer\n",
- " subject subject_label predicate \\\n",
- "789 aio:RandomContrast_Layer RandomContrast Layer rdfs:subClassOf \n",
- "790 aio:RandomContrast_Layer RandomContrast Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "789 None aio:Layer Layer \n",
- "790 None aio:RandomContrast_Layer RandomContrast Layer \n",
- "parent aio:Layer\n",
- "aio:RandomContrast_Layer\n",
- " subject subject_label predicate \\\n",
- "789 aio:RandomContrast_Layer RandomContrast Layer rdfs:subClassOf \n",
- "790 aio:RandomContrast_Layer RandomContrast Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "789 None aio:Layer Layer \n",
- "790 None aio:RandomContrast_Layer RandomContrast Layer \n",
- "parent aio:Layer\n",
- "aio:RandomCrop_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "791 aio:RandomCrop_Layer RandomCrop Layer rdfs:subClassOf None \n",
- "792 aio:RandomCrop_Layer RandomCrop Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "791 aio:Layer Layer \n",
- "792 aio:RandomCrop_Layer RandomCrop Layer \n",
- "parent aio:Layer\n",
- "aio:RandomCrop_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "791 aio:RandomCrop_Layer RandomCrop Layer rdfs:subClassOf None \n",
- "792 aio:RandomCrop_Layer RandomCrop Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "791 aio:Layer Layer \n",
- "792 aio:RandomCrop_Layer RandomCrop Layer \n",
- "parent aio:Layer\n",
- "aio:RandomFlip_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "793 aio:RandomFlip_Layer RandomFlip Layer rdfs:subClassOf None \n",
- "794 aio:RandomFlip_Layer RandomFlip Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "793 aio:Layer Layer \n",
- "794 aio:RandomFlip_Layer RandomFlip Layer \n",
- "parent aio:Layer\n",
- "aio:RandomFlip_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "793 aio:RandomFlip_Layer RandomFlip Layer rdfs:subClassOf None \n",
- "794 aio:RandomFlip_Layer RandomFlip Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "793 aio:Layer Layer \n",
- "794 aio:RandomFlip_Layer RandomFlip Layer \n",
- "parent aio:Layer\n",
- "aio:RandomHeight_Layer\n",
- " subject subject_label predicate \\\n",
- "795 aio:RandomHeight_Layer RandomHeight Layer rdfs:subClassOf \n",
- "796 aio:RandomHeight_Layer RandomHeight Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "795 None aio:Layer Layer \n",
- "796 None aio:RandomHeight_Layer RandomHeight Layer \n",
- "parent aio:Layer\n",
- "aio:RandomHeight_Layer\n",
- " subject subject_label predicate \\\n",
- "795 aio:RandomHeight_Layer RandomHeight Layer rdfs:subClassOf \n",
- "796 aio:RandomHeight_Layer RandomHeight Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "795 None aio:Layer Layer \n",
- "796 None aio:RandomHeight_Layer RandomHeight Layer \n",
- "parent aio:Layer\n",
- "aio:RandomRotation_Layer\n",
- " subject subject_label predicate \\\n",
- "797 aio:RandomRotation_Layer RandomRotation Layer rdfs:subClassOf \n",
- "798 aio:RandomRotation_Layer RandomRotation Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "797 None aio:Layer Layer \n",
- "798 None aio:RandomRotation_Layer RandomRotation Layer \n",
- "parent aio:Layer\n",
- "aio:RandomRotation_Layer\n",
- " subject subject_label predicate \\\n",
- "797 aio:RandomRotation_Layer RandomRotation Layer rdfs:subClassOf \n",
- "798 aio:RandomRotation_Layer RandomRotation Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "797 None aio:Layer Layer \n",
- "798 None aio:RandomRotation_Layer RandomRotation Layer \n",
- "parent aio:Layer\n",
- "aio:RandomTranslation_Layer\n",
- " subject subject_label predicate \\\n",
- "799 aio:RandomTranslation_Layer RandomTranslation Layer rdfs:subClassOf \n",
- "800 aio:RandomTranslation_Layer RandomTranslation Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "799 None aio:Layer Layer \n",
- "800 None aio:RandomTranslation_Layer RandomTranslation Layer \n",
- "parent aio:Layer\n",
- "aio:RandomTranslation_Layer\n",
- " subject subject_label predicate \\\n",
- "799 aio:RandomTranslation_Layer RandomTranslation Layer rdfs:subClassOf \n",
- "800 aio:RandomTranslation_Layer RandomTranslation Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "799 None aio:Layer Layer \n",
- "800 None aio:RandomTranslation_Layer RandomTranslation Layer \n",
- "parent aio:Layer\n",
- "aio:RandomWidth_Layer\n",
- " subject subject_label predicate \\\n",
- "801 aio:RandomWidth_Layer RandomWidth Layer rdfs:subClassOf \n",
- "802 aio:RandomWidth_Layer RandomWidth Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "801 None aio:Layer Layer \n",
- "802 None aio:RandomWidth_Layer RandomWidth Layer \n",
- "parent aio:Layer\n",
- "aio:RandomWidth_Layer\n",
- " subject subject_label predicate \\\n",
- "801 aio:RandomWidth_Layer RandomWidth Layer rdfs:subClassOf \n",
- "802 aio:RandomWidth_Layer RandomWidth Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "801 None aio:Layer Layer \n",
- "802 None aio:RandomWidth_Layer RandomWidth Layer \n",
- "parent aio:Layer\n",
- "aio:RandomZoom_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "803 aio:RandomZoom_Layer RandomZoom Layer rdfs:subClassOf None \n",
- "804 aio:RandomZoom_Layer RandomZoom Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "803 aio:Layer Layer \n",
- "804 aio:RandomZoom_Layer RandomZoom Layer \n",
- "parent aio:Layer\n",
- "aio:RandomZoom_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "803 aio:RandomZoom_Layer RandomZoom Layer rdfs:subClassOf None \n",
- "804 aio:RandomZoom_Layer RandomZoom Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "803 aio:Layer Layer \n",
- "804 aio:RandomZoom_Layer RandomZoom Layer \n",
- "parent aio:Layer\n",
- "aio:Random_Effects_Model\n",
- " subject subject_label predicate \\\n",
- "805 aio:Random_Effects_Model Random Effects Model rdfs:subClassOf \n",
- "806 aio:Random_Effects_Model Random Effects Model rdfs:subClassOf \n",
- "807 aio:Random_Effects_Model Random Effects Model rdfs:subClassOf \n",
- "808 aio:Random_Effects_Model Random Effects Model rdfs:subClassOf \n",
- "809 aio:Random_Effects_Model Random Effects Model rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "805 None aio:Machine_Learning Machine Learning \n",
- "806 None aio:Method Method \n",
- "807 None aio:Random_Effects_Model Random Effects Model \n",
- "808 None aio:Regression_Analysis Regression Analysis \n",
- "809 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Random_Effects_Model\n",
- " subject subject_label predicate \\\n",
- "805 aio:Random_Effects_Model Random Effects Model rdfs:subClassOf \n",
- "806 aio:Random_Effects_Model Random Effects Model rdfs:subClassOf \n",
- "807 aio:Random_Effects_Model Random Effects Model rdfs:subClassOf \n",
- "808 aio:Random_Effects_Model Random Effects Model rdfs:subClassOf \n",
- "809 aio:Random_Effects_Model Random Effects Model rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "805 None aio:Machine_Learning Machine Learning \n",
- "806 None aio:Method Method \n",
- "807 None aio:Random_Effects_Model Random Effects Model \n",
- "808 None aio:Regression_Analysis Regression Analysis \n",
- "809 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Random_Effects_Model\n",
- " subject subject_label predicate \\\n",
- "805 aio:Random_Effects_Model Random Effects Model rdfs:subClassOf \n",
- "806 aio:Random_Effects_Model Random Effects Model rdfs:subClassOf \n",
- "807 aio:Random_Effects_Model Random Effects Model rdfs:subClassOf \n",
- "808 aio:Random_Effects_Model Random Effects Model rdfs:subClassOf \n",
- "809 aio:Random_Effects_Model Random Effects Model rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "805 None aio:Machine_Learning Machine Learning \n",
- "806 None aio:Method Method \n",
- "807 None aio:Random_Effects_Model Random Effects Model \n",
- "808 None aio:Regression_Analysis Regression Analysis \n",
- "809 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Random_Effects_Model\n",
- " subject subject_label predicate \\\n",
- "805 aio:Random_Effects_Model Random Effects Model rdfs:subClassOf \n",
- "806 aio:Random_Effects_Model Random Effects Model rdfs:subClassOf \n",
- "807 aio:Random_Effects_Model Random Effects Model rdfs:subClassOf \n",
- "808 aio:Random_Effects_Model Random Effects Model rdfs:subClassOf \n",
- "809 aio:Random_Effects_Model Random Effects Model rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "805 None aio:Machine_Learning Machine Learning \n",
- "806 None aio:Method Method \n",
- "807 None aio:Random_Effects_Model Random Effects Model \n",
- "808 None aio:Regression_Analysis Regression Analysis \n",
- "809 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Random_Effects_Model\n",
- " subject subject_label predicate \\\n",
- "805 aio:Random_Effects_Model Random Effects Model rdfs:subClassOf \n",
- "806 aio:Random_Effects_Model Random Effects Model rdfs:subClassOf \n",
- "807 aio:Random_Effects_Model Random Effects Model rdfs:subClassOf \n",
- "808 aio:Random_Effects_Model Random Effects Model rdfs:subClassOf \n",
- "809 aio:Random_Effects_Model Random Effects Model rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "805 None aio:Machine_Learning Machine Learning \n",
- "806 None aio:Method Method \n",
- "807 None aio:Random_Effects_Model Random Effects Model \n",
- "808 None aio:Regression_Analysis Regression Analysis \n",
- "809 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Random_Forest\n",
- " subject subject_label predicate predicate_label \\\n",
- "810 aio:Random_Forest Random Forest rdfs:subClassOf None \n",
- "811 aio:Random_Forest Random Forest rdfs:subClassOf None \n",
- "812 aio:Random_Forest Random Forest rdfs:subClassOf None \n",
- "813 aio:Random_Forest Random Forest rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "810 aio:Ensemble_Learning Ensemble Learning \n",
- "811 aio:Machine_Learning Machine Learning \n",
- "812 aio:Method Method \n",
- "813 aio:Random_Forest Random Forest \n",
- "parent aio:Ensemble_Learning\n",
- "aio:Ensemble_Learning\n",
- " subject subject_label predicate \\\n",
- "313 aio:Ensemble_Learning Ensemble Learning rdfs:subClassOf \n",
- "314 aio:Ensemble_Learning Ensemble Learning rdfs:subClassOf \n",
- "315 aio:Ensemble_Learning Ensemble Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "313 None aio:Ensemble_Learning Ensemble Learning \n",
- "314 None aio:Machine_Learning Machine Learning \n",
- "315 None aio:Method Method \n",
- "parent aio:Ensemble_Learning\n",
- "aio:Random_Forest\n",
- " subject subject_label predicate predicate_label \\\n",
- "810 aio:Random_Forest Random Forest rdfs:subClassOf None \n",
- "811 aio:Random_Forest Random Forest rdfs:subClassOf None \n",
- "812 aio:Random_Forest Random Forest rdfs:subClassOf None \n",
- "813 aio:Random_Forest Random Forest rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "810 aio:Ensemble_Learning Ensemble Learning \n",
- "811 aio:Machine_Learning Machine Learning \n",
- "812 aio:Method Method \n",
- "813 aio:Random_Forest Random Forest \n",
- "parent aio:Ensemble_Learning\n",
- "aio:Ensemble_Learning\n",
- " subject subject_label predicate \\\n",
- "313 aio:Ensemble_Learning Ensemble Learning rdfs:subClassOf \n",
- "314 aio:Ensemble_Learning Ensemble Learning rdfs:subClassOf \n",
- "315 aio:Ensemble_Learning Ensemble Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "313 None aio:Ensemble_Learning Ensemble Learning \n",
- "314 None aio:Machine_Learning Machine Learning \n",
- "315 None aio:Method Method \n",
- "parent aio:Ensemble_Learning\n",
- "aio:Random_Forest\n",
- " subject subject_label predicate predicate_label \\\n",
- "810 aio:Random_Forest Random Forest rdfs:subClassOf None \n",
- "811 aio:Random_Forest Random Forest rdfs:subClassOf None \n",
- "812 aio:Random_Forest Random Forest rdfs:subClassOf None \n",
- "813 aio:Random_Forest Random Forest rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "810 aio:Ensemble_Learning Ensemble Learning \n",
- "811 aio:Machine_Learning Machine Learning \n",
- "812 aio:Method Method \n",
- "813 aio:Random_Forest Random Forest \n",
- "parent aio:Ensemble_Learning\n",
- "aio:Ensemble_Learning\n",
- " subject subject_label predicate \\\n",
- "313 aio:Ensemble_Learning Ensemble Learning rdfs:subClassOf \n",
- "314 aio:Ensemble_Learning Ensemble Learning rdfs:subClassOf \n",
- "315 aio:Ensemble_Learning Ensemble Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "313 None aio:Ensemble_Learning Ensemble Learning \n",
- "314 None aio:Machine_Learning Machine Learning \n",
- "315 None aio:Method Method \n",
- "parent aio:Ensemble_Learning\n",
- "aio:Random_Forest\n",
- " subject subject_label predicate predicate_label \\\n",
- "810 aio:Random_Forest Random Forest rdfs:subClassOf None \n",
- "811 aio:Random_Forest Random Forest rdfs:subClassOf None \n",
- "812 aio:Random_Forest Random Forest rdfs:subClassOf None \n",
- "813 aio:Random_Forest Random Forest rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "810 aio:Ensemble_Learning Ensemble Learning \n",
- "811 aio:Machine_Learning Machine Learning \n",
- "812 aio:Method Method \n",
- "813 aio:Random_Forest Random Forest \n",
- "parent aio:Ensemble_Learning\n",
- "aio:Ensemble_Learning\n",
- " subject subject_label predicate \\\n",
- "313 aio:Ensemble_Learning Ensemble Learning rdfs:subClassOf \n",
- "314 aio:Ensemble_Learning Ensemble Learning rdfs:subClassOf \n",
- "315 aio:Ensemble_Learning Ensemble Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "313 None aio:Ensemble_Learning Ensemble Learning \n",
- "314 None aio:Machine_Learning Machine Learning \n",
- "315 None aio:Method Method \n",
- "parent aio:Ensemble_Learning\n",
- "aio:Ranking_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "814 aio:Ranking_Bias Ranking Bias rdfs:subClassOf None \n",
- "815 aio:Ranking_Bias Ranking Bias rdfs:subClassOf None \n",
- "816 aio:Ranking_Bias Ranking Bias rdfs:subClassOf None \n",
- "817 aio:Ranking_Bias Ranking Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "814 aio:Anchoring_Bias Anchoring Bias \n",
- "815 aio:Bias Bias \n",
- "816 aio:Individual_Bias Individual Bias \n",
- "817 aio:Ranking_Bias Ranking Bias \n",
- "parent aio:Anchoring_Bias\n",
- "aio:Anchoring_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "50 aio:Anchoring_Bias Anchoring Bias rdfs:subClassOf None \n",
- "51 aio:Anchoring_Bias Anchoring Bias rdfs:subClassOf None \n",
- "52 aio:Anchoring_Bias Anchoring Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "50 aio:Anchoring_Bias Anchoring Bias \n",
- "51 aio:Bias Bias \n",
- "52 aio:Individual_Bias Individual Bias \n",
- "parent aio:Anchoring_Bias\n",
- "aio:Ranking_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "814 aio:Ranking_Bias Ranking Bias rdfs:subClassOf None \n",
- "815 aio:Ranking_Bias Ranking Bias rdfs:subClassOf None \n",
- "816 aio:Ranking_Bias Ranking Bias rdfs:subClassOf None \n",
- "817 aio:Ranking_Bias Ranking Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "814 aio:Anchoring_Bias Anchoring Bias \n",
- "815 aio:Bias Bias \n",
- "816 aio:Individual_Bias Individual Bias \n",
- "817 aio:Ranking_Bias Ranking Bias \n",
- "parent aio:Anchoring_Bias\n",
- "aio:Anchoring_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "50 aio:Anchoring_Bias Anchoring Bias rdfs:subClassOf None \n",
- "51 aio:Anchoring_Bias Anchoring Bias rdfs:subClassOf None \n",
- "52 aio:Anchoring_Bias Anchoring Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "50 aio:Anchoring_Bias Anchoring Bias \n",
- "51 aio:Bias Bias \n",
- "52 aio:Individual_Bias Individual Bias \n",
- "parent aio:Anchoring_Bias\n",
- "aio:Ranking_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "814 aio:Ranking_Bias Ranking Bias rdfs:subClassOf None \n",
- "815 aio:Ranking_Bias Ranking Bias rdfs:subClassOf None \n",
- "816 aio:Ranking_Bias Ranking Bias rdfs:subClassOf None \n",
- "817 aio:Ranking_Bias Ranking Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "814 aio:Anchoring_Bias Anchoring Bias \n",
- "815 aio:Bias Bias \n",
- "816 aio:Individual_Bias Individual Bias \n",
- "817 aio:Ranking_Bias Ranking Bias \n",
- "parent aio:Anchoring_Bias\n",
- "aio:Anchoring_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "50 aio:Anchoring_Bias Anchoring Bias rdfs:subClassOf None \n",
- "51 aio:Anchoring_Bias Anchoring Bias rdfs:subClassOf None \n",
- "52 aio:Anchoring_Bias Anchoring Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "50 aio:Anchoring_Bias Anchoring Bias \n",
- "51 aio:Bias Bias \n",
- "52 aio:Individual_Bias Individual Bias \n",
- "parent aio:Anchoring_Bias\n",
- "aio:Ranking_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "814 aio:Ranking_Bias Ranking Bias rdfs:subClassOf None \n",
- "815 aio:Ranking_Bias Ranking Bias rdfs:subClassOf None \n",
- "816 aio:Ranking_Bias Ranking Bias rdfs:subClassOf None \n",
- "817 aio:Ranking_Bias Ranking Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "814 aio:Anchoring_Bias Anchoring Bias \n",
- "815 aio:Bias Bias \n",
- "816 aio:Individual_Bias Individual Bias \n",
- "817 aio:Ranking_Bias Ranking Bias \n",
- "parent aio:Anchoring_Bias\n",
- "aio:Anchoring_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "50 aio:Anchoring_Bias Anchoring Bias rdfs:subClassOf None \n",
- "51 aio:Anchoring_Bias Anchoring Bias rdfs:subClassOf None \n",
- "52 aio:Anchoring_Bias Anchoring Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "50 aio:Anchoring_Bias Anchoring Bias \n",
- "51 aio:Bias Bias \n",
- "52 aio:Individual_Bias Individual Bias \n",
- "parent aio:Anchoring_Bias\n",
- "aio:Rashomon_Effect_Bias\n",
- " subject subject_label predicate \\\n",
- "818 aio:Rashomon_Effect_Bias Rashomon Effect Bias rdfs:subClassOf \n",
- "819 aio:Rashomon_Effect_Bias Rashomon Effect Bias rdfs:subClassOf \n",
- "820 aio:Rashomon_Effect_Bias Rashomon Effect Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "818 None aio:Bias Bias \n",
- "819 None aio:Individual_Bias Individual Bias \n",
- "820 None aio:Rashomon_Effect_Bias Rashomon Effect Bias \n",
- "parent aio:Bias\n",
- "aio:Rashomon_Effect_Bias\n",
- " subject subject_label predicate \\\n",
- "818 aio:Rashomon_Effect_Bias Rashomon Effect Bias rdfs:subClassOf \n",
- "819 aio:Rashomon_Effect_Bias Rashomon Effect Bias rdfs:subClassOf \n",
- "820 aio:Rashomon_Effect_Bias Rashomon Effect Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "818 None aio:Bias Bias \n",
- "819 None aio:Individual_Bias Individual Bias \n",
- "820 None aio:Rashomon_Effect_Bias Rashomon Effect Bias \n",
- "parent aio:Bias\n",
- "aio:Rashomon_Effect_Bias\n",
- " subject subject_label predicate \\\n",
- "818 aio:Rashomon_Effect_Bias Rashomon Effect Bias rdfs:subClassOf \n",
- "819 aio:Rashomon_Effect_Bias Rashomon Effect Bias rdfs:subClassOf \n",
- "820 aio:Rashomon_Effect_Bias Rashomon Effect Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "818 None aio:Bias Bias \n",
- "819 None aio:Individual_Bias Individual Bias \n",
- "820 None aio:Rashomon_Effect_Bias Rashomon Effect Bias \n",
- "parent aio:Bias\n",
- "aio:ReLU_Function\n",
- " subject subject_label predicate predicate_label \\\n",
- "821 aio:ReLU_Function ReLU Function rdfs:subClassOf None \n",
- "822 aio:ReLU_Function ReLU Function rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "821 aio:Function None \n",
- "822 aio:ReLU_Function ReLU Function \n",
- "parent aio:Function\n",
- "aio:ReLU_Function\n",
- " subject subject_label predicate predicate_label \\\n",
- "821 aio:ReLU_Function ReLU Function rdfs:subClassOf None \n",
- "822 aio:ReLU_Function ReLU Function rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "821 aio:Function None \n",
- "822 aio:ReLU_Function ReLU Function \n",
- "parent aio:Function\n",
- "aio:ReLU_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "823 aio:ReLU_Layer ReLU Layer rdfs:subClassOf None \n",
- "824 aio:ReLU_Layer ReLU Layer rdfs:subClassOf None \n",
- "825 aio:ReLU_Layer ReLU Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "823 aio:Activation_Layer Activation Layer \n",
- "824 aio:Layer Layer \n",
- "825 aio:ReLU_Layer ReLU Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:Activation_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "7 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "8 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "7 aio:Activation_Layer Activation Layer \n",
- "8 aio:Layer Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:ReLU_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "823 aio:ReLU_Layer ReLU Layer rdfs:subClassOf None \n",
- "824 aio:ReLU_Layer ReLU Layer rdfs:subClassOf None \n",
- "825 aio:ReLU_Layer ReLU Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "823 aio:Activation_Layer Activation Layer \n",
- "824 aio:Layer Layer \n",
- "825 aio:ReLU_Layer ReLU Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:Activation_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "7 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "8 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "7 aio:Activation_Layer Activation Layer \n",
- "8 aio:Layer Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:ReLU_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "823 aio:ReLU_Layer ReLU Layer rdfs:subClassOf None \n",
- "824 aio:ReLU_Layer ReLU Layer rdfs:subClassOf None \n",
- "825 aio:ReLU_Layer ReLU Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "823 aio:Activation_Layer Activation Layer \n",
- "824 aio:Layer Layer \n",
- "825 aio:ReLU_Layer ReLU Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:Activation_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "7 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "8 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "7 aio:Activation_Layer Activation Layer \n",
- "8 aio:Layer Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:RecNN\n",
- " subject subject_label predicate predicate_label \\\n",
- "826 aio:RecNN Recurrent Neural Network rdfs:subClassOf None \n",
- "827 aio:RecNN Recurrent Neural Network rdfs:subClassOf None \n",
- "828 aio:RecNN Recurrent Neural Network rdfs:subClassOf None \n",
- "829 aio:RecNN Recurrent Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "826 aio:ANN Artificial Neural Network \n",
- "827 aio:DNN Deep Neural Network \n",
- "828 aio:Network Network \n",
- "829 aio:RecNN Recurrent Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:RecNN\n",
- " subject subject_label predicate predicate_label \\\n",
- "826 aio:RecNN Recurrent Neural Network rdfs:subClassOf None \n",
- "827 aio:RecNN Recurrent Neural Network rdfs:subClassOf None \n",
- "828 aio:RecNN Recurrent Neural Network rdfs:subClassOf None \n",
- "829 aio:RecNN Recurrent Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "826 aio:ANN Artificial Neural Network \n",
- "827 aio:DNN Deep Neural Network \n",
- "828 aio:Network Network \n",
- "829 aio:RecNN Recurrent Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:RecNN\n",
- " subject subject_label predicate predicate_label \\\n",
- "826 aio:RecNN Recurrent Neural Network rdfs:subClassOf None \n",
- "827 aio:RecNN Recurrent Neural Network rdfs:subClassOf None \n",
- "828 aio:RecNN Recurrent Neural Network rdfs:subClassOf None \n",
- "829 aio:RecNN Recurrent Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "826 aio:ANN Artificial Neural Network \n",
- "827 aio:DNN Deep Neural Network \n",
- "828 aio:Network Network \n",
- "829 aio:RecNN Recurrent Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:RecNN\n",
- " subject subject_label predicate predicate_label \\\n",
- "826 aio:RecNN Recurrent Neural Network rdfs:subClassOf None \n",
- "827 aio:RecNN Recurrent Neural Network rdfs:subClassOf None \n",
- "828 aio:RecNN Recurrent Neural Network rdfs:subClassOf None \n",
- "829 aio:RecNN Recurrent Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "826 aio:ANN Artificial Neural Network \n",
- "827 aio:DNN Deep Neural Network \n",
- "828 aio:Network Network \n",
- "829 aio:RecNN Recurrent Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:RecuNN\n",
- " subject subject_label predicate predicate_label \\\n",
- "830 aio:RecuNN Recursive Neural Network rdfs:subClassOf None \n",
- "831 aio:RecuNN Recursive Neural Network rdfs:subClassOf None \n",
- "832 aio:RecuNN Recursive Neural Network rdfs:subClassOf None \n",
- "833 aio:RecuNN Recursive Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "830 aio:ANN Artificial Neural Network \n",
- "831 aio:DNN Deep Neural Network \n",
- "832 aio:Network Network \n",
- "833 aio:RecuNN Recursive Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:RecuNN\n",
- " subject subject_label predicate predicate_label \\\n",
- "830 aio:RecuNN Recursive Neural Network rdfs:subClassOf None \n",
- "831 aio:RecuNN Recursive Neural Network rdfs:subClassOf None \n",
- "832 aio:RecuNN Recursive Neural Network rdfs:subClassOf None \n",
- "833 aio:RecuNN Recursive Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "830 aio:ANN Artificial Neural Network \n",
- "831 aio:DNN Deep Neural Network \n",
- "832 aio:Network Network \n",
- "833 aio:RecuNN Recursive Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:RecuNN\n",
- " subject subject_label predicate predicate_label \\\n",
- "830 aio:RecuNN Recursive Neural Network rdfs:subClassOf None \n",
- "831 aio:RecuNN Recursive Neural Network rdfs:subClassOf None \n",
- "832 aio:RecuNN Recursive Neural Network rdfs:subClassOf None \n",
- "833 aio:RecuNN Recursive Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "830 aio:ANN Artificial Neural Network \n",
- "831 aio:DNN Deep Neural Network \n",
- "832 aio:Network Network \n",
- "833 aio:RecuNN Recursive Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:RecuNN\n",
- " subject subject_label predicate predicate_label \\\n",
- "830 aio:RecuNN Recursive Neural Network rdfs:subClassOf None \n",
- "831 aio:RecuNN Recursive Neural Network rdfs:subClassOf None \n",
- "832 aio:RecuNN Recursive Neural Network rdfs:subClassOf None \n",
- "833 aio:RecuNN Recursive Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "830 aio:ANN Artificial Neural Network \n",
- "831 aio:DNN Deep Neural Network \n",
- "832 aio:Network Network \n",
- "833 aio:RecuNN Recursive Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Recurrent_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "834 aio:Recurrent_Layer Recurrent Layer rdfs:subClassOf None \n",
- "835 aio:Recurrent_Layer Recurrent Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "834 aio:Layer Layer \n",
- "835 aio:Recurrent_Layer Recurrent Layer \n",
- "parent aio:Layer\n",
- "aio:Recurrent_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "834 aio:Recurrent_Layer Recurrent Layer rdfs:subClassOf None \n",
- "835 aio:Recurrent_Layer Recurrent Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "834 aio:Layer Layer \n",
- "835 aio:Recurrent_Layer Recurrent Layer \n",
- "parent aio:Layer\n",
- "aio:Regression_Analysis\n",
- " subject subject_label predicate \\\n",
- "836 aio:Regression_Analysis Regression Analysis rdfs:subClassOf \n",
- "837 aio:Regression_Analysis Regression Analysis rdfs:subClassOf \n",
- "838 aio:Regression_Analysis Regression Analysis rdfs:subClassOf \n",
- "839 aio:Regression_Analysis Regression Analysis rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "836 None aio:Machine_Learning Machine Learning \n",
- "837 None aio:Method Method \n",
- "838 None aio:Regression_Analysis Regression Analysis \n",
- "839 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Regression_Analysis\n",
- " subject subject_label predicate \\\n",
- "836 aio:Regression_Analysis Regression Analysis rdfs:subClassOf \n",
- "837 aio:Regression_Analysis Regression Analysis rdfs:subClassOf \n",
- "838 aio:Regression_Analysis Regression Analysis rdfs:subClassOf \n",
- "839 aio:Regression_Analysis Regression Analysis rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "836 None aio:Machine_Learning Machine Learning \n",
- "837 None aio:Method Method \n",
- "838 None aio:Regression_Analysis Regression Analysis \n",
- "839 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Regression_Analysis\n",
- " subject subject_label predicate \\\n",
- "836 aio:Regression_Analysis Regression Analysis rdfs:subClassOf \n",
- "837 aio:Regression_Analysis Regression Analysis rdfs:subClassOf \n",
- "838 aio:Regression_Analysis Regression Analysis rdfs:subClassOf \n",
- "839 aio:Regression_Analysis Regression Analysis rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "836 None aio:Machine_Learning Machine Learning \n",
- "837 None aio:Method Method \n",
- "838 None aio:Regression_Analysis Regression Analysis \n",
- "839 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Regression_Analysis\n",
- " subject subject_label predicate \\\n",
- "836 aio:Regression_Analysis Regression Analysis rdfs:subClassOf \n",
- "837 aio:Regression_Analysis Regression Analysis rdfs:subClassOf \n",
- "838 aio:Regression_Analysis Regression Analysis rdfs:subClassOf \n",
- "839 aio:Regression_Analysis Regression Analysis rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "836 None aio:Machine_Learning Machine Learning \n",
- "837 None aio:Method Method \n",
- "838 None aio:Regression_Analysis Regression Analysis \n",
- "839 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Regularization_Layer\n",
- " subject subject_label predicate \\\n",
- "840 aio:Regularization_Layer Regularization Layer rdfs:subClassOf \n",
- "841 aio:Regularization_Layer Regularization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "840 None aio:Layer Layer \n",
- "841 None aio:Regularization_Layer Regularization Layer \n",
- "parent aio:Layer\n",
- "aio:Regularization_Layer\n",
- " subject subject_label predicate \\\n",
- "840 aio:Regularization_Layer Regularization Layer rdfs:subClassOf \n",
- "841 aio:Regularization_Layer Regularization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "840 None aio:Layer Layer \n",
- "841 None aio:Regularization_Layer Regularization Layer \n",
- "parent aio:Layer\n",
- "aio:Reinforcement_Learning\n",
- " subject subject_label predicate \\\n",
- "842 aio:Reinforcement_Learning Reinforcement Learning rdfs:subClassOf \n",
- "843 aio:Reinforcement_Learning Reinforcement Learning rdfs:subClassOf \n",
- "844 aio:Reinforcement_Learning Reinforcement Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "842 None aio:Machine_Learning Machine Learning \n",
- "843 None aio:Method Method \n",
- "844 None aio:Reinforcement_Learning Reinforcement Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Reinforcement_Learning\n",
- " subject subject_label predicate \\\n",
- "842 aio:Reinforcement_Learning Reinforcement Learning rdfs:subClassOf \n",
- "843 aio:Reinforcement_Learning Reinforcement Learning rdfs:subClassOf \n",
- "844 aio:Reinforcement_Learning Reinforcement Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "842 None aio:Machine_Learning Machine Learning \n",
- "843 None aio:Method Method \n",
- "844 None aio:Reinforcement_Learning Reinforcement Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Reinforcement_Learning\n",
- " subject subject_label predicate \\\n",
- "842 aio:Reinforcement_Learning Reinforcement Learning rdfs:subClassOf \n",
- "843 aio:Reinforcement_Learning Reinforcement Learning rdfs:subClassOf \n",
- "844 aio:Reinforcement_Learning Reinforcement Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "842 None aio:Machine_Learning Machine Learning \n",
- "843 None aio:Method Method \n",
- "844 None aio:Reinforcement_Learning Reinforcement Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:RepeatVector_Layer\n",
- " subject subject_label predicate \\\n",
- "845 aio:RepeatVector_Layer RepeatVector Layer rdfs:subClassOf \n",
- "846 aio:RepeatVector_Layer RepeatVector Layer rdfs:subClassOf \n",
- "847 aio:RepeatVector_Layer RepeatVector Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "845 None aio:Layer Layer \n",
- "846 None aio:RepeatVector_Layer RepeatVector Layer \n",
- "847 None aio:Reshaping_Layer Reshaping Layer \n",
- "parent aio:Layer\n",
- "aio:RepeatVector_Layer\n",
- " subject subject_label predicate \\\n",
- "845 aio:RepeatVector_Layer RepeatVector Layer rdfs:subClassOf \n",
- "846 aio:RepeatVector_Layer RepeatVector Layer rdfs:subClassOf \n",
- "847 aio:RepeatVector_Layer RepeatVector Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "845 None aio:Layer Layer \n",
- "846 None aio:RepeatVector_Layer RepeatVector Layer \n",
- "847 None aio:Reshaping_Layer Reshaping Layer \n",
- "parent aio:Layer\n",
- "aio:RepeatVector_Layer\n",
- " subject subject_label predicate \\\n",
- "845 aio:RepeatVector_Layer RepeatVector Layer rdfs:subClassOf \n",
- "846 aio:RepeatVector_Layer RepeatVector Layer rdfs:subClassOf \n",
- "847 aio:RepeatVector_Layer RepeatVector Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "845 None aio:Layer Layer \n",
- "846 None aio:RepeatVector_Layer RepeatVector Layer \n",
- "847 None aio:Reshaping_Layer Reshaping Layer \n",
- "parent aio:Layer\n",
- "aio:Representation_Bias\n",
- " subject subject_label predicate \\\n",
- "848 aio:Representation_Bias Representation Bias rdfs:subClassOf \n",
- "849 aio:Representation_Bias Representation Bias rdfs:subClassOf \n",
- "850 aio:Representation_Bias Representation Bias rdfs:subClassOf \n",
- "851 aio:Representation_Bias Representation Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "848 None aio:Bias \n",
- "849 None aio:Computational_Bias \n",
- "850 None aio:Representation_Bias \n",
- "851 None aio:Selection_And_Sampling_Bias \n",
- "\n",
- " object_label \n",
- "848 Bias \n",
- "849 Computational Bias \n",
- "850 Representation Bias \n",
- "851 Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Representation_Bias\n",
- " subject subject_label predicate \\\n",
- "848 aio:Representation_Bias Representation Bias rdfs:subClassOf \n",
- "849 aio:Representation_Bias Representation Bias rdfs:subClassOf \n",
- "850 aio:Representation_Bias Representation Bias rdfs:subClassOf \n",
- "851 aio:Representation_Bias Representation Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "848 None aio:Bias \n",
- "849 None aio:Computational_Bias \n",
- "850 None aio:Representation_Bias \n",
- "851 None aio:Selection_And_Sampling_Bias \n",
- "\n",
- " object_label \n",
- "848 Bias \n",
- "849 Computational Bias \n",
- "850 Representation Bias \n",
- "851 Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Representation_Bias\n",
- " subject subject_label predicate \\\n",
- "848 aio:Representation_Bias Representation Bias rdfs:subClassOf \n",
- "849 aio:Representation_Bias Representation Bias rdfs:subClassOf \n",
- "850 aio:Representation_Bias Representation Bias rdfs:subClassOf \n",
- "851 aio:Representation_Bias Representation Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "848 None aio:Bias \n",
- "849 None aio:Computational_Bias \n",
- "850 None aio:Representation_Bias \n",
- "851 None aio:Selection_And_Sampling_Bias \n",
- "\n",
- " object_label \n",
- "848 Bias \n",
- "849 Computational Bias \n",
- "850 Representation Bias \n",
- "851 Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Representation_Bias\n",
- " subject subject_label predicate \\\n",
- "848 aio:Representation_Bias Representation Bias rdfs:subClassOf \n",
- "849 aio:Representation_Bias Representation Bias rdfs:subClassOf \n",
- "850 aio:Representation_Bias Representation Bias rdfs:subClassOf \n",
- "851 aio:Representation_Bias Representation Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "848 None aio:Bias \n",
- "849 None aio:Computational_Bias \n",
- "850 None aio:Representation_Bias \n",
- "851 None aio:Selection_And_Sampling_Bias \n",
- "\n",
- " object_label \n",
- "848 Bias \n",
- "849 Computational Bias \n",
- "850 Representation Bias \n",
- "851 Selection And Sampling Bias \n",
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- "aio:Representation_Learning\n",
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- "855 aio:Representation_Learning Representation Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "852 None aio:ANN Artificial Neural Network \n",
- "853 None aio:DNN Deep Neural Network \n",
- "854 None aio:Network Network \n",
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- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
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- "\n",
- " object object_label \n",
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- "854 aio:Representation_Learning Representation Learning rdfs:subClassOf \n",
- "855 aio:Representation_Learning Representation Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "852 None aio:ANN Artificial Neural Network \n",
- "853 None aio:DNN Deep Neural Network \n",
- "854 None aio:Network Network \n",
- "855 None aio:Representation_Learning Representation Learning \n",
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- " subject subject_label predicate predicate_label \\\n",
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- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
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- "854 aio:Representation_Learning Representation Learning rdfs:subClassOf \n",
- "855 aio:Representation_Learning Representation Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "852 None aio:ANN Artificial Neural Network \n",
- "853 None aio:DNN Deep Neural Network \n",
- "854 None aio:Network Network \n",
- "855 None aio:Representation_Learning Representation Learning \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
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- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
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- "parent aio:ANN\n",
- "aio:Representation_Learning\n",
- " subject subject_label predicate \\\n",
- "852 aio:Representation_Learning Representation Learning rdfs:subClassOf \n",
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- "854 aio:Representation_Learning Representation Learning rdfs:subClassOf \n",
- "855 aio:Representation_Learning Representation Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "852 None aio:ANN Artificial Neural Network \n",
- "853 None aio:DNN Deep Neural Network \n",
- "854 None aio:Network Network \n",
- "855 None aio:Representation_Learning Representation Learning \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ResNN\n",
- " subject subject_label predicate predicate_label \\\n",
- "856 aio:ResNN Residual Neural Network rdfs:subClassOf None \n",
- "857 aio:ResNN Residual Neural Network rdfs:subClassOf None \n",
- "858 aio:ResNN Residual Neural Network rdfs:subClassOf None \n",
- "859 aio:ResNN Residual Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "856 aio:ANN Artificial Neural Network \n",
- "857 aio:DNN Deep Neural Network \n",
- "858 aio:Network Network \n",
- "859 aio:ResNN Residual Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ResNN\n",
- " subject subject_label predicate predicate_label \\\n",
- "856 aio:ResNN Residual Neural Network rdfs:subClassOf None \n",
- "857 aio:ResNN Residual Neural Network rdfs:subClassOf None \n",
- "858 aio:ResNN Residual Neural Network rdfs:subClassOf None \n",
- "859 aio:ResNN Residual Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "856 aio:ANN Artificial Neural Network \n",
- "857 aio:DNN Deep Neural Network \n",
- "858 aio:Network Network \n",
- "859 aio:ResNN Residual Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ResNN\n",
- " subject subject_label predicate predicate_label \\\n",
- "856 aio:ResNN Residual Neural Network rdfs:subClassOf None \n",
- "857 aio:ResNN Residual Neural Network rdfs:subClassOf None \n",
- "858 aio:ResNN Residual Neural Network rdfs:subClassOf None \n",
- "859 aio:ResNN Residual Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "856 aio:ANN Artificial Neural Network \n",
- "857 aio:DNN Deep Neural Network \n",
- "858 aio:Network Network \n",
- "859 aio:ResNN Residual Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ResNN\n",
- " subject subject_label predicate predicate_label \\\n",
- "856 aio:ResNN Residual Neural Network rdfs:subClassOf None \n",
- "857 aio:ResNN Residual Neural Network rdfs:subClassOf None \n",
- "858 aio:ResNN Residual Neural Network rdfs:subClassOf None \n",
- "859 aio:ResNN Residual Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "856 aio:ANN Artificial Neural Network \n",
- "857 aio:DNN Deep Neural Network \n",
- "858 aio:Network Network \n",
- "859 aio:ResNN Residual Neural Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Rescaling_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "860 aio:Rescaling_Layer Rescaling Layer rdfs:subClassOf None \n",
- "861 aio:Rescaling_Layer Rescaling Layer rdfs:subClassOf None \n",
- "862 aio:Rescaling_Layer Rescaling Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
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- "861 aio:Layer Layer \n",
- "862 aio:Rescaling_Layer Rescaling Layer \n",
- "parent aio:Image_Preprocessing_Layer\n",
- "aio:Image_Preprocessing_Layer\n",
- " subject subject_label \\\n",
- "460 aio:Image_Preprocessing_Layer Image Preprocessing Layer \n",
- "461 aio:Image_Preprocessing_Layer Image Preprocessing Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "460 rdfs:subClassOf None aio:Image_Preprocessing_Layer \n",
- "461 rdfs:subClassOf None aio:Layer \n",
- "\n",
- " object_label \n",
- "460 Image Preprocessing Layer \n",
- "461 Layer \n",
- "parent aio:Image_Preprocessing_Layer\n",
- "aio:Rescaling_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "860 aio:Rescaling_Layer Rescaling Layer rdfs:subClassOf None \n",
- "861 aio:Rescaling_Layer Rescaling Layer rdfs:subClassOf None \n",
- "862 aio:Rescaling_Layer Rescaling Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "860 aio:Image_Preprocessing_Layer Image Preprocessing Layer \n",
- "861 aio:Layer Layer \n",
- "862 aio:Rescaling_Layer Rescaling Layer \n",
- "parent aio:Image_Preprocessing_Layer\n",
- "aio:Image_Preprocessing_Layer\n",
- " subject subject_label \\\n",
- "460 aio:Image_Preprocessing_Layer Image Preprocessing Layer \n",
- "461 aio:Image_Preprocessing_Layer Image Preprocessing Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "460 rdfs:subClassOf None aio:Image_Preprocessing_Layer \n",
- "461 rdfs:subClassOf None aio:Layer \n",
- "\n",
- " object_label \n",
- "460 Image Preprocessing Layer \n",
- "461 Layer \n",
- "parent aio:Image_Preprocessing_Layer\n",
- "aio:Rescaling_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "860 aio:Rescaling_Layer Rescaling Layer rdfs:subClassOf None \n",
- "861 aio:Rescaling_Layer Rescaling Layer rdfs:subClassOf None \n",
- "862 aio:Rescaling_Layer Rescaling Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "860 aio:Image_Preprocessing_Layer Image Preprocessing Layer \n",
- "861 aio:Layer Layer \n",
- "862 aio:Rescaling_Layer Rescaling Layer \n",
- "parent aio:Image_Preprocessing_Layer\n",
- "aio:Image_Preprocessing_Layer\n",
- " subject subject_label \\\n",
- "460 aio:Image_Preprocessing_Layer Image Preprocessing Layer \n",
- "461 aio:Image_Preprocessing_Layer Image Preprocessing Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "460 rdfs:subClassOf None aio:Image_Preprocessing_Layer \n",
- "461 rdfs:subClassOf None aio:Layer \n",
- "\n",
- " object_label \n",
- "460 Image Preprocessing Layer \n",
- "461 Layer \n",
- "parent aio:Image_Preprocessing_Layer\n",
- "aio:Reshape_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "863 aio:Reshape_Layer Reshape Layer rdfs:subClassOf None \n",
- "864 aio:Reshape_Layer Reshape Layer rdfs:subClassOf None \n",
- "865 aio:Reshape_Layer Reshape Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "863 aio:Layer Layer \n",
- "864 aio:Reshape_Layer Reshape Layer \n",
- "865 aio:Reshaping_Layer Reshaping Layer \n",
- "parent aio:Layer\n",
- "aio:Reshape_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "863 aio:Reshape_Layer Reshape Layer rdfs:subClassOf None \n",
- "864 aio:Reshape_Layer Reshape Layer rdfs:subClassOf None \n",
- "865 aio:Reshape_Layer Reshape Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "863 aio:Layer Layer \n",
- "864 aio:Reshape_Layer Reshape Layer \n",
- "865 aio:Reshaping_Layer Reshaping Layer \n",
- "parent aio:Layer\n",
- "aio:Reshape_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "863 aio:Reshape_Layer Reshape Layer rdfs:subClassOf None \n",
- "864 aio:Reshape_Layer Reshape Layer rdfs:subClassOf None \n",
- "865 aio:Reshape_Layer Reshape Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "863 aio:Layer Layer \n",
- "864 aio:Reshape_Layer Reshape Layer \n",
- "865 aio:Reshaping_Layer Reshaping Layer \n",
- "parent aio:Layer\n",
- "aio:Reshaping_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "866 aio:Reshaping_Layer Reshaping Layer rdfs:subClassOf None \n",
- "867 aio:Reshaping_Layer Reshaping Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "866 aio:Layer Layer \n",
- "867 aio:Reshaping_Layer Reshaping Layer \n",
- "parent aio:Layer\n",
- "aio:Reshaping_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "866 aio:Reshaping_Layer Reshaping Layer rdfs:subClassOf None \n",
- "867 aio:Reshaping_Layer Reshaping Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "866 aio:Layer Layer \n",
- "867 aio:Reshaping_Layer Reshaping Layer \n",
- "parent aio:Layer\n",
- "aio:Resizing_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "868 aio:Resizing_Layer Resizing Layer rdfs:subClassOf None \n",
- "869 aio:Resizing_Layer Resizing Layer rdfs:subClassOf None \n",
- "870 aio:Resizing_Layer Resizing Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "868 aio:Image_Preprocessing_Layer Image Preprocessing Layer \n",
- "869 aio:Layer Layer \n",
- "870 aio:Resizing_Layer Resizing Layer \n",
- "parent aio:Image_Preprocessing_Layer\n",
- "aio:Image_Preprocessing_Layer\n",
- " subject subject_label \\\n",
- "460 aio:Image_Preprocessing_Layer Image Preprocessing Layer \n",
- "461 aio:Image_Preprocessing_Layer Image Preprocessing Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "460 rdfs:subClassOf None aio:Image_Preprocessing_Layer \n",
- "461 rdfs:subClassOf None aio:Layer \n",
- "\n",
- " object_label \n",
- "460 Image Preprocessing Layer \n",
- "461 Layer \n",
- "parent aio:Image_Preprocessing_Layer\n",
- "aio:Resizing_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "868 aio:Resizing_Layer Resizing Layer rdfs:subClassOf None \n",
- "869 aio:Resizing_Layer Resizing Layer rdfs:subClassOf None \n",
- "870 aio:Resizing_Layer Resizing Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "868 aio:Image_Preprocessing_Layer Image Preprocessing Layer \n",
- "869 aio:Layer Layer \n",
- "870 aio:Resizing_Layer Resizing Layer \n",
- "parent aio:Image_Preprocessing_Layer\n",
- "aio:Image_Preprocessing_Layer\n",
- " subject subject_label \\\n",
- "460 aio:Image_Preprocessing_Layer Image Preprocessing Layer \n",
- "461 aio:Image_Preprocessing_Layer Image Preprocessing Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "460 rdfs:subClassOf None aio:Image_Preprocessing_Layer \n",
- "461 rdfs:subClassOf None aio:Layer \n",
- "\n",
- " object_label \n",
- "460 Image Preprocessing Layer \n",
- "461 Layer \n",
- "parent aio:Image_Preprocessing_Layer\n",
- "aio:Resizing_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "868 aio:Resizing_Layer Resizing Layer rdfs:subClassOf None \n",
- "869 aio:Resizing_Layer Resizing Layer rdfs:subClassOf None \n",
- "870 aio:Resizing_Layer Resizing Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "868 aio:Image_Preprocessing_Layer Image Preprocessing Layer \n",
- "869 aio:Layer Layer \n",
- "870 aio:Resizing_Layer Resizing Layer \n",
- "parent aio:Image_Preprocessing_Layer\n",
- "aio:Image_Preprocessing_Layer\n",
- " subject subject_label \\\n",
- "460 aio:Image_Preprocessing_Layer Image Preprocessing Layer \n",
- "461 aio:Image_Preprocessing_Layer Image Preprocessing Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "460 rdfs:subClassOf None aio:Image_Preprocessing_Layer \n",
- "461 rdfs:subClassOf None aio:Layer \n",
- "\n",
- " object_label \n",
- "460 Image Preprocessing Layer \n",
- "461 Layer \n",
- "parent aio:Image_Preprocessing_Layer\n",
- "aio:Ridge_Regression\n",
- " subject subject_label predicate predicate_label \\\n",
- "871 aio:Ridge_Regression Ridge Regression rdfs:subClassOf None \n",
- "872 aio:Ridge_Regression Ridge Regression rdfs:subClassOf None \n",
- "873 aio:Ridge_Regression Ridge Regression rdfs:subClassOf None \n",
- "874 aio:Ridge_Regression Ridge Regression rdfs:subClassOf None \n",
- "875 aio:Ridge_Regression Ridge Regression rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "871 aio:Machine_Learning Machine Learning \n",
- "872 aio:Method Method \n",
- "873 aio:Regression_Analysis Regression Analysis \n",
- "874 aio:Ridge_Regression Ridge Regression \n",
- "875 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Ridge_Regression\n",
- " subject subject_label predicate predicate_label \\\n",
- "871 aio:Ridge_Regression Ridge Regression rdfs:subClassOf None \n",
- "872 aio:Ridge_Regression Ridge Regression rdfs:subClassOf None \n",
- "873 aio:Ridge_Regression Ridge Regression rdfs:subClassOf None \n",
- "874 aio:Ridge_Regression Ridge Regression rdfs:subClassOf None \n",
- "875 aio:Ridge_Regression Ridge Regression rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "871 aio:Machine_Learning Machine Learning \n",
- "872 aio:Method Method \n",
- "873 aio:Regression_Analysis Regression Analysis \n",
- "874 aio:Ridge_Regression Ridge Regression \n",
- "875 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Ridge_Regression\n",
- " subject subject_label predicate predicate_label \\\n",
- "871 aio:Ridge_Regression Ridge Regression rdfs:subClassOf None \n",
- "872 aio:Ridge_Regression Ridge Regression rdfs:subClassOf None \n",
- "873 aio:Ridge_Regression Ridge Regression rdfs:subClassOf None \n",
- "874 aio:Ridge_Regression Ridge Regression rdfs:subClassOf None \n",
- "875 aio:Ridge_Regression Ridge Regression rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "871 aio:Machine_Learning Machine Learning \n",
- "872 aio:Method Method \n",
- "873 aio:Regression_Analysis Regression Analysis \n",
- "874 aio:Ridge_Regression Ridge Regression \n",
- "875 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Ridge_Regression\n",
- " subject subject_label predicate predicate_label \\\n",
- "871 aio:Ridge_Regression Ridge Regression rdfs:subClassOf None \n",
- "872 aio:Ridge_Regression Ridge Regression rdfs:subClassOf None \n",
- "873 aio:Ridge_Regression Ridge Regression rdfs:subClassOf None \n",
- "874 aio:Ridge_Regression Ridge Regression rdfs:subClassOf None \n",
- "875 aio:Ridge_Regression Ridge Regression rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "871 aio:Machine_Learning Machine Learning \n",
- "872 aio:Method Method \n",
- "873 aio:Regression_Analysis Regression Analysis \n",
- "874 aio:Ridge_Regression Ridge Regression \n",
- "875 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Ridge_Regression\n",
- " subject subject_label predicate predicate_label \\\n",
- "871 aio:Ridge_Regression Ridge Regression rdfs:subClassOf None \n",
- "872 aio:Ridge_Regression Ridge Regression rdfs:subClassOf None \n",
- "873 aio:Ridge_Regression Ridge Regression rdfs:subClassOf None \n",
- "874 aio:Ridge_Regression Ridge Regression rdfs:subClassOf None \n",
- "875 aio:Ridge_Regression Ridge Regression rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "871 aio:Machine_Learning Machine Learning \n",
- "872 aio:Method Method \n",
- "873 aio:Regression_Analysis Regression Analysis \n",
- "874 aio:Ridge_Regression Ridge Regression \n",
- "875 aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:SAE\n",
- " subject subject_label predicate predicate_label object \\\n",
- "876 aio:SAE Sparse AE rdfs:subClassOf None aio:AE \n",
- "877 aio:SAE Sparse AE rdfs:subClassOf None aio:Network \n",
- "878 aio:SAE Sparse AE rdfs:subClassOf None aio:SAE \n",
- "879 aio:SAE Sparse AE rdfs:subClassOf None aio:UPN \n",
- "\n",
- " object_label \n",
- "876 Auto Encoder Network \n",
- "877 Network \n",
- "878 Sparse AE \n",
- "879 Unsupervised Pretrained Network \n",
- "parent aio:AE\n",
- "aio:AE\n",
- " subject subject_label predicate predicate_label object \\\n",
- "0 aio:AE Auto Encoder Network rdfs:subClassOf None aio:AE \n",
- "1 aio:AE Auto Encoder Network rdfs:subClassOf None aio:Network \n",
- "2 aio:AE Auto Encoder Network rdfs:subClassOf None aio:UPN \n",
- "\n",
- " object_label \n",
- "0 Auto Encoder Network \n",
- "1 Network \n",
- "2 Unsupervised Pretrained Network \n",
- "parent aio:AE\n",
- "aio:SAE\n",
- " subject subject_label predicate predicate_label object \\\n",
- "876 aio:SAE Sparse AE rdfs:subClassOf None aio:AE \n",
- "877 aio:SAE Sparse AE rdfs:subClassOf None aio:Network \n",
- "878 aio:SAE Sparse AE rdfs:subClassOf None aio:SAE \n",
- "879 aio:SAE Sparse AE rdfs:subClassOf None aio:UPN \n",
- "\n",
- " object_label \n",
- "876 Auto Encoder Network \n",
- "877 Network \n",
- "878 Sparse AE \n",
- "879 Unsupervised Pretrained Network \n",
- "parent aio:AE\n",
- "aio:AE\n",
- " subject subject_label predicate predicate_label object \\\n",
- "0 aio:AE Auto Encoder Network rdfs:subClassOf None aio:AE \n",
- "1 aio:AE Auto Encoder Network rdfs:subClassOf None aio:Network \n",
- "2 aio:AE Auto Encoder Network rdfs:subClassOf None aio:UPN \n",
- "\n",
- " object_label \n",
- "0 Auto Encoder Network \n",
- "1 Network \n",
- "2 Unsupervised Pretrained Network \n",
- "parent aio:AE\n",
- "aio:SAE\n",
- " subject subject_label predicate predicate_label object \\\n",
- "876 aio:SAE Sparse AE rdfs:subClassOf None aio:AE \n",
- "877 aio:SAE Sparse AE rdfs:subClassOf None aio:Network \n",
- "878 aio:SAE Sparse AE rdfs:subClassOf None aio:SAE \n",
- "879 aio:SAE Sparse AE rdfs:subClassOf None aio:UPN \n",
- "\n",
- " object_label \n",
- "876 Auto Encoder Network \n",
- "877 Network \n",
- "878 Sparse AE \n",
- "879 Unsupervised Pretrained Network \n",
- "parent aio:AE\n",
- "aio:AE\n",
- " subject subject_label predicate predicate_label object \\\n",
- "0 aio:AE Auto Encoder Network rdfs:subClassOf None aio:AE \n",
- "1 aio:AE Auto Encoder Network rdfs:subClassOf None aio:Network \n",
- "2 aio:AE Auto Encoder Network rdfs:subClassOf None aio:UPN \n",
- "\n",
- " object_label \n",
- "0 Auto Encoder Network \n",
- "1 Network \n",
- "2 Unsupervised Pretrained Network \n",
- "parent aio:AE\n",
- "aio:SAE\n",
- " subject subject_label predicate predicate_label object \\\n",
- "876 aio:SAE Sparse AE rdfs:subClassOf None aio:AE \n",
- "877 aio:SAE Sparse AE rdfs:subClassOf None aio:Network \n",
- "878 aio:SAE Sparse AE rdfs:subClassOf None aio:SAE \n",
- "879 aio:SAE Sparse AE rdfs:subClassOf None aio:UPN \n",
- "\n",
- " object_label \n",
- "876 Auto Encoder Network \n",
- "877 Network \n",
- "878 Sparse AE \n",
- "879 Unsupervised Pretrained Network \n",
- "parent aio:AE\n",
- "aio:AE\n",
- " subject subject_label predicate predicate_label object \\\n",
- "0 aio:AE Auto Encoder Network rdfs:subClassOf None aio:AE \n",
- "1 aio:AE Auto Encoder Network rdfs:subClassOf None aio:Network \n",
- "2 aio:AE Auto Encoder Network rdfs:subClassOf None aio:UPN \n",
- "\n",
- " object_label \n",
- "0 Auto Encoder Network \n",
- "1 Network \n",
- "2 Unsupervised Pretrained Network \n",
- "parent aio:AE\n",
- "aio:SCN\n",
- " subject subject_label predicate \\\n",
- "880 aio:SCN Symmetrically Connected Network rdfs:subClassOf \n",
- "881 aio:SCN Symmetrically Connected Network rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "880 None aio:Network Network \n",
- "881 None aio:SCN Symmetrically Connected Network \n",
- "parent aio:Network\n",
- "aio:SCN\n",
- " subject subject_label predicate \\\n",
- "880 aio:SCN Symmetrically Connected Network rdfs:subClassOf \n",
- "881 aio:SCN Symmetrically Connected Network rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "880 None aio:Network Network \n",
- "881 None aio:SCN Symmetrically Connected Network \n",
- "parent aio:Network\n",
- "aio:SVM\n",
- " subject subject_label predicate predicate_label \\\n",
- "882 aio:SVM Support Vector Machine rdfs:subClassOf None \n",
- "883 aio:SVM Support Vector Machine rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "882 aio:Network Network \n",
- "883 aio:SVM Support Vector Machine \n",
- "parent aio:Network\n",
- "aio:SVM\n",
- " subject subject_label predicate predicate_label \\\n",
- "882 aio:SVM Support Vector Machine rdfs:subClassOf None \n",
- "883 aio:SVM Support Vector Machine rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "882 aio:Network Network \n",
- "883 aio:SVM Support Vector Machine \n",
- "parent aio:Network\n",
- "aio:SeLu_Function\n",
- " subject subject_label predicate predicate_label \\\n",
- "884 aio:SeLu_Function SELU Function rdfs:subClassOf None \n",
- "885 aio:SeLu_Function SELU Function rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "884 aio:Function None \n",
- "885 aio:SeLu_Function SELU Function \n",
- "parent aio:Function\n",
- "aio:SeLu_Function\n",
- " subject subject_label predicate predicate_label \\\n",
- "884 aio:SeLu_Function SELU Function rdfs:subClassOf None \n",
- "885 aio:SeLu_Function SELU Function rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "884 aio:Function None \n",
- "885 aio:SeLu_Function SELU Function \n",
- "parent aio:Function\n",
- "aio:Selection_And_Sampling_Bias\n",
- " subject subject_label \\\n",
- "886 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "887 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "888 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "\n",
- " predicate predicate_label object \\\n",
- "886 rdfs:subClassOf None aio:Bias \n",
- "887 rdfs:subClassOf None aio:Computational_Bias \n",
- "888 rdfs:subClassOf None aio:Selection_And_Sampling_Bias \n",
- "\n",
- " object_label \n",
- "886 Bias \n",
- "887 Computational Bias \n",
- "888 Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Selection_And_Sampling_Bias\n",
- " subject subject_label \\\n",
- "886 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "887 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "888 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "\n",
- " predicate predicate_label object \\\n",
- "886 rdfs:subClassOf None aio:Bias \n",
- "887 rdfs:subClassOf None aio:Computational_Bias \n",
- "888 rdfs:subClassOf None aio:Selection_And_Sampling_Bias \n",
- "\n",
- " object_label \n",
- "886 Bias \n",
- "887 Computational Bias \n",
- "888 Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Selection_And_Sampling_Bias\n",
- " subject subject_label \\\n",
- "886 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "887 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "888 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "\n",
- " predicate predicate_label object \\\n",
- "886 rdfs:subClassOf None aio:Bias \n",
- "887 rdfs:subClassOf None aio:Computational_Bias \n",
- "888 rdfs:subClassOf None aio:Selection_And_Sampling_Bias \n",
- "\n",
- " object_label \n",
- "886 Bias \n",
- "887 Computational Bias \n",
- "888 Selection And Sampling Bias \n",
- "parent aio:Bias\n",
- "aio:Selective_Adherence_Bias\n",
- " subject subject_label predicate \\\n",
- "889 aio:Selective_Adherence_Bias Selective Adherence Bias rdfs:subClassOf \n",
- "890 aio:Selective_Adherence_Bias Selective Adherence Bias rdfs:subClassOf \n",
- "891 aio:Selective_Adherence_Bias Selective Adherence Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "889 None aio:Bias Bias \n",
- "890 None aio:Individual_Bias Individual Bias \n",
- "891 None aio:Selective_Adherence_Bias Selective Adherence Bias \n",
- "parent aio:Bias\n",
- "aio:Selective_Adherence_Bias\n",
- " subject subject_label predicate \\\n",
- "889 aio:Selective_Adherence_Bias Selective Adherence Bias rdfs:subClassOf \n",
- "890 aio:Selective_Adherence_Bias Selective Adherence Bias rdfs:subClassOf \n",
- "891 aio:Selective_Adherence_Bias Selective Adherence Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "889 None aio:Bias Bias \n",
- "890 None aio:Individual_Bias Individual Bias \n",
- "891 None aio:Selective_Adherence_Bias Selective Adherence Bias \n",
- "parent aio:Bias\n",
- "aio:Selective_Adherence_Bias\n",
- " subject subject_label predicate \\\n",
- "889 aio:Selective_Adherence_Bias Selective Adherence Bias rdfs:subClassOf \n",
- "890 aio:Selective_Adherence_Bias Selective Adherence Bias rdfs:subClassOf \n",
- "891 aio:Selective_Adherence_Bias Selective Adherence Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "889 None aio:Bias Bias \n",
- "890 None aio:Individual_Bias Individual Bias \n",
- "891 None aio:Selective_Adherence_Bias Selective Adherence Bias \n",
- "parent aio:Bias\n",
- "aio:Self-supervised_Learning\n",
- " subject subject_label predicate \\\n",
- "892 aio:Self-supervised_Learning Self-supervised Learning rdfs:subClassOf \n",
- "893 aio:Self-supervised_Learning Self-supervised Learning rdfs:subClassOf \n",
- "894 aio:Self-supervised_Learning Self-supervised Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "892 None aio:Machine_Learning Machine Learning \n",
- "893 None aio:Method Method \n",
- "894 None aio:Self-supervised_Learning Self-supervised Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Self-supervised_Learning\n",
- " subject subject_label predicate \\\n",
- "892 aio:Self-supervised_Learning Self-supervised Learning rdfs:subClassOf \n",
- "893 aio:Self-supervised_Learning Self-supervised Learning rdfs:subClassOf \n",
- "894 aio:Self-supervised_Learning Self-supervised Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "892 None aio:Machine_Learning Machine Learning \n",
- "893 None aio:Method Method \n",
- "894 None aio:Self-supervised_Learning Self-supervised Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Self-supervised_Learning\n",
- " subject subject_label predicate \\\n",
- "892 aio:Self-supervised_Learning Self-supervised Learning rdfs:subClassOf \n",
- "893 aio:Self-supervised_Learning Self-supervised Learning rdfs:subClassOf \n",
- "894 aio:Self-supervised_Learning Self-supervised Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "892 None aio:Machine_Learning Machine Learning \n",
- "893 None aio:Method Method \n",
- "894 None aio:Self-supervised_Learning Self-supervised Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:SeparableConvolution1D_Layer\n",
- " subject subject_label \\\n",
- "895 aio:SeparableConvolution1D_Layer SeparableConvolution1D Layer \n",
- "896 aio:SeparableConvolution1D_Layer SeparableConvolution1D Layer \n",
- "897 aio:SeparableConvolution1D_Layer SeparableConvolution1D Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "895 rdfs:subClassOf None aio:Convolutional_Layer \n",
- "896 rdfs:subClassOf None aio:Layer \n",
- "897 rdfs:subClassOf None aio:SeparableConvolution1D_Layer \n",
- "\n",
- " object_label \n",
- "895 Convolutional Layer \n",
- "896 Layer \n",
- "897 SeparableConvolution1D Layer \n",
- "parent aio:Convolutional_Layer\n",
- "aio:Convolutional_Layer\n",
- " subject subject_label predicate \\\n",
- "198 aio:Convolutional_Layer Convolutional Layer rdfs:subClassOf \n",
- "199 aio:Convolutional_Layer Convolutional Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "198 None aio:Convolutional_Layer Convolutional Layer \n",
- "199 None aio:Layer Layer \n",
- "parent aio:Convolutional_Layer\n",
- "aio:SeparableConvolution1D_Layer\n",
- " subject subject_label \\\n",
- "895 aio:SeparableConvolution1D_Layer SeparableConvolution1D Layer \n",
- "896 aio:SeparableConvolution1D_Layer SeparableConvolution1D Layer \n",
- "897 aio:SeparableConvolution1D_Layer SeparableConvolution1D Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "895 rdfs:subClassOf None aio:Convolutional_Layer \n",
- "896 rdfs:subClassOf None aio:Layer \n",
- "897 rdfs:subClassOf None aio:SeparableConvolution1D_Layer \n",
- "\n",
- " object_label \n",
- "895 Convolutional Layer \n",
- "896 Layer \n",
- "897 SeparableConvolution1D Layer \n",
- "parent aio:Convolutional_Layer\n",
- "aio:Convolutional_Layer\n",
- " subject subject_label predicate \\\n",
- "198 aio:Convolutional_Layer Convolutional Layer rdfs:subClassOf \n",
- "199 aio:Convolutional_Layer Convolutional Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "198 None aio:Convolutional_Layer Convolutional Layer \n",
- "199 None aio:Layer Layer \n",
- "parent aio:Convolutional_Layer\n",
- "aio:SeparableConvolution1D_Layer\n",
- " subject subject_label \\\n",
- "895 aio:SeparableConvolution1D_Layer SeparableConvolution1D Layer \n",
- "896 aio:SeparableConvolution1D_Layer SeparableConvolution1D Layer \n",
- "897 aio:SeparableConvolution1D_Layer SeparableConvolution1D Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "895 rdfs:subClassOf None aio:Convolutional_Layer \n",
- "896 rdfs:subClassOf None aio:Layer \n",
- "897 rdfs:subClassOf None aio:SeparableConvolution1D_Layer \n",
- "\n",
- " object_label \n",
- "895 Convolutional Layer \n",
- "896 Layer \n",
- "897 SeparableConvolution1D Layer \n",
- "parent aio:Convolutional_Layer\n",
- "aio:Convolutional_Layer\n",
- " subject subject_label predicate \\\n",
- "198 aio:Convolutional_Layer Convolutional Layer rdfs:subClassOf \n",
- "199 aio:Convolutional_Layer Convolutional Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "198 None aio:Convolutional_Layer Convolutional Layer \n",
- "199 None aio:Layer Layer \n",
- "parent aio:Convolutional_Layer\n",
- "aio:SeparableConvolution2D_Layer\n",
- " subject subject_label \\\n",
- "898 aio:SeparableConvolution2D_Layer SeparableConvolution2D Layer \n",
- "899 aio:SeparableConvolution2D_Layer SeparableConvolution2D Layer \n",
- "900 aio:SeparableConvolution2D_Layer SeparableConvolution2D Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "898 rdfs:subClassOf None aio:Convolutional_Layer \n",
- "899 rdfs:subClassOf None aio:Layer \n",
- "900 rdfs:subClassOf None aio:SeparableConvolution2D_Layer \n",
- "\n",
- " object_label \n",
- "898 Convolutional Layer \n",
- "899 Layer \n",
- "900 SeparableConvolution2D Layer \n",
- "parent aio:Convolutional_Layer\n",
- "aio:Convolutional_Layer\n",
- " subject subject_label predicate \\\n",
- "198 aio:Convolutional_Layer Convolutional Layer rdfs:subClassOf \n",
- "199 aio:Convolutional_Layer Convolutional Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "198 None aio:Convolutional_Layer Convolutional Layer \n",
- "199 None aio:Layer Layer \n",
- "parent aio:Convolutional_Layer\n",
- "aio:SeparableConvolution2D_Layer\n",
- " subject subject_label \\\n",
- "898 aio:SeparableConvolution2D_Layer SeparableConvolution2D Layer \n",
- "899 aio:SeparableConvolution2D_Layer SeparableConvolution2D Layer \n",
- "900 aio:SeparableConvolution2D_Layer SeparableConvolution2D Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "898 rdfs:subClassOf None aio:Convolutional_Layer \n",
- "899 rdfs:subClassOf None aio:Layer \n",
- "900 rdfs:subClassOf None aio:SeparableConvolution2D_Layer \n",
- "\n",
- " object_label \n",
- "898 Convolutional Layer \n",
- "899 Layer \n",
- "900 SeparableConvolution2D Layer \n",
- "parent aio:Convolutional_Layer\n",
- "aio:Convolutional_Layer\n",
- " subject subject_label predicate \\\n",
- "198 aio:Convolutional_Layer Convolutional Layer rdfs:subClassOf \n",
- "199 aio:Convolutional_Layer Convolutional Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "198 None aio:Convolutional_Layer Convolutional Layer \n",
- "199 None aio:Layer Layer \n",
- "parent aio:Convolutional_Layer\n",
- "aio:SeparableConvolution2D_Layer\n",
- " subject subject_label \\\n",
- "898 aio:SeparableConvolution2D_Layer SeparableConvolution2D Layer \n",
- "899 aio:SeparableConvolution2D_Layer SeparableConvolution2D Layer \n",
- "900 aio:SeparableConvolution2D_Layer SeparableConvolution2D Layer \n",
- "\n",
- " predicate predicate_label object \\\n",
- "898 rdfs:subClassOf None aio:Convolutional_Layer \n",
- "899 rdfs:subClassOf None aio:Layer \n",
- "900 rdfs:subClassOf None aio:SeparableConvolution2D_Layer \n",
- "\n",
- " object_label \n",
- "898 Convolutional Layer \n",
- "899 Layer \n",
- "900 SeparableConvolution2D Layer \n",
- "parent aio:Convolutional_Layer\n",
- "aio:Convolutional_Layer\n",
- " subject subject_label predicate \\\n",
- "198 aio:Convolutional_Layer Convolutional Layer rdfs:subClassOf \n",
- "199 aio:Convolutional_Layer Convolutional Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "198 None aio:Convolutional_Layer Convolutional Layer \n",
- "199 None aio:Layer Layer \n",
- "parent aio:Convolutional_Layer\n",
- "aio:Sigmoid_Function\n",
- " subject subject_label predicate predicate_label \\\n",
- "901 aio:Sigmoid_Function Sigmoid Function rdfs:subClassOf None \n",
- "902 aio:Sigmoid_Function Sigmoid Function rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "901 aio:Function None \n",
- "902 aio:Sigmoid_Function Sigmoid Function \n",
- "parent aio:Function\n",
- "aio:Sigmoid_Function\n",
- " subject subject_label predicate predicate_label \\\n",
- "901 aio:Sigmoid_Function Sigmoid Function rdfs:subClassOf None \n",
- "902 aio:Sigmoid_Function Sigmoid Function rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "901 aio:Function None \n",
- "902 aio:Sigmoid_Function Sigmoid Function \n",
- "parent aio:Function\n",
- "aio:SimpleRNNCell_Layer\n",
- " subject subject_label predicate \\\n",
- "903 aio:SimpleRNNCell_Layer SimpleRNNCell Layer rdfs:subClassOf \n",
- "904 aio:SimpleRNNCell_Layer SimpleRNNCell Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "903 None aio:Layer Layer \n",
- "904 None aio:SimpleRNNCell_Layer SimpleRNNCell Layer \n",
- "parent aio:Layer\n",
- "aio:SimpleRNNCell_Layer\n",
- " subject subject_label predicate \\\n",
- "903 aio:SimpleRNNCell_Layer SimpleRNNCell Layer rdfs:subClassOf \n",
- "904 aio:SimpleRNNCell_Layer SimpleRNNCell Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "903 None aio:Layer Layer \n",
- "904 None aio:SimpleRNNCell_Layer SimpleRNNCell Layer \n",
- "parent aio:Layer\n",
- "aio:SimpleRNN_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "905 aio:SimpleRNN_Layer SimpleRNN Layer rdfs:subClassOf None \n",
- "906 aio:SimpleRNN_Layer SimpleRNN Layer rdfs:subClassOf None \n",
- "907 aio:SimpleRNN_Layer SimpleRNN Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "905 aio:Layer Layer \n",
- "906 aio:Recurrent_Layer Recurrent Layer \n",
- "907 aio:SimpleRNN_Layer SimpleRNN Layer \n",
- "parent aio:Layer\n",
- "aio:SimpleRNN_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "905 aio:SimpleRNN_Layer SimpleRNN Layer rdfs:subClassOf None \n",
- "906 aio:SimpleRNN_Layer SimpleRNN Layer rdfs:subClassOf None \n",
- "907 aio:SimpleRNN_Layer SimpleRNN Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "905 aio:Layer Layer \n",
- "906 aio:Recurrent_Layer Recurrent Layer \n",
- "907 aio:SimpleRNN_Layer SimpleRNN Layer \n",
- "parent aio:Layer\n",
- "aio:SimpleRNN_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "905 aio:SimpleRNN_Layer SimpleRNN Layer rdfs:subClassOf None \n",
- "906 aio:SimpleRNN_Layer SimpleRNN Layer rdfs:subClassOf None \n",
- "907 aio:SimpleRNN_Layer SimpleRNN Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "905 aio:Layer Layer \n",
- "906 aio:Recurrent_Layer Recurrent Layer \n",
- "907 aio:SimpleRNN_Layer SimpleRNN Layer \n",
- "parent aio:Layer\n",
- "aio:Simpon's_Paradox_Bias\n",
- " subject subject_label predicate \\\n",
- "908 aio:Simpon's_Paradox_Bias Simpon's Paradox Bias rdfs:subClassOf \n",
- "909 aio:Simpon's_Paradox_Bias Simpon's Paradox Bias rdfs:subClassOf \n",
- "910 aio:Simpon's_Paradox_Bias Simpon's Paradox Bias rdfs:subClassOf \n",
- "911 aio:Simpon's_Paradox_Bias Simpon's Paradox Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "908 None aio:Bias \n",
- "909 None aio:Computational_Bias \n",
- "910 None aio:Selection_And_Sampling_Bias \n",
- "911 None aio:Simpon's_Paradox_Bias \n",
- "\n",
- " object_label \n",
- "908 Bias \n",
- "909 Computational Bias \n",
- "910 Selection And Sampling Bias \n",
- "911 Simpon's Paradox Bias \n",
- "parent aio:Bias\n",
- "aio:Simpon's_Paradox_Bias\n",
- " subject subject_label predicate \\\n",
- "908 aio:Simpon's_Paradox_Bias Simpon's Paradox Bias rdfs:subClassOf \n",
- "909 aio:Simpon's_Paradox_Bias Simpon's Paradox Bias rdfs:subClassOf \n",
- "910 aio:Simpon's_Paradox_Bias Simpon's Paradox Bias rdfs:subClassOf \n",
- "911 aio:Simpon's_Paradox_Bias Simpon's Paradox Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "908 None aio:Bias \n",
- "909 None aio:Computational_Bias \n",
- "910 None aio:Selection_And_Sampling_Bias \n",
- "911 None aio:Simpon's_Paradox_Bias \n",
- "\n",
- " object_label \n",
- "908 Bias \n",
- "909 Computational Bias \n",
- "910 Selection And Sampling Bias \n",
- "911 Simpon's Paradox Bias \n",
- "parent aio:Bias\n",
- "aio:Simpon's_Paradox_Bias\n",
- " subject subject_label predicate \\\n",
- "908 aio:Simpon's_Paradox_Bias Simpon's Paradox Bias rdfs:subClassOf \n",
- "909 aio:Simpon's_Paradox_Bias Simpon's Paradox Bias rdfs:subClassOf \n",
- "910 aio:Simpon's_Paradox_Bias Simpon's Paradox Bias rdfs:subClassOf \n",
- "911 aio:Simpon's_Paradox_Bias Simpon's Paradox Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "908 None aio:Bias \n",
- "909 None aio:Computational_Bias \n",
- "910 None aio:Selection_And_Sampling_Bias \n",
- "911 None aio:Simpon's_Paradox_Bias \n",
- "\n",
- " object_label \n",
- "908 Bias \n",
- "909 Computational Bias \n",
- "910 Selection And Sampling Bias \n",
- "911 Simpon's Paradox Bias \n",
- "parent aio:Bias\n",
- "aio:Simpon's_Paradox_Bias\n",
- " subject subject_label predicate \\\n",
- "908 aio:Simpon's_Paradox_Bias Simpon's Paradox Bias rdfs:subClassOf \n",
- "909 aio:Simpon's_Paradox_Bias Simpon's Paradox Bias rdfs:subClassOf \n",
- "910 aio:Simpon's_Paradox_Bias Simpon's Paradox Bias rdfs:subClassOf \n",
- "911 aio:Simpon's_Paradox_Bias Simpon's Paradox Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "908 None aio:Bias \n",
- "909 None aio:Computational_Bias \n",
- "910 None aio:Selection_And_Sampling_Bias \n",
- "911 None aio:Simpon's_Paradox_Bias \n",
- "\n",
- " object_label \n",
- "908 Bias \n",
- "909 Computational Bias \n",
- "910 Selection And Sampling Bias \n",
- "911 Simpon's Paradox Bias \n",
- "parent aio:Bias\n",
- "aio:Societal_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "912 aio:Societal_Bias Societal Bias rdfs:subClassOf None \n",
- "913 aio:Societal_Bias Societal Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "912 aio:Bias Bias \n",
- "913 aio:Societal_Bias Societal Bias \n",
- "parent aio:Bias\n",
- "aio:Societal_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "912 aio:Societal_Bias Societal Bias rdfs:subClassOf None \n",
- "913 aio:Societal_Bias Societal Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "912 aio:Bias Bias \n",
- "913 aio:Societal_Bias Societal Bias \n",
- "parent aio:Bias\n",
- "aio:Softmax_Function\n",
- " subject subject_label predicate predicate_label \\\n",
- "914 aio:Softmax_Function Softmax Function rdfs:subClassOf None \n",
- "915 aio:Softmax_Function Softmax Function rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "914 aio:Function None \n",
- "915 aio:Softmax_Function Softmax Function \n",
- "parent aio:Function\n",
- "aio:Softmax_Function\n",
- " subject subject_label predicate predicate_label \\\n",
- "914 aio:Softmax_Function Softmax Function rdfs:subClassOf None \n",
- "915 aio:Softmax_Function Softmax Function rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "914 aio:Function None \n",
- "915 aio:Softmax_Function Softmax Function \n",
- "parent aio:Function\n",
- "aio:Softmax_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "916 aio:Softmax_Layer Softmax Layer rdfs:subClassOf None \n",
- "917 aio:Softmax_Layer Softmax Layer rdfs:subClassOf None \n",
- "918 aio:Softmax_Layer Softmax Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "916 aio:Activation_Layer Activation Layer \n",
- "917 aio:Layer Layer \n",
- "918 aio:Softmax_Layer Softmax Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:Activation_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "7 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "8 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "7 aio:Activation_Layer Activation Layer \n",
- "8 aio:Layer Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:Softmax_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "916 aio:Softmax_Layer Softmax Layer rdfs:subClassOf None \n",
- "917 aio:Softmax_Layer Softmax Layer rdfs:subClassOf None \n",
- "918 aio:Softmax_Layer Softmax Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "916 aio:Activation_Layer Activation Layer \n",
- "917 aio:Layer Layer \n",
- "918 aio:Softmax_Layer Softmax Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:Activation_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "7 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "8 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "7 aio:Activation_Layer Activation Layer \n",
- "8 aio:Layer Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:Softmax_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "916 aio:Softmax_Layer Softmax Layer rdfs:subClassOf None \n",
- "917 aio:Softmax_Layer Softmax Layer rdfs:subClassOf None \n",
- "918 aio:Softmax_Layer Softmax Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "916 aio:Activation_Layer Activation Layer \n",
- "917 aio:Layer Layer \n",
- "918 aio:Softmax_Layer Softmax Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:Activation_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "7 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "8 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "7 aio:Activation_Layer Activation Layer \n",
- "8 aio:Layer Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:Softplus_Function\n",
- " subject subject_label predicate \\\n",
- "919 aio:Softplus_Function Softplus Function rdfs:subClassOf \n",
- "920 aio:Softplus_Function Softplus Function rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "919 None aio:Function None \n",
- "920 None aio:Softplus_Function Softplus Function \n",
- "parent aio:Function\n",
- "aio:Softplus_Function\n",
- " subject subject_label predicate \\\n",
- "919 aio:Softplus_Function Softplus Function rdfs:subClassOf \n",
- "920 aio:Softplus_Function Softplus Function rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "919 None aio:Function None \n",
- "920 None aio:Softplus_Function Softplus Function \n",
- "parent aio:Function\n",
- "aio:Softsign_Function\n",
- " subject subject_label predicate \\\n",
- "921 aio:Softsign_Function Softsign Function rdfs:subClassOf \n",
- "922 aio:Softsign_Function Softsign Function rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "921 None aio:Function None \n",
- "922 None aio:Softsign_Function Softsign Function \n",
- "parent aio:Function\n",
- "aio:Softsign_Function\n",
- " subject subject_label predicate \\\n",
- "921 aio:Softsign_Function Softsign Function rdfs:subClassOf \n",
- "922 aio:Softsign_Function Softsign Function rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "921 None aio:Function None \n",
- "922 None aio:Softsign_Function Softsign Function \n",
- "parent aio:Function\n",
- "aio:Sparse_Learning\n",
- " subject subject_label predicate predicate_label \\\n",
- "923 aio:Sparse_Learning Sparse Learning rdfs:subClassOf None \n",
- "924 aio:Sparse_Learning Sparse Learning rdfs:subClassOf None \n",
- "925 aio:Sparse_Learning Sparse Learning rdfs:subClassOf None \n",
- "926 aio:Sparse_Learning Sparse Learning rdfs:subClassOf None \n",
- "927 aio:Sparse_Learning Sparse Learning rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "923 aio:ANN Artificial Neural Network \n",
- "924 aio:DNN Deep Neural Network \n",
- "925 aio:Network Network \n",
- "926 aio:Representation_Learning Representation Learning \n",
- "927 aio:Sparse_Learning Sparse Learning \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Sparse_Learning\n",
- " subject subject_label predicate predicate_label \\\n",
- "923 aio:Sparse_Learning Sparse Learning rdfs:subClassOf None \n",
- "924 aio:Sparse_Learning Sparse Learning rdfs:subClassOf None \n",
- "925 aio:Sparse_Learning Sparse Learning rdfs:subClassOf None \n",
- "926 aio:Sparse_Learning Sparse Learning rdfs:subClassOf None \n",
- "927 aio:Sparse_Learning Sparse Learning rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "923 aio:ANN Artificial Neural Network \n",
- "924 aio:DNN Deep Neural Network \n",
- "925 aio:Network Network \n",
- "926 aio:Representation_Learning Representation Learning \n",
- "927 aio:Sparse_Learning Sparse Learning \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Sparse_Learning\n",
- " subject subject_label predicate predicate_label \\\n",
- "923 aio:Sparse_Learning Sparse Learning rdfs:subClassOf None \n",
- "924 aio:Sparse_Learning Sparse Learning rdfs:subClassOf None \n",
- "925 aio:Sparse_Learning Sparse Learning rdfs:subClassOf None \n",
- "926 aio:Sparse_Learning Sparse Learning rdfs:subClassOf None \n",
- "927 aio:Sparse_Learning Sparse Learning rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "923 aio:ANN Artificial Neural Network \n",
- "924 aio:DNN Deep Neural Network \n",
- "925 aio:Network Network \n",
- "926 aio:Representation_Learning Representation Learning \n",
- "927 aio:Sparse_Learning Sparse Learning \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Sparse_Learning\n",
- " subject subject_label predicate predicate_label \\\n",
- "923 aio:Sparse_Learning Sparse Learning rdfs:subClassOf None \n",
- "924 aio:Sparse_Learning Sparse Learning rdfs:subClassOf None \n",
- "925 aio:Sparse_Learning Sparse Learning rdfs:subClassOf None \n",
- "926 aio:Sparse_Learning Sparse Learning rdfs:subClassOf None \n",
- "927 aio:Sparse_Learning Sparse Learning rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "923 aio:ANN Artificial Neural Network \n",
- "924 aio:DNN Deep Neural Network \n",
- "925 aio:Network Network \n",
- "926 aio:Representation_Learning Representation Learning \n",
- "927 aio:Sparse_Learning Sparse Learning \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Sparse_Learning\n",
- " subject subject_label predicate predicate_label \\\n",
- "923 aio:Sparse_Learning Sparse Learning rdfs:subClassOf None \n",
- "924 aio:Sparse_Learning Sparse Learning rdfs:subClassOf None \n",
- "925 aio:Sparse_Learning Sparse Learning rdfs:subClassOf None \n",
- "926 aio:Sparse_Learning Sparse Learning rdfs:subClassOf None \n",
- "927 aio:Sparse_Learning Sparse Learning rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "923 aio:ANN Artificial Neural Network \n",
- "924 aio:DNN Deep Neural Network \n",
- "925 aio:Network Network \n",
- "926 aio:Representation_Learning Representation Learning \n",
- "927 aio:Sparse_Learning Sparse Learning \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:SpatialDropout1D_Layer\n",
- " subject subject_label predicate \\\n",
- "928 aio:SpatialDropout1D_Layer SpatialDropout1D Layer rdfs:subClassOf \n",
- "929 aio:SpatialDropout1D_Layer SpatialDropout1D Layer rdfs:subClassOf \n",
- "930 aio:SpatialDropout1D_Layer SpatialDropout1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "928 None aio:Layer Layer \n",
- "929 None aio:Regularization_Layer Regularization Layer \n",
- "930 None aio:SpatialDropout1D_Layer SpatialDropout1D Layer \n",
- "parent aio:Layer\n",
- "aio:SpatialDropout1D_Layer\n",
- " subject subject_label predicate \\\n",
- "928 aio:SpatialDropout1D_Layer SpatialDropout1D Layer rdfs:subClassOf \n",
- "929 aio:SpatialDropout1D_Layer SpatialDropout1D Layer rdfs:subClassOf \n",
- "930 aio:SpatialDropout1D_Layer SpatialDropout1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "928 None aio:Layer Layer \n",
- "929 None aio:Regularization_Layer Regularization Layer \n",
- "930 None aio:SpatialDropout1D_Layer SpatialDropout1D Layer \n",
- "parent aio:Layer\n",
- "aio:SpatialDropout1D_Layer\n",
- " subject subject_label predicate \\\n",
- "928 aio:SpatialDropout1D_Layer SpatialDropout1D Layer rdfs:subClassOf \n",
- "929 aio:SpatialDropout1D_Layer SpatialDropout1D Layer rdfs:subClassOf \n",
- "930 aio:SpatialDropout1D_Layer SpatialDropout1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "928 None aio:Layer Layer \n",
- "929 None aio:Regularization_Layer Regularization Layer \n",
- "930 None aio:SpatialDropout1D_Layer SpatialDropout1D Layer \n",
- "parent aio:Layer\n",
- "aio:SpatialDropout2D_Layer\n",
- " subject subject_label predicate \\\n",
- "931 aio:SpatialDropout2D_Layer SpatialDropout2D Layer rdfs:subClassOf \n",
- "932 aio:SpatialDropout2D_Layer SpatialDropout2D Layer rdfs:subClassOf \n",
- "933 aio:SpatialDropout2D_Layer SpatialDropout2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "931 None aio:Layer Layer \n",
- "932 None aio:Regularization_Layer Regularization Layer \n",
- "933 None aio:SpatialDropout2D_Layer SpatialDropout2D Layer \n",
- "parent aio:Layer\n",
- "aio:SpatialDropout2D_Layer\n",
- " subject subject_label predicate \\\n",
- "931 aio:SpatialDropout2D_Layer SpatialDropout2D Layer rdfs:subClassOf \n",
- "932 aio:SpatialDropout2D_Layer SpatialDropout2D Layer rdfs:subClassOf \n",
- "933 aio:SpatialDropout2D_Layer SpatialDropout2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "931 None aio:Layer Layer \n",
- "932 None aio:Regularization_Layer Regularization Layer \n",
- "933 None aio:SpatialDropout2D_Layer SpatialDropout2D Layer \n",
- "parent aio:Layer\n",
- "aio:SpatialDropout2D_Layer\n",
- " subject subject_label predicate \\\n",
- "931 aio:SpatialDropout2D_Layer SpatialDropout2D Layer rdfs:subClassOf \n",
- "932 aio:SpatialDropout2D_Layer SpatialDropout2D Layer rdfs:subClassOf \n",
- "933 aio:SpatialDropout2D_Layer SpatialDropout2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "931 None aio:Layer Layer \n",
- "932 None aio:Regularization_Layer Regularization Layer \n",
- "933 None aio:SpatialDropout2D_Layer SpatialDropout2D Layer \n",
- "parent aio:Layer\n",
- "aio:SpatialDropout3D_Layer\n",
- " subject subject_label predicate \\\n",
- "934 aio:SpatialDropout3D_Layer SpatialDropout3D Layer rdfs:subClassOf \n",
- "935 aio:SpatialDropout3D_Layer SpatialDropout3D Layer rdfs:subClassOf \n",
- "936 aio:SpatialDropout3D_Layer SpatialDropout3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "934 None aio:Layer Layer \n",
- "935 None aio:Regularization_Layer Regularization Layer \n",
- "936 None aio:SpatialDropout3D_Layer SpatialDropout3D Layer \n",
- "parent aio:Layer\n",
- "aio:SpatialDropout3D_Layer\n",
- " subject subject_label predicate \\\n",
- "934 aio:SpatialDropout3D_Layer SpatialDropout3D Layer rdfs:subClassOf \n",
- "935 aio:SpatialDropout3D_Layer SpatialDropout3D Layer rdfs:subClassOf \n",
- "936 aio:SpatialDropout3D_Layer SpatialDropout3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "934 None aio:Layer Layer \n",
- "935 None aio:Regularization_Layer Regularization Layer \n",
- "936 None aio:SpatialDropout3D_Layer SpatialDropout3D Layer \n",
- "parent aio:Layer\n",
- "aio:SpatialDropout3D_Layer\n",
- " subject subject_label predicate \\\n",
- "934 aio:SpatialDropout3D_Layer SpatialDropout3D Layer rdfs:subClassOf \n",
- "935 aio:SpatialDropout3D_Layer SpatialDropout3D Layer rdfs:subClassOf \n",
- "936 aio:SpatialDropout3D_Layer SpatialDropout3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "934 None aio:Layer Layer \n",
- "935 None aio:Regularization_Layer Regularization Layer \n",
- "936 None aio:SpatialDropout3D_Layer SpatialDropout3D Layer \n",
- "parent aio:Layer\n",
- "aio:Spatial_Regression\n",
- " subject subject_label predicate \\\n",
- "937 aio:Spatial_Regression Spatial Regression rdfs:subClassOf \n",
- "938 aio:Spatial_Regression Spatial Regression rdfs:subClassOf \n",
- "939 aio:Spatial_Regression Spatial Regression rdfs:subClassOf \n",
- "940 aio:Spatial_Regression Spatial Regression rdfs:subClassOf \n",
- "941 aio:Spatial_Regression Spatial Regression rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "937 None aio:Machine_Learning Machine Learning \n",
- "938 None aio:Method Method \n",
- "939 None aio:Regression_Analysis Regression Analysis \n",
- "940 None aio:Spatial_Regression Spatial Regression \n",
- "941 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Spatial_Regression\n",
- " subject subject_label predicate \\\n",
- "937 aio:Spatial_Regression Spatial Regression rdfs:subClassOf \n",
- "938 aio:Spatial_Regression Spatial Regression rdfs:subClassOf \n",
- "939 aio:Spatial_Regression Spatial Regression rdfs:subClassOf \n",
- "940 aio:Spatial_Regression Spatial Regression rdfs:subClassOf \n",
- "941 aio:Spatial_Regression Spatial Regression rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "937 None aio:Machine_Learning Machine Learning \n",
- "938 None aio:Method Method \n",
- "939 None aio:Regression_Analysis Regression Analysis \n",
- "940 None aio:Spatial_Regression Spatial Regression \n",
- "941 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Spatial_Regression\n",
- " subject subject_label predicate \\\n",
- "937 aio:Spatial_Regression Spatial Regression rdfs:subClassOf \n",
- "938 aio:Spatial_Regression Spatial Regression rdfs:subClassOf \n",
- "939 aio:Spatial_Regression Spatial Regression rdfs:subClassOf \n",
- "940 aio:Spatial_Regression Spatial Regression rdfs:subClassOf \n",
- "941 aio:Spatial_Regression Spatial Regression rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "937 None aio:Machine_Learning Machine Learning \n",
- "938 None aio:Method Method \n",
- "939 None aio:Regression_Analysis Regression Analysis \n",
- "940 None aio:Spatial_Regression Spatial Regression \n",
- "941 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Spatial_Regression\n",
- " subject subject_label predicate \\\n",
- "937 aio:Spatial_Regression Spatial Regression rdfs:subClassOf \n",
- "938 aio:Spatial_Regression Spatial Regression rdfs:subClassOf \n",
- "939 aio:Spatial_Regression Spatial Regression rdfs:subClassOf \n",
- "940 aio:Spatial_Regression Spatial Regression rdfs:subClassOf \n",
- "941 aio:Spatial_Regression Spatial Regression rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "937 None aio:Machine_Learning Machine Learning \n",
- "938 None aio:Method Method \n",
- "939 None aio:Regression_Analysis Regression Analysis \n",
- "940 None aio:Spatial_Regression Spatial Regression \n",
- "941 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Spatial_Regression\n",
- " subject subject_label predicate \\\n",
- "937 aio:Spatial_Regression Spatial Regression rdfs:subClassOf \n",
- "938 aio:Spatial_Regression Spatial Regression rdfs:subClassOf \n",
- "939 aio:Spatial_Regression Spatial Regression rdfs:subClassOf \n",
- "940 aio:Spatial_Regression Spatial Regression rdfs:subClassOf \n",
- "941 aio:Spatial_Regression Spatial Regression rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "937 None aio:Machine_Learning Machine Learning \n",
- "938 None aio:Method Method \n",
- "939 None aio:Regression_Analysis Regression Analysis \n",
- "940 None aio:Spatial_Regression Spatial Regression \n",
- "941 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:StackedRNNCells_Layer\n",
- " subject subject_label predicate \\\n",
- "942 aio:StackedRNNCells_Layer StackedRNNCells Layer rdfs:subClassOf \n",
- "943 aio:StackedRNNCells_Layer StackedRNNCells Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "942 None aio:Layer Layer \n",
- "943 None aio:StackedRNNCells_Layer StackedRNNCells Layer \n",
- "parent aio:Layer\n",
- "aio:StackedRNNCells_Layer\n",
- " subject subject_label predicate \\\n",
- "942 aio:StackedRNNCells_Layer StackedRNNCells Layer rdfs:subClassOf \n",
- "943 aio:StackedRNNCells_Layer StackedRNNCells Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "942 None aio:Layer Layer \n",
- "943 None aio:StackedRNNCells_Layer StackedRNNCells Layer \n",
- "parent aio:Layer\n",
- "aio:Streetlight_Effect_Bias\n",
- " subject subject_label predicate \\\n",
- "944 aio:Streetlight_Effect_Bias Streetlight Effect Bias rdfs:subClassOf \n",
- "945 aio:Streetlight_Effect_Bias Streetlight Effect Bias rdfs:subClassOf \n",
- "946 aio:Streetlight_Effect_Bias Streetlight Effect Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "944 None aio:Bias Bias \n",
- "945 None aio:Individual_Bias Individual Bias \n",
- "946 None aio:Streetlight_Effect_Bias Streetlight Effect Bias \n",
- "parent aio:Bias\n",
- "aio:Streetlight_Effect_Bias\n",
- " subject subject_label predicate \\\n",
- "944 aio:Streetlight_Effect_Bias Streetlight Effect Bias rdfs:subClassOf \n",
- "945 aio:Streetlight_Effect_Bias Streetlight Effect Bias rdfs:subClassOf \n",
- "946 aio:Streetlight_Effect_Bias Streetlight Effect Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "944 None aio:Bias Bias \n",
- "945 None aio:Individual_Bias Individual Bias \n",
- "946 None aio:Streetlight_Effect_Bias Streetlight Effect Bias \n",
- "parent aio:Bias\n",
- "aio:Streetlight_Effect_Bias\n",
- " subject subject_label predicate \\\n",
- "944 aio:Streetlight_Effect_Bias Streetlight Effect Bias rdfs:subClassOf \n",
- "945 aio:Streetlight_Effect_Bias Streetlight Effect Bias rdfs:subClassOf \n",
- "946 aio:Streetlight_Effect_Bias Streetlight Effect Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "944 None aio:Bias Bias \n",
- "945 None aio:Individual_Bias Individual Bias \n",
- "946 None aio:Streetlight_Effect_Bias Streetlight Effect Bias \n",
- "parent aio:Bias\n",
- "aio:StringLookup_Layer\n",
- " subject subject_label predicate \\\n",
- "947 aio:StringLookup_Layer StringLookup Layer rdfs:subClassOf \n",
- "948 aio:StringLookup_Layer StringLookup Layer rdfs:subClassOf \n",
- "949 aio:StringLookup_Layer StringLookup Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "947 None aio:Categorical_Features_Preprocessing_Layer \n",
- "948 None aio:Layer \n",
- "949 None aio:StringLookup_Layer \n",
- "\n",
- " object_label \n",
- "947 Categorical Features Preprocessing Layer \n",
- "948 Layer \n",
- "949 StringLookup Layer \n",
- "parent aio:Categorical_Features_Preprocessing_Layer\n",
- "aio:Categorical_Features_Preprocessing_Layer\n",
- " subject \\\n",
- "128 aio:Categorical_Features_Preprocessing_Layer \n",
- "129 aio:Categorical_Features_Preprocessing_Layer \n",
- "\n",
- " subject_label predicate \\\n",
- "128 Categorical Features Preprocessing Layer rdfs:subClassOf \n",
- "129 Categorical Features Preprocessing Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "128 None aio:Categorical_Features_Preprocessing_Layer \n",
- "129 None aio:Layer \n",
- "\n",
- " object_label \n",
- "128 Categorical Features Preprocessing Layer \n",
- "129 Layer \n",
- "parent aio:Categorical_Features_Preprocessing_Layer\n",
- "aio:StringLookup_Layer\n",
- " subject subject_label predicate \\\n",
- "947 aio:StringLookup_Layer StringLookup Layer rdfs:subClassOf \n",
- "948 aio:StringLookup_Layer StringLookup Layer rdfs:subClassOf \n",
- "949 aio:StringLookup_Layer StringLookup Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "947 None aio:Categorical_Features_Preprocessing_Layer \n",
- "948 None aio:Layer \n",
- "949 None aio:StringLookup_Layer \n",
- "\n",
- " object_label \n",
- "947 Categorical Features Preprocessing Layer \n",
- "948 Layer \n",
- "949 StringLookup Layer \n",
- "parent aio:Categorical_Features_Preprocessing_Layer\n",
- "aio:Categorical_Features_Preprocessing_Layer\n",
- " subject \\\n",
- "128 aio:Categorical_Features_Preprocessing_Layer \n",
- "129 aio:Categorical_Features_Preprocessing_Layer \n",
- "\n",
- " subject_label predicate \\\n",
- "128 Categorical Features Preprocessing Layer rdfs:subClassOf \n",
- "129 Categorical Features Preprocessing Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "128 None aio:Categorical_Features_Preprocessing_Layer \n",
- "129 None aio:Layer \n",
- "\n",
- " object_label \n",
- "128 Categorical Features Preprocessing Layer \n",
- "129 Layer \n",
- "parent aio:Categorical_Features_Preprocessing_Layer\n",
- "aio:StringLookup_Layer\n",
- " subject subject_label predicate \\\n",
- "947 aio:StringLookup_Layer StringLookup Layer rdfs:subClassOf \n",
- "948 aio:StringLookup_Layer StringLookup Layer rdfs:subClassOf \n",
- "949 aio:StringLookup_Layer StringLookup Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "947 None aio:Categorical_Features_Preprocessing_Layer \n",
- "948 None aio:Layer \n",
- "949 None aio:StringLookup_Layer \n",
- "\n",
- " object_label \n",
- "947 Categorical Features Preprocessing Layer \n",
- "948 Layer \n",
- "949 StringLookup Layer \n",
- "parent aio:Categorical_Features_Preprocessing_Layer\n",
- "aio:Categorical_Features_Preprocessing_Layer\n",
- " subject \\\n",
- "128 aio:Categorical_Features_Preprocessing_Layer \n",
- "129 aio:Categorical_Features_Preprocessing_Layer \n",
- "\n",
- " subject_label predicate \\\n",
- "128 Categorical Features Preprocessing Layer rdfs:subClassOf \n",
- "129 Categorical Features Preprocessing Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "128 None aio:Categorical_Features_Preprocessing_Layer \n",
- "129 None aio:Layer \n",
- "\n",
- " object_label \n",
- "128 Categorical Features Preprocessing Layer \n",
- "129 Layer \n",
- "parent aio:Categorical_Features_Preprocessing_Layer\n",
- "aio:Subtract_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "950 aio:Subtract_Layer Subtract Layer rdfs:subClassOf None \n",
- "951 aio:Subtract_Layer Subtract Layer rdfs:subClassOf None \n",
- "952 aio:Subtract_Layer Subtract Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "950 aio:Layer Layer \n",
- "951 aio:Merging_Layer Merging Layer \n",
- "952 aio:Subtract_Layer Subtract Layer \n",
- "parent aio:Layer\n",
- "aio:Subtract_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "950 aio:Subtract_Layer Subtract Layer rdfs:subClassOf None \n",
- "951 aio:Subtract_Layer Subtract Layer rdfs:subClassOf None \n",
- "952 aio:Subtract_Layer Subtract Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "950 aio:Layer Layer \n",
- "951 aio:Merging_Layer Merging Layer \n",
- "952 aio:Subtract_Layer Subtract Layer \n",
- "parent aio:Layer\n",
- "aio:Subtract_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "950 aio:Subtract_Layer Subtract Layer rdfs:subClassOf None \n",
- "951 aio:Subtract_Layer Subtract Layer rdfs:subClassOf None \n",
- "952 aio:Subtract_Layer Subtract Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "950 aio:Layer Layer \n",
- "951 aio:Merging_Layer Merging Layer \n",
- "952 aio:Subtract_Layer Subtract Layer \n",
- "parent aio:Layer\n",
- "aio:Sunk_Cost_Fallacy_Bias\n",
- " subject subject_label predicate \\\n",
- "953 aio:Sunk_Cost_Fallacy_Bias Sunk Cost Fallacy Bias rdfs:subClassOf \n",
- "954 aio:Sunk_Cost_Fallacy_Bias Sunk Cost Fallacy Bias rdfs:subClassOf \n",
- "955 aio:Sunk_Cost_Fallacy_Bias Sunk Cost Fallacy Bias rdfs:subClassOf \n",
- "956 aio:Sunk_Cost_Fallacy_Bias Sunk Cost Fallacy Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "953 None aio:Bias Bias \n",
- "954 None aio:Group_Bias Group Bias \n",
- "955 None aio:Human_Bias Human Bias \n",
- "956 None aio:Sunk_Cost_Fallacy_Bias Sunk Cost Fallacy Bias \n",
- "parent aio:Bias\n",
- "aio:Sunk_Cost_Fallacy_Bias\n",
- " subject subject_label predicate \\\n",
- "953 aio:Sunk_Cost_Fallacy_Bias Sunk Cost Fallacy Bias rdfs:subClassOf \n",
- "954 aio:Sunk_Cost_Fallacy_Bias Sunk Cost Fallacy Bias rdfs:subClassOf \n",
- "955 aio:Sunk_Cost_Fallacy_Bias Sunk Cost Fallacy Bias rdfs:subClassOf \n",
- "956 aio:Sunk_Cost_Fallacy_Bias Sunk Cost Fallacy Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "953 None aio:Bias Bias \n",
- "954 None aio:Group_Bias Group Bias \n",
- "955 None aio:Human_Bias Human Bias \n",
- "956 None aio:Sunk_Cost_Fallacy_Bias Sunk Cost Fallacy Bias \n",
- "parent aio:Bias\n",
- "aio:Sunk_Cost_Fallacy_Bias\n",
- " subject subject_label predicate \\\n",
- "953 aio:Sunk_Cost_Fallacy_Bias Sunk Cost Fallacy Bias rdfs:subClassOf \n",
- "954 aio:Sunk_Cost_Fallacy_Bias Sunk Cost Fallacy Bias rdfs:subClassOf \n",
- "955 aio:Sunk_Cost_Fallacy_Bias Sunk Cost Fallacy Bias rdfs:subClassOf \n",
- "956 aio:Sunk_Cost_Fallacy_Bias Sunk Cost Fallacy Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "953 None aio:Bias Bias \n",
- "954 None aio:Group_Bias Group Bias \n",
- "955 None aio:Human_Bias Human Bias \n",
- "956 None aio:Sunk_Cost_Fallacy_Bias Sunk Cost Fallacy Bias \n",
- "parent aio:Bias\n",
- "aio:Sunk_Cost_Fallacy_Bias\n",
- " subject subject_label predicate \\\n",
- "953 aio:Sunk_Cost_Fallacy_Bias Sunk Cost Fallacy Bias rdfs:subClassOf \n",
- "954 aio:Sunk_Cost_Fallacy_Bias Sunk Cost Fallacy Bias rdfs:subClassOf \n",
- "955 aio:Sunk_Cost_Fallacy_Bias Sunk Cost Fallacy Bias rdfs:subClassOf \n",
- "956 aio:Sunk_Cost_Fallacy_Bias Sunk Cost Fallacy Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "953 None aio:Bias Bias \n",
- "954 None aio:Group_Bias Group Bias \n",
- "955 None aio:Human_Bias Human Bias \n",
- "956 None aio:Sunk_Cost_Fallacy_Bias Sunk Cost Fallacy Bias \n",
- "parent aio:Bias\n",
- "aio:Supervised_Biclustering\n",
- " subject subject_label predicate \\\n",
- "957 aio:Supervised_Biclustering Supervised Biclustering rdfs:subClassOf \n",
- "958 aio:Supervised_Biclustering Supervised Biclustering rdfs:subClassOf \n",
- "959 aio:Supervised_Biclustering Supervised Biclustering rdfs:subClassOf \n",
- "960 aio:Supervised_Biclustering Supervised Biclustering rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "957 None aio:Biclustering Biclustering \n",
- "958 None aio:Machine_Learning Machine Learning \n",
- "959 None aio:Method Method \n",
- "960 None aio:Supervised_Biclustering Supervised Biclustering \n",
- "parent aio:Biclustering\n",
- "aio:Biclustering\n",
- " subject subject_label predicate predicate_label \\\n",
- "117 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "118 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "119 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "117 aio:Biclustering Biclustering \n",
- "118 aio:Machine_Learning Machine Learning \n",
- "119 aio:Method Method \n",
- "parent aio:Biclustering\n",
- "aio:Supervised_Biclustering\n",
- " subject subject_label predicate \\\n",
- "957 aio:Supervised_Biclustering Supervised Biclustering rdfs:subClassOf \n",
- "958 aio:Supervised_Biclustering Supervised Biclustering rdfs:subClassOf \n",
- "959 aio:Supervised_Biclustering Supervised Biclustering rdfs:subClassOf \n",
- "960 aio:Supervised_Biclustering Supervised Biclustering rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "957 None aio:Biclustering Biclustering \n",
- "958 None aio:Machine_Learning Machine Learning \n",
- "959 None aio:Method Method \n",
- "960 None aio:Supervised_Biclustering Supervised Biclustering \n",
- "parent aio:Biclustering\n",
- "aio:Biclustering\n",
- " subject subject_label predicate predicate_label \\\n",
- "117 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "118 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "119 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "117 aio:Biclustering Biclustering \n",
- "118 aio:Machine_Learning Machine Learning \n",
- "119 aio:Method Method \n",
- "parent aio:Biclustering\n",
- "aio:Supervised_Biclustering\n",
- " subject subject_label predicate \\\n",
- "957 aio:Supervised_Biclustering Supervised Biclustering rdfs:subClassOf \n",
- "958 aio:Supervised_Biclustering Supervised Biclustering rdfs:subClassOf \n",
- "959 aio:Supervised_Biclustering Supervised Biclustering rdfs:subClassOf \n",
- "960 aio:Supervised_Biclustering Supervised Biclustering rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "957 None aio:Biclustering Biclustering \n",
- "958 None aio:Machine_Learning Machine Learning \n",
- "959 None aio:Method Method \n",
- "960 None aio:Supervised_Biclustering Supervised Biclustering \n",
- "parent aio:Biclustering\n",
- "aio:Biclustering\n",
- " subject subject_label predicate predicate_label \\\n",
- "117 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "118 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "119 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "117 aio:Biclustering Biclustering \n",
- "118 aio:Machine_Learning Machine Learning \n",
- "119 aio:Method Method \n",
- "parent aio:Biclustering\n",
- "aio:Supervised_Biclustering\n",
- " subject subject_label predicate \\\n",
- "957 aio:Supervised_Biclustering Supervised Biclustering rdfs:subClassOf \n",
- "958 aio:Supervised_Biclustering Supervised Biclustering rdfs:subClassOf \n",
- "959 aio:Supervised_Biclustering Supervised Biclustering rdfs:subClassOf \n",
- "960 aio:Supervised_Biclustering Supervised Biclustering rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "957 None aio:Biclustering Biclustering \n",
- "958 None aio:Machine_Learning Machine Learning \n",
- "959 None aio:Method Method \n",
- "960 None aio:Supervised_Biclustering Supervised Biclustering \n",
- "parent aio:Biclustering\n",
- "aio:Biclustering\n",
- " subject subject_label predicate predicate_label \\\n",
- "117 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "118 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "119 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "117 aio:Biclustering Biclustering \n",
- "118 aio:Machine_Learning Machine Learning \n",
- "119 aio:Method Method \n",
- "parent aio:Biclustering\n",
- "aio:Supervised_Clustering\n",
- " subject subject_label predicate \\\n",
- "961 aio:Supervised_Clustering Supervised Clustering rdfs:subClassOf \n",
- "962 aio:Supervised_Clustering Supervised Clustering rdfs:subClassOf \n",
- "963 aio:Supervised_Clustering Supervised Clustering rdfs:subClassOf \n",
- "964 aio:Supervised_Clustering Supervised Clustering rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "961 None aio:Clustering Clustering \n",
- "962 None aio:Machine_Learning Machine Learning \n",
- "963 None aio:Method Method \n",
- "964 None aio:Supervised_Clustering Supervised Clustering \n",
- "parent aio:Clustering\n",
- "aio:Clustering\n",
- " subject subject_label predicate predicate_label \\\n",
- "144 aio:Clustering Clustering rdfs:subClassOf None \n",
- "145 aio:Clustering Clustering rdfs:subClassOf None \n",
- "146 aio:Clustering Clustering rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "144 aio:Clustering Clustering \n",
- "145 aio:Machine_Learning Machine Learning \n",
- "146 aio:Method Method \n",
- "parent aio:Clustering\n",
- "aio:Supervised_Clustering\n",
- " subject subject_label predicate \\\n",
- "961 aio:Supervised_Clustering Supervised Clustering rdfs:subClassOf \n",
- "962 aio:Supervised_Clustering Supervised Clustering rdfs:subClassOf \n",
- "963 aio:Supervised_Clustering Supervised Clustering rdfs:subClassOf \n",
- "964 aio:Supervised_Clustering Supervised Clustering rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "961 None aio:Clustering Clustering \n",
- "962 None aio:Machine_Learning Machine Learning \n",
- "963 None aio:Method Method \n",
- "964 None aio:Supervised_Clustering Supervised Clustering \n",
- "parent aio:Clustering\n",
- "aio:Clustering\n",
- " subject subject_label predicate predicate_label \\\n",
- "144 aio:Clustering Clustering rdfs:subClassOf None \n",
- "145 aio:Clustering Clustering rdfs:subClassOf None \n",
- "146 aio:Clustering Clustering rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "144 aio:Clustering Clustering \n",
- "145 aio:Machine_Learning Machine Learning \n",
- "146 aio:Method Method \n",
- "parent aio:Clustering\n",
- "aio:Supervised_Clustering\n",
- " subject subject_label predicate \\\n",
- "961 aio:Supervised_Clustering Supervised Clustering rdfs:subClassOf \n",
- "962 aio:Supervised_Clustering Supervised Clustering rdfs:subClassOf \n",
- "963 aio:Supervised_Clustering Supervised Clustering rdfs:subClassOf \n",
- "964 aio:Supervised_Clustering Supervised Clustering rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "961 None aio:Clustering Clustering \n",
- "962 None aio:Machine_Learning Machine Learning \n",
- "963 None aio:Method Method \n",
- "964 None aio:Supervised_Clustering Supervised Clustering \n",
- "parent aio:Clustering\n",
- "aio:Clustering\n",
- " subject subject_label predicate predicate_label \\\n",
- "144 aio:Clustering Clustering rdfs:subClassOf None \n",
- "145 aio:Clustering Clustering rdfs:subClassOf None \n",
- "146 aio:Clustering Clustering rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "144 aio:Clustering Clustering \n",
- "145 aio:Machine_Learning Machine Learning \n",
- "146 aio:Method Method \n",
- "parent aio:Clustering\n",
- "aio:Supervised_Clustering\n",
- " subject subject_label predicate \\\n",
- "961 aio:Supervised_Clustering Supervised Clustering rdfs:subClassOf \n",
- "962 aio:Supervised_Clustering Supervised Clustering rdfs:subClassOf \n",
- "963 aio:Supervised_Clustering Supervised Clustering rdfs:subClassOf \n",
- "964 aio:Supervised_Clustering Supervised Clustering rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "961 None aio:Clustering Clustering \n",
- "962 None aio:Machine_Learning Machine Learning \n",
- "963 None aio:Method Method \n",
- "964 None aio:Supervised_Clustering Supervised Clustering \n",
- "parent aio:Clustering\n",
- "aio:Clustering\n",
- " subject subject_label predicate predicate_label \\\n",
- "144 aio:Clustering Clustering rdfs:subClassOf None \n",
- "145 aio:Clustering Clustering rdfs:subClassOf None \n",
- "146 aio:Clustering Clustering rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "144 aio:Clustering Clustering \n",
- "145 aio:Machine_Learning Machine Learning \n",
- "146 aio:Method Method \n",
- "parent aio:Clustering\n",
- "aio:Supervised_Learning\n",
- " subject subject_label predicate \\\n",
- "965 aio:Supervised_Learning Supervised Learning rdfs:subClassOf \n",
- "966 aio:Supervised_Learning Supervised Learning rdfs:subClassOf \n",
- "967 aio:Supervised_Learning Supervised Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "965 None aio:Machine_Learning Machine Learning \n",
- "966 None aio:Method Method \n",
- "967 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Supervised_Learning\n",
- " subject subject_label predicate \\\n",
- "965 aio:Supervised_Learning Supervised Learning rdfs:subClassOf \n",
- "966 aio:Supervised_Learning Supervised Learning rdfs:subClassOf \n",
- "967 aio:Supervised_Learning Supervised Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "965 None aio:Machine_Learning Machine Learning \n",
- "966 None aio:Method Method \n",
- "967 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Supervised_Learning\n",
- " subject subject_label predicate \\\n",
- "965 aio:Supervised_Learning Supervised Learning rdfs:subClassOf \n",
- "966 aio:Supervised_Learning Supervised Learning rdfs:subClassOf \n",
- "967 aio:Supervised_Learning Supervised Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "965 None aio:Machine_Learning Machine Learning \n",
- "966 None aio:Method Method \n",
- "967 None aio:Supervised_Learning Supervised Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Survival_Analysis\n",
- " subject subject_label predicate \\\n",
- "968 aio:Survival_Analysis Survival Analysis rdfs:subClassOf \n",
- "969 aio:Survival_Analysis Survival Analysis rdfs:subClassOf \n",
- "970 aio:Survival_Analysis Survival Analysis rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "968 None aio:Machine_Learning Machine Learning \n",
- "969 None aio:Method Method \n",
- "970 None aio:Survival_Analysis Survival Analysis \n",
- "parent aio:Machine_Learning\n",
- "aio:Survival_Analysis\n",
- " subject subject_label predicate \\\n",
- "968 aio:Survival_Analysis Survival Analysis rdfs:subClassOf \n",
- "969 aio:Survival_Analysis Survival Analysis rdfs:subClassOf \n",
- "970 aio:Survival_Analysis Survival Analysis rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "968 None aio:Machine_Learning Machine Learning \n",
- "969 None aio:Method Method \n",
- "970 None aio:Survival_Analysis Survival Analysis \n",
- "parent aio:Machine_Learning\n",
- "aio:Survival_Analysis\n",
- " subject subject_label predicate \\\n",
- "968 aio:Survival_Analysis Survival Analysis rdfs:subClassOf \n",
- "969 aio:Survival_Analysis Survival Analysis rdfs:subClassOf \n",
- "970 aio:Survival_Analysis Survival Analysis rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "968 None aio:Machine_Learning Machine Learning \n",
- "969 None aio:Method Method \n",
- "970 None aio:Survival_Analysis Survival Analysis \n",
- "parent aio:Machine_Learning\n",
- "aio:Survivorship_Bias\n",
- " subject subject_label predicate \\\n",
- "971 aio:Survivorship_Bias Survivorship Bias rdfs:subClassOf \n",
- "972 aio:Survivorship_Bias Survivorship Bias rdfs:subClassOf \n",
- "973 aio:Survivorship_Bias Survivorship Bias rdfs:subClassOf \n",
- "974 aio:Survivorship_Bias Survivorship Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "971 None aio:Bias Bias \n",
- "972 None aio:Computational_Bias Computational Bias \n",
- "973 None aio:Processing_Bias Processing Bias \n",
- "974 None aio:Survivorship_Bias Survivorship Bias \n",
- "parent aio:Bias\n",
- "aio:Survivorship_Bias\n",
- " subject subject_label predicate \\\n",
- "971 aio:Survivorship_Bias Survivorship Bias rdfs:subClassOf \n",
- "972 aio:Survivorship_Bias Survivorship Bias rdfs:subClassOf \n",
- "973 aio:Survivorship_Bias Survivorship Bias rdfs:subClassOf \n",
- "974 aio:Survivorship_Bias Survivorship Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "971 None aio:Bias Bias \n",
- "972 None aio:Computational_Bias Computational Bias \n",
- "973 None aio:Processing_Bias Processing Bias \n",
- "974 None aio:Survivorship_Bias Survivorship Bias \n",
- "parent aio:Bias\n",
- "aio:Survivorship_Bias\n",
- " subject subject_label predicate \\\n",
- "971 aio:Survivorship_Bias Survivorship Bias rdfs:subClassOf \n",
- "972 aio:Survivorship_Bias Survivorship Bias rdfs:subClassOf \n",
- "973 aio:Survivorship_Bias Survivorship Bias rdfs:subClassOf \n",
- "974 aio:Survivorship_Bias Survivorship Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "971 None aio:Bias Bias \n",
- "972 None aio:Computational_Bias Computational Bias \n",
- "973 None aio:Processing_Bias Processing Bias \n",
- "974 None aio:Survivorship_Bias Survivorship Bias \n",
- "parent aio:Bias\n",
- "aio:Survivorship_Bias\n",
- " subject subject_label predicate \\\n",
- "971 aio:Survivorship_Bias Survivorship Bias rdfs:subClassOf \n",
- "972 aio:Survivorship_Bias Survivorship Bias rdfs:subClassOf \n",
- "973 aio:Survivorship_Bias Survivorship Bias rdfs:subClassOf \n",
- "974 aio:Survivorship_Bias Survivorship Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "971 None aio:Bias Bias \n",
- "972 None aio:Computational_Bias Computational Bias \n",
- "973 None aio:Processing_Bias Processing Bias \n",
- "974 None aio:Survivorship_Bias Survivorship Bias \n",
- "parent aio:Bias\n",
- "aio:Swish_Function\n",
- " subject subject_label predicate predicate_label \\\n",
- "975 aio:Swish_Function Swish Function rdfs:subClassOf None \n",
- "976 aio:Swish_Function Swish Function rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "975 aio:Function None \n",
- "976 aio:Swish_Function Swish Function \n",
- "parent aio:Function\n",
- "aio:Swish_Function\n",
- " subject subject_label predicate predicate_label \\\n",
- "975 aio:Swish_Function Swish Function rdfs:subClassOf None \n",
- "976 aio:Swish_Function Swish Function rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "975 aio:Function None \n",
- "976 aio:Swish_Function Swish Function \n",
- "parent aio:Function\n",
- "aio:SyncBatchNorm_Layer\n",
- " subject subject_label predicate \\\n",
- "977 aio:SyncBatchNorm_Layer SyncBatchNorm Layer rdfs:subClassOf \n",
- "978 aio:SyncBatchNorm_Layer SyncBatchNorm Layer rdfs:subClassOf \n",
- "979 aio:SyncBatchNorm_Layer SyncBatchNorm Layer rdfs:subClassOf \n",
- "980 aio:SyncBatchNorm_Layer SyncBatchNorm Layer rdfs:subClassOf \n",
- "981 aio:SyncBatchNorm_Layer SyncBatchNorm Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "977 None aio:BatchNormalization_Layer \n",
- "978 None aio:Layer \n",
- "979 None aio:Normalization_Layer \n",
- "980 None aio:Numerical_Features_Preprocessing_Layer \n",
- "981 None aio:SyncBatchNorm_Layer \n",
- "\n",
- " object_label \n",
- "977 BatchNormalization Layer \n",
- "978 Layer \n",
- "979 Normalization Layer \n",
- "980 Numerical Features Preprocessing Layer \n",
- "981 SyncBatchNorm Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:BatchNormalization_Layer\n",
- " subject subject_label predicate \\\n",
- "107 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "108 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "109 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "110 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "107 None aio:BatchNormalization_Layer \n",
- "108 None aio:Layer \n",
- "109 None aio:Normalization_Layer \n",
- "110 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "107 BatchNormalization Layer \n",
- "108 Layer \n",
- "109 Normalization Layer \n",
- "110 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:SyncBatchNorm_Layer\n",
- " subject subject_label predicate \\\n",
- "977 aio:SyncBatchNorm_Layer SyncBatchNorm Layer rdfs:subClassOf \n",
- "978 aio:SyncBatchNorm_Layer SyncBatchNorm Layer rdfs:subClassOf \n",
- "979 aio:SyncBatchNorm_Layer SyncBatchNorm Layer rdfs:subClassOf \n",
- "980 aio:SyncBatchNorm_Layer SyncBatchNorm Layer rdfs:subClassOf \n",
- "981 aio:SyncBatchNorm_Layer SyncBatchNorm Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "977 None aio:BatchNormalization_Layer \n",
- "978 None aio:Layer \n",
- "979 None aio:Normalization_Layer \n",
- "980 None aio:Numerical_Features_Preprocessing_Layer \n",
- "981 None aio:SyncBatchNorm_Layer \n",
- "\n",
- " object_label \n",
- "977 BatchNormalization Layer \n",
- "978 Layer \n",
- "979 Normalization Layer \n",
- "980 Numerical Features Preprocessing Layer \n",
- "981 SyncBatchNorm Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:BatchNormalization_Layer\n",
- " subject subject_label predicate \\\n",
- "107 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "108 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "109 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "110 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "107 None aio:BatchNormalization_Layer \n",
- "108 None aio:Layer \n",
- "109 None aio:Normalization_Layer \n",
- "110 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "107 BatchNormalization Layer \n",
- "108 Layer \n",
- "109 Normalization Layer \n",
- "110 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:SyncBatchNorm_Layer\n",
- " subject subject_label predicate \\\n",
- "977 aio:SyncBatchNorm_Layer SyncBatchNorm Layer rdfs:subClassOf \n",
- "978 aio:SyncBatchNorm_Layer SyncBatchNorm Layer rdfs:subClassOf \n",
- "979 aio:SyncBatchNorm_Layer SyncBatchNorm Layer rdfs:subClassOf \n",
- "980 aio:SyncBatchNorm_Layer SyncBatchNorm Layer rdfs:subClassOf \n",
- "981 aio:SyncBatchNorm_Layer SyncBatchNorm Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "977 None aio:BatchNormalization_Layer \n",
- "978 None aio:Layer \n",
- "979 None aio:Normalization_Layer \n",
- "980 None aio:Numerical_Features_Preprocessing_Layer \n",
- "981 None aio:SyncBatchNorm_Layer \n",
- "\n",
- " object_label \n",
- "977 BatchNormalization Layer \n",
- "978 Layer \n",
- "979 Normalization Layer \n",
- "980 Numerical Features Preprocessing Layer \n",
- "981 SyncBatchNorm Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:BatchNormalization_Layer\n",
- " subject subject_label predicate \\\n",
- "107 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "108 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "109 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "110 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "107 None aio:BatchNormalization_Layer \n",
- "108 None aio:Layer \n",
- "109 None aio:Normalization_Layer \n",
- "110 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "107 BatchNormalization Layer \n",
- "108 Layer \n",
- "109 Normalization Layer \n",
- "110 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:SyncBatchNorm_Layer\n",
- " subject subject_label predicate \\\n",
- "977 aio:SyncBatchNorm_Layer SyncBatchNorm Layer rdfs:subClassOf \n",
- "978 aio:SyncBatchNorm_Layer SyncBatchNorm Layer rdfs:subClassOf \n",
- "979 aio:SyncBatchNorm_Layer SyncBatchNorm Layer rdfs:subClassOf \n",
- "980 aio:SyncBatchNorm_Layer SyncBatchNorm Layer rdfs:subClassOf \n",
- "981 aio:SyncBatchNorm_Layer SyncBatchNorm Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "977 None aio:BatchNormalization_Layer \n",
- "978 None aio:Layer \n",
- "979 None aio:Normalization_Layer \n",
- "980 None aio:Numerical_Features_Preprocessing_Layer \n",
- "981 None aio:SyncBatchNorm_Layer \n",
- "\n",
- " object_label \n",
- "977 BatchNormalization Layer \n",
- "978 Layer \n",
- "979 Normalization Layer \n",
- "980 Numerical Features Preprocessing Layer \n",
- "981 SyncBatchNorm Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:BatchNormalization_Layer\n",
- " subject subject_label predicate \\\n",
- "107 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "108 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "109 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "110 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "107 None aio:BatchNormalization_Layer \n",
- "108 None aio:Layer \n",
- "109 None aio:Normalization_Layer \n",
- "110 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "107 BatchNormalization Layer \n",
- "108 Layer \n",
- "109 Normalization Layer \n",
- "110 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:SyncBatchNorm_Layer\n",
- " subject subject_label predicate \\\n",
- "977 aio:SyncBatchNorm_Layer SyncBatchNorm Layer rdfs:subClassOf \n",
- "978 aio:SyncBatchNorm_Layer SyncBatchNorm Layer rdfs:subClassOf \n",
- "979 aio:SyncBatchNorm_Layer SyncBatchNorm Layer rdfs:subClassOf \n",
- "980 aio:SyncBatchNorm_Layer SyncBatchNorm Layer rdfs:subClassOf \n",
- "981 aio:SyncBatchNorm_Layer SyncBatchNorm Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "977 None aio:BatchNormalization_Layer \n",
- "978 None aio:Layer \n",
- "979 None aio:Normalization_Layer \n",
- "980 None aio:Numerical_Features_Preprocessing_Layer \n",
- "981 None aio:SyncBatchNorm_Layer \n",
- "\n",
- " object_label \n",
- "977 BatchNormalization Layer \n",
- "978 Layer \n",
- "979 Normalization Layer \n",
- "980 Numerical Features Preprocessing Layer \n",
- "981 SyncBatchNorm Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:BatchNormalization_Layer\n",
- " subject subject_label predicate \\\n",
- "107 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "108 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "109 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "110 aio:BatchNormalization_Layer BatchNormalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "107 None aio:BatchNormalization_Layer \n",
- "108 None aio:Layer \n",
- "109 None aio:Normalization_Layer \n",
- "110 None aio:Numerical_Features_Preprocessing_Layer \n",
- "\n",
- " object_label \n",
- "107 BatchNormalization Layer \n",
- "108 Layer \n",
- "109 Normalization Layer \n",
- "110 Numerical Features Preprocessing Layer \n",
- "parent aio:BatchNormalization_Layer\n",
- "aio:Systemic_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "982 aio:Systemic_Bias Systemic Bias rdfs:subClassOf None \n",
- "983 aio:Systemic_Bias Systemic Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "982 aio:Bias Bias \n",
- "983 aio:Systemic_Bias Systemic Bias \n",
- "parent aio:Bias\n",
- "aio:Systemic_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "982 aio:Systemic_Bias Systemic Bias rdfs:subClassOf None \n",
- "983 aio:Systemic_Bias Systemic Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "982 aio:Bias Bias \n",
- "983 aio:Systemic_Bias Systemic Bias \n",
- "parent aio:Bias\n",
- "aio:Tanh_Function\n",
- " subject subject_label predicate predicate_label \\\n",
- "984 aio:Tanh_Function Tanh Function rdfs:subClassOf None \n",
- "985 aio:Tanh_Function Tanh Function rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "984 aio:Function None \n",
- "985 aio:Tanh_Function Tanh Function \n",
- "parent aio:Function\n",
- "aio:Tanh_Function\n",
- " subject subject_label predicate predicate_label \\\n",
- "984 aio:Tanh_Function Tanh Function rdfs:subClassOf None \n",
- "985 aio:Tanh_Function Tanh Function rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "984 aio:Function None \n",
- "985 aio:Tanh_Function Tanh Function \n",
- "parent aio:Function\n",
- "aio:Temporal_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "986 aio:Temporal_Bias Temporal Bias rdfs:subClassOf None \n",
- "987 aio:Temporal_Bias Temporal Bias rdfs:subClassOf None \n",
- "988 aio:Temporal_Bias Temporal Bias rdfs:subClassOf None \n",
- "989 aio:Temporal_Bias Temporal Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "986 aio:Bias Bias \n",
- "987 aio:Computational_Bias Computational Bias \n",
- "988 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "989 aio:Temporal_Bias Temporal Bias \n",
- "parent aio:Bias\n",
- "aio:Temporal_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "986 aio:Temporal_Bias Temporal Bias rdfs:subClassOf None \n",
- "987 aio:Temporal_Bias Temporal Bias rdfs:subClassOf None \n",
- "988 aio:Temporal_Bias Temporal Bias rdfs:subClassOf None \n",
- "989 aio:Temporal_Bias Temporal Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "986 aio:Bias Bias \n",
- "987 aio:Computational_Bias Computational Bias \n",
- "988 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "989 aio:Temporal_Bias Temporal Bias \n",
- "parent aio:Bias\n",
- "aio:Temporal_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "986 aio:Temporal_Bias Temporal Bias rdfs:subClassOf None \n",
- "987 aio:Temporal_Bias Temporal Bias rdfs:subClassOf None \n",
- "988 aio:Temporal_Bias Temporal Bias rdfs:subClassOf None \n",
- "989 aio:Temporal_Bias Temporal Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "986 aio:Bias Bias \n",
- "987 aio:Computational_Bias Computational Bias \n",
- "988 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "989 aio:Temporal_Bias Temporal Bias \n",
- "parent aio:Bias\n",
- "aio:Temporal_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "986 aio:Temporal_Bias Temporal Bias rdfs:subClassOf None \n",
- "987 aio:Temporal_Bias Temporal Bias rdfs:subClassOf None \n",
- "988 aio:Temporal_Bias Temporal Bias rdfs:subClassOf None \n",
- "989 aio:Temporal_Bias Temporal Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "986 aio:Bias Bias \n",
- "987 aio:Computational_Bias Computational Bias \n",
- "988 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "989 aio:Temporal_Bias Temporal Bias \n",
- "parent aio:Bias\n",
- "aio:TextVectorization_Layer\n",
- " subject subject_label predicate \\\n",
- "990 aio:TextVectorization_Layer TextVectorization Layer rdfs:subClassOf \n",
- "991 aio:TextVectorization_Layer TextVectorization Layer rdfs:subClassOf \n",
- "992 aio:TextVectorization_Layer TextVectorization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "990 None aio:Layer Layer \n",
- "991 None aio:TextVectorization_Layer TextVectorization Layer \n",
- "992 None aio:Text_Preprocessing_Layer Text Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:TextVectorization_Layer\n",
- " subject subject_label predicate \\\n",
- "990 aio:TextVectorization_Layer TextVectorization Layer rdfs:subClassOf \n",
- "991 aio:TextVectorization_Layer TextVectorization Layer rdfs:subClassOf \n",
- "992 aio:TextVectorization_Layer TextVectorization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "990 None aio:Layer Layer \n",
- "991 None aio:TextVectorization_Layer TextVectorization Layer \n",
- "992 None aio:Text_Preprocessing_Layer Text Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:TextVectorization_Layer\n",
- " subject subject_label predicate \\\n",
- "990 aio:TextVectorization_Layer TextVectorization Layer rdfs:subClassOf \n",
- "991 aio:TextVectorization_Layer TextVectorization Layer rdfs:subClassOf \n",
- "992 aio:TextVectorization_Layer TextVectorization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "990 None aio:Layer Layer \n",
- "991 None aio:TextVectorization_Layer TextVectorization Layer \n",
- "992 None aio:Text_Preprocessing_Layer Text Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:Text_Preprocessing_Layer\n",
- " subject subject_label predicate \\\n",
- "993 aio:Text_Preprocessing_Layer Text Preprocessing Layer rdfs:subClassOf \n",
- "994 aio:Text_Preprocessing_Layer Text Preprocessing Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "993 None aio:Layer Layer \n",
- "994 None aio:Text_Preprocessing_Layer Text Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:Text_Preprocessing_Layer\n",
- " subject subject_label predicate \\\n",
- "993 aio:Text_Preprocessing_Layer Text Preprocessing Layer rdfs:subClassOf \n",
- "994 aio:Text_Preprocessing_Layer Text Preprocessing Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "993 None aio:Layer Layer \n",
- "994 None aio:Text_Preprocessing_Layer Text Preprocessing Layer \n",
- "parent aio:Layer\n",
- "aio:ThresholdedReLU_Layer\n",
- " subject subject_label predicate \\\n",
- "995 aio:ThresholdedReLU_Layer ThresholdedReLU Layer rdfs:subClassOf \n",
- "996 aio:ThresholdedReLU_Layer ThresholdedReLU Layer rdfs:subClassOf \n",
- "997 aio:ThresholdedReLU_Layer ThresholdedReLU Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "995 None aio:Activation_Layer Activation Layer \n",
- "996 None aio:Layer Layer \n",
- "997 None aio:ThresholdedReLU_Layer ThresholdedReLU Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:Activation_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "7 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "8 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "7 aio:Activation_Layer Activation Layer \n",
- "8 aio:Layer Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:ThresholdedReLU_Layer\n",
- " subject subject_label predicate \\\n",
- "995 aio:ThresholdedReLU_Layer ThresholdedReLU Layer rdfs:subClassOf \n",
- "996 aio:ThresholdedReLU_Layer ThresholdedReLU Layer rdfs:subClassOf \n",
- "997 aio:ThresholdedReLU_Layer ThresholdedReLU Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "995 None aio:Activation_Layer Activation Layer \n",
- "996 None aio:Layer Layer \n",
- "997 None aio:ThresholdedReLU_Layer ThresholdedReLU Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:Activation_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "7 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "8 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "7 aio:Activation_Layer Activation Layer \n",
- "8 aio:Layer Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:ThresholdedReLU_Layer\n",
- " subject subject_label predicate \\\n",
- "995 aio:ThresholdedReLU_Layer ThresholdedReLU Layer rdfs:subClassOf \n",
- "996 aio:ThresholdedReLU_Layer ThresholdedReLU Layer rdfs:subClassOf \n",
- "997 aio:ThresholdedReLU_Layer ThresholdedReLU Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "995 None aio:Activation_Layer Activation Layer \n",
- "996 None aio:Layer Layer \n",
- "997 None aio:ThresholdedReLU_Layer ThresholdedReLU Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:Activation_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "7 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "8 aio:Activation_Layer Activation Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "7 aio:Activation_Layer Activation Layer \n",
- "8 aio:Layer Layer \n",
- "parent aio:Activation_Layer\n",
- "aio:TimeDistributed_Layer\n",
- " subject subject_label predicate \\\n",
- "998 aio:TimeDistributed_Layer TimeDistributed Layer rdfs:subClassOf \n",
- "999 aio:TimeDistributed_Layer TimeDistributed Layer rdfs:subClassOf \n",
- "1000 aio:TimeDistributed_Layer TimeDistributed Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "998 None aio:Layer Layer \n",
- "999 None aio:Recurrent_Layer Recurrent Layer \n",
- "1000 None aio:TimeDistributed_Layer TimeDistributed Layer \n",
- "parent aio:Layer\n",
- "aio:TimeDistributed_Layer\n",
- " subject subject_label predicate \\\n",
- "998 aio:TimeDistributed_Layer TimeDistributed Layer rdfs:subClassOf \n",
- "999 aio:TimeDistributed_Layer TimeDistributed Layer rdfs:subClassOf \n",
- "1000 aio:TimeDistributed_Layer TimeDistributed Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "998 None aio:Layer Layer \n",
- "999 None aio:Recurrent_Layer Recurrent Layer \n",
- "1000 None aio:TimeDistributed_Layer TimeDistributed Layer \n",
- "parent aio:Layer\n",
- "aio:TimeDistributed_Layer\n",
- " subject subject_label predicate \\\n",
- "998 aio:TimeDistributed_Layer TimeDistributed Layer rdfs:subClassOf \n",
- "999 aio:TimeDistributed_Layer TimeDistributed Layer rdfs:subClassOf \n",
- "1000 aio:TimeDistributed_Layer TimeDistributed Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "998 None aio:Layer Layer \n",
- "999 None aio:Recurrent_Layer Recurrent Layer \n",
- "1000 None aio:TimeDistributed_Layer TimeDistributed Layer \n",
- "parent aio:Layer\n",
- "aio:Time_Series_Analysis\n",
- " subject subject_label predicate \\\n",
- "1001 aio:Time_Series_Analysis Time Series Analysis rdfs:subClassOf \n",
- "1002 aio:Time_Series_Analysis Time Series Analysis rdfs:subClassOf \n",
- "1003 aio:Time_Series_Analysis Time Series Analysis rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1001 None aio:Machine_Learning Machine Learning \n",
- "1002 None aio:Method Method \n",
- "1003 None aio:Time_Series_Analysis Time Series Analysis \n",
- "parent aio:Machine_Learning\n",
- "aio:Time_Series_Analysis\n",
- " subject subject_label predicate \\\n",
- "1001 aio:Time_Series_Analysis Time Series Analysis rdfs:subClassOf \n",
- "1002 aio:Time_Series_Analysis Time Series Analysis rdfs:subClassOf \n",
- "1003 aio:Time_Series_Analysis Time Series Analysis rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1001 None aio:Machine_Learning Machine Learning \n",
- "1002 None aio:Method Method \n",
- "1003 None aio:Time_Series_Analysis Time Series Analysis \n",
- "parent aio:Machine_Learning\n",
- "aio:Time_Series_Analysis\n",
- " subject subject_label predicate \\\n",
- "1001 aio:Time_Series_Analysis Time Series Analysis rdfs:subClassOf \n",
- "1002 aio:Time_Series_Analysis Time Series Analysis rdfs:subClassOf \n",
- "1003 aio:Time_Series_Analysis Time Series Analysis rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1001 None aio:Machine_Learning Machine Learning \n",
- "1002 None aio:Method Method \n",
- "1003 None aio:Time_Series_Analysis Time Series Analysis \n",
- "parent aio:Machine_Learning\n",
- "aio:Time_Series_Forecasting\n",
- " subject subject_label predicate \\\n",
- "1004 aio:Time_Series_Forecasting Time Series Forecasting rdfs:subClassOf \n",
- "1005 aio:Time_Series_Forecasting Time Series Forecasting rdfs:subClassOf \n",
- "1006 aio:Time_Series_Forecasting Time Series Forecasting rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1004 None aio:Machine_Learning Machine Learning \n",
- "1005 None aio:Method Method \n",
- "1006 None aio:Time_Series_Forecasting Time Series Forecasting \n",
- "parent aio:Machine_Learning\n",
- "aio:Time_Series_Forecasting\n",
- " subject subject_label predicate \\\n",
- "1004 aio:Time_Series_Forecasting Time Series Forecasting rdfs:subClassOf \n",
- "1005 aio:Time_Series_Forecasting Time Series Forecasting rdfs:subClassOf \n",
- "1006 aio:Time_Series_Forecasting Time Series Forecasting rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1004 None aio:Machine_Learning Machine Learning \n",
- "1005 None aio:Method Method \n",
- "1006 None aio:Time_Series_Forecasting Time Series Forecasting \n",
- "parent aio:Machine_Learning\n",
- "aio:Time_Series_Forecasting\n",
- " subject subject_label predicate \\\n",
- "1004 aio:Time_Series_Forecasting Time Series Forecasting rdfs:subClassOf \n",
- "1005 aio:Time_Series_Forecasting Time Series Forecasting rdfs:subClassOf \n",
- "1006 aio:Time_Series_Forecasting Time Series Forecasting rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1004 None aio:Machine_Learning Machine Learning \n",
- "1005 None aio:Method Method \n",
- "1006 None aio:Time_Series_Forecasting Time Series Forecasting \n",
- "parent aio:Machine_Learning\n",
- "aio:Transfer_Learning\n",
- " subject subject_label predicate \\\n",
- "1007 aio:Transfer_Learning Transfer Learning rdfs:subClassOf \n",
- "1008 aio:Transfer_Learning Transfer Learning rdfs:subClassOf \n",
- "1009 aio:Transfer_Learning Transfer Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1007 None aio:Machine_Learning Machine Learning \n",
- "1008 None aio:Method Method \n",
- "1009 None aio:Transfer_Learning Transfer Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Transfer_Learning\n",
- " subject subject_label predicate \\\n",
- "1007 aio:Transfer_Learning Transfer Learning rdfs:subClassOf \n",
- "1008 aio:Transfer_Learning Transfer Learning rdfs:subClassOf \n",
- "1009 aio:Transfer_Learning Transfer Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1007 None aio:Machine_Learning Machine Learning \n",
- "1008 None aio:Method Method \n",
- "1009 None aio:Transfer_Learning Transfer Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Transfer_Learning\n",
- " subject subject_label predicate \\\n",
- "1007 aio:Transfer_Learning Transfer Learning rdfs:subClassOf \n",
- "1008 aio:Transfer_Learning Transfer Learning rdfs:subClassOf \n",
- "1009 aio:Transfer_Learning Transfer Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1007 None aio:Machine_Learning Machine Learning \n",
- "1008 None aio:Method Method \n",
- "1009 None aio:Transfer_Learning Transfer Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Transformer\n",
- " subject subject_label predicate predicate_label \\\n",
- "1010 aio:Transformer Transformer Network rdfs:subClassOf None \n",
- "1011 aio:Transformer Transformer Network rdfs:subClassOf None \n",
- "1012 aio:Transformer Transformer Network rdfs:subClassOf None \n",
- "1013 aio:Transformer Transformer Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "1010 aio:ANN Artificial Neural Network \n",
- "1011 aio:DNN Deep Neural Network \n",
- "1012 aio:Network Network \n",
- "1013 aio:Transformer Transformer Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Transformer\n",
- " subject subject_label predicate predicate_label \\\n",
- "1010 aio:Transformer Transformer Network rdfs:subClassOf None \n",
- "1011 aio:Transformer Transformer Network rdfs:subClassOf None \n",
- "1012 aio:Transformer Transformer Network rdfs:subClassOf None \n",
- "1013 aio:Transformer Transformer Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "1010 aio:ANN Artificial Neural Network \n",
- "1011 aio:DNN Deep Neural Network \n",
- "1012 aio:Network Network \n",
- "1013 aio:Transformer Transformer Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Transformer\n",
- " subject subject_label predicate predicate_label \\\n",
- "1010 aio:Transformer Transformer Network rdfs:subClassOf None \n",
- "1011 aio:Transformer Transformer Network rdfs:subClassOf None \n",
- "1012 aio:Transformer Transformer Network rdfs:subClassOf None \n",
- "1013 aio:Transformer Transformer Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "1010 aio:ANN Artificial Neural Network \n",
- "1011 aio:DNN Deep Neural Network \n",
- "1012 aio:Network Network \n",
- "1013 aio:Transformer Transformer Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Transformer\n",
- " subject subject_label predicate predicate_label \\\n",
- "1010 aio:Transformer Transformer Network rdfs:subClassOf None \n",
- "1011 aio:Transformer Transformer Network rdfs:subClassOf None \n",
- "1012 aio:Transformer Transformer Network rdfs:subClassOf None \n",
- "1013 aio:Transformer Transformer Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "1010 aio:ANN Artificial Neural Network \n",
- "1011 aio:DNN Deep Neural Network \n",
- "1012 aio:Network Network \n",
- "1013 aio:Transformer Transformer Network \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:UPN\n",
- " subject subject_label predicate \\\n",
- "1014 aio:UPN Unsupervised Pretrained Network rdfs:subClassOf \n",
- "1015 aio:UPN Unsupervised Pretrained Network rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1014 None aio:Network Network \n",
- "1015 None aio:UPN Unsupervised Pretrained Network \n",
- "parent aio:Network\n",
- "aio:UPN\n",
- " subject subject_label predicate \\\n",
- "1014 aio:UPN Unsupervised Pretrained Network rdfs:subClassOf \n",
- "1015 aio:UPN Unsupervised Pretrained Network rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1014 None aio:Network Network \n",
- "1015 None aio:UPN Unsupervised Pretrained Network \n",
- "parent aio:Network\n",
- "aio:Uncertainty_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "1016 aio:Uncertainty_Bias Uncertainty Bias rdfs:subClassOf None \n",
- "1017 aio:Uncertainty_Bias Uncertainty Bias rdfs:subClassOf None \n",
- "1018 aio:Uncertainty_Bias Uncertainty Bias rdfs:subClassOf None \n",
- "1019 aio:Uncertainty_Bias Uncertainty Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "1016 aio:Bias Bias \n",
- "1017 aio:Computational_Bias Computational Bias \n",
- "1018 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "1019 aio:Uncertainty_Bias Uncertainty Bias \n",
- "parent aio:Bias\n",
- "aio:Uncertainty_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "1016 aio:Uncertainty_Bias Uncertainty Bias rdfs:subClassOf None \n",
- "1017 aio:Uncertainty_Bias Uncertainty Bias rdfs:subClassOf None \n",
- "1018 aio:Uncertainty_Bias Uncertainty Bias rdfs:subClassOf None \n",
- "1019 aio:Uncertainty_Bias Uncertainty Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "1016 aio:Bias Bias \n",
- "1017 aio:Computational_Bias Computational Bias \n",
- "1018 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "1019 aio:Uncertainty_Bias Uncertainty Bias \n",
- "parent aio:Bias\n",
- "aio:Uncertainty_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "1016 aio:Uncertainty_Bias Uncertainty Bias rdfs:subClassOf None \n",
- "1017 aio:Uncertainty_Bias Uncertainty Bias rdfs:subClassOf None \n",
- "1018 aio:Uncertainty_Bias Uncertainty Bias rdfs:subClassOf None \n",
- "1019 aio:Uncertainty_Bias Uncertainty Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "1016 aio:Bias Bias \n",
- "1017 aio:Computational_Bias Computational Bias \n",
- "1018 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "1019 aio:Uncertainty_Bias Uncertainty Bias \n",
- "parent aio:Bias\n",
- "aio:Uncertainty_Bias\n",
- " subject subject_label predicate predicate_label \\\n",
- "1016 aio:Uncertainty_Bias Uncertainty Bias rdfs:subClassOf None \n",
- "1017 aio:Uncertainty_Bias Uncertainty Bias rdfs:subClassOf None \n",
- "1018 aio:Uncertainty_Bias Uncertainty Bias rdfs:subClassOf None \n",
- "1019 aio:Uncertainty_Bias Uncertainty Bias rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "1016 aio:Bias Bias \n",
- "1017 aio:Computational_Bias Computational Bias \n",
- "1018 aio:Selection_And_Sampling_Bias Selection And Sampling Bias \n",
- "1019 aio:Uncertainty_Bias Uncertainty Bias \n",
- "parent aio:Bias\n",
- "aio:UnitNormalization_Layer\n",
- " subject subject_label predicate \\\n",
- "1020 aio:UnitNormalization_Layer UnitNormalization Layer rdfs:subClassOf \n",
- "1021 aio:UnitNormalization_Layer UnitNormalization Layer rdfs:subClassOf \n",
- "1022 aio:UnitNormalization_Layer UnitNormalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1020 None aio:Layer Layer \n",
- "1021 None aio:Recurrent_Layer Recurrent Layer \n",
- "1022 None aio:UnitNormalization_Layer UnitNormalization Layer \n",
- "parent aio:Layer\n",
- "aio:UnitNormalization_Layer\n",
- " subject subject_label predicate \\\n",
- "1020 aio:UnitNormalization_Layer UnitNormalization Layer rdfs:subClassOf \n",
- "1021 aio:UnitNormalization_Layer UnitNormalization Layer rdfs:subClassOf \n",
- "1022 aio:UnitNormalization_Layer UnitNormalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1020 None aio:Layer Layer \n",
- "1021 None aio:Recurrent_Layer Recurrent Layer \n",
- "1022 None aio:UnitNormalization_Layer UnitNormalization Layer \n",
- "parent aio:Layer\n",
- "aio:UnitNormalization_Layer\n",
- " subject subject_label predicate \\\n",
- "1020 aio:UnitNormalization_Layer UnitNormalization Layer rdfs:subClassOf \n",
- "1021 aio:UnitNormalization_Layer UnitNormalization Layer rdfs:subClassOf \n",
- "1022 aio:UnitNormalization_Layer UnitNormalization Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1020 None aio:Layer Layer \n",
- "1021 None aio:Recurrent_Layer Recurrent Layer \n",
- "1022 None aio:UnitNormalization_Layer UnitNormalization Layer \n",
- "parent aio:Layer\n",
- "aio:Unsupervised_Biclustering\n",
- " subject subject_label \\\n",
- "1023 aio:Unsupervised_Biclustering Unsupervised Biclustering \n",
- "1024 aio:Unsupervised_Biclustering Unsupervised Biclustering \n",
- "1025 aio:Unsupervised_Biclustering Unsupervised Biclustering \n",
- "1026 aio:Unsupervised_Biclustering Unsupervised Biclustering \n",
- "\n",
- " predicate predicate_label object \\\n",
- "1023 rdfs:subClassOf None aio:Biclustering \n",
- "1024 rdfs:subClassOf None aio:Machine_Learning \n",
- "1025 rdfs:subClassOf None aio:Method \n",
- "1026 rdfs:subClassOf None aio:Unsupervised_Biclustering \n",
- "\n",
- " object_label \n",
- "1023 Biclustering \n",
- "1024 Machine Learning \n",
- "1025 Method \n",
- "1026 Unsupervised Biclustering \n",
- "parent aio:Biclustering\n",
- "aio:Biclustering\n",
- " subject subject_label predicate predicate_label \\\n",
- "117 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "118 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "119 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "117 aio:Biclustering Biclustering \n",
- "118 aio:Machine_Learning Machine Learning \n",
- "119 aio:Method Method \n",
- "parent aio:Biclustering\n",
- "aio:Unsupervised_Biclustering\n",
- " subject subject_label \\\n",
- "1023 aio:Unsupervised_Biclustering Unsupervised Biclustering \n",
- "1024 aio:Unsupervised_Biclustering Unsupervised Biclustering \n",
- "1025 aio:Unsupervised_Biclustering Unsupervised Biclustering \n",
- "1026 aio:Unsupervised_Biclustering Unsupervised Biclustering \n",
- "\n",
- " predicate predicate_label object \\\n",
- "1023 rdfs:subClassOf None aio:Biclustering \n",
- "1024 rdfs:subClassOf None aio:Machine_Learning \n",
- "1025 rdfs:subClassOf None aio:Method \n",
- "1026 rdfs:subClassOf None aio:Unsupervised_Biclustering \n",
- "\n",
- " object_label \n",
- "1023 Biclustering \n",
- "1024 Machine Learning \n",
- "1025 Method \n",
- "1026 Unsupervised Biclustering \n",
- "parent aio:Biclustering\n",
- "aio:Biclustering\n",
- " subject subject_label predicate predicate_label \\\n",
- "117 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "118 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "119 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "117 aio:Biclustering Biclustering \n",
- "118 aio:Machine_Learning Machine Learning \n",
- "119 aio:Method Method \n",
- "parent aio:Biclustering\n",
- "aio:Unsupervised_Biclustering\n",
- " subject subject_label \\\n",
- "1023 aio:Unsupervised_Biclustering Unsupervised Biclustering \n",
- "1024 aio:Unsupervised_Biclustering Unsupervised Biclustering \n",
- "1025 aio:Unsupervised_Biclustering Unsupervised Biclustering \n",
- "1026 aio:Unsupervised_Biclustering Unsupervised Biclustering \n",
- "\n",
- " predicate predicate_label object \\\n",
- "1023 rdfs:subClassOf None aio:Biclustering \n",
- "1024 rdfs:subClassOf None aio:Machine_Learning \n",
- "1025 rdfs:subClassOf None aio:Method \n",
- "1026 rdfs:subClassOf None aio:Unsupervised_Biclustering \n",
- "\n",
- " object_label \n",
- "1023 Biclustering \n",
- "1024 Machine Learning \n",
- "1025 Method \n",
- "1026 Unsupervised Biclustering \n",
- "parent aio:Biclustering\n",
- "aio:Biclustering\n",
- " subject subject_label predicate predicate_label \\\n",
- "117 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "118 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "119 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "117 aio:Biclustering Biclustering \n",
- "118 aio:Machine_Learning Machine Learning \n",
- "119 aio:Method Method \n",
- "parent aio:Biclustering\n",
- "aio:Unsupervised_Biclustering\n",
- " subject subject_label \\\n",
- "1023 aio:Unsupervised_Biclustering Unsupervised Biclustering \n",
- "1024 aio:Unsupervised_Biclustering Unsupervised Biclustering \n",
- "1025 aio:Unsupervised_Biclustering Unsupervised Biclustering \n",
- "1026 aio:Unsupervised_Biclustering Unsupervised Biclustering \n",
- "\n",
- " predicate predicate_label object \\\n",
- "1023 rdfs:subClassOf None aio:Biclustering \n",
- "1024 rdfs:subClassOf None aio:Machine_Learning \n",
- "1025 rdfs:subClassOf None aio:Method \n",
- "1026 rdfs:subClassOf None aio:Unsupervised_Biclustering \n",
- "\n",
- " object_label \n",
- "1023 Biclustering \n",
- "1024 Machine Learning \n",
- "1025 Method \n",
- "1026 Unsupervised Biclustering \n",
- "parent aio:Biclustering\n",
- "aio:Biclustering\n",
- " subject subject_label predicate predicate_label \\\n",
- "117 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "118 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "119 aio:Biclustering Biclustering rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "117 aio:Biclustering Biclustering \n",
- "118 aio:Machine_Learning Machine Learning \n",
- "119 aio:Method Method \n",
- "parent aio:Biclustering\n",
- "aio:Unsupervised_Clustering\n",
- " subject subject_label predicate \\\n",
- "1027 aio:Unsupervised_Clustering Unsupervised Clustering rdfs:subClassOf \n",
- "1028 aio:Unsupervised_Clustering Unsupervised Clustering rdfs:subClassOf \n",
- "1029 aio:Unsupervised_Clustering Unsupervised Clustering rdfs:subClassOf \n",
- "1030 aio:Unsupervised_Clustering Unsupervised Clustering rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1027 None aio:Clustering Clustering \n",
- "1028 None aio:Machine_Learning Machine Learning \n",
- "1029 None aio:Method Method \n",
- "1030 None aio:Unsupervised_Clustering Unsupervised Clustering \n",
- "parent aio:Clustering\n",
- "aio:Clustering\n",
- " subject subject_label predicate predicate_label \\\n",
- "144 aio:Clustering Clustering rdfs:subClassOf None \n",
- "145 aio:Clustering Clustering rdfs:subClassOf None \n",
- "146 aio:Clustering Clustering rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "144 aio:Clustering Clustering \n",
- "145 aio:Machine_Learning Machine Learning \n",
- "146 aio:Method Method \n",
- "parent aio:Clustering\n",
- "aio:Unsupervised_Clustering\n",
- " subject subject_label predicate \\\n",
- "1027 aio:Unsupervised_Clustering Unsupervised Clustering rdfs:subClassOf \n",
- "1028 aio:Unsupervised_Clustering Unsupervised Clustering rdfs:subClassOf \n",
- "1029 aio:Unsupervised_Clustering Unsupervised Clustering rdfs:subClassOf \n",
- "1030 aio:Unsupervised_Clustering Unsupervised Clustering rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1027 None aio:Clustering Clustering \n",
- "1028 None aio:Machine_Learning Machine Learning \n",
- "1029 None aio:Method Method \n",
- "1030 None aio:Unsupervised_Clustering Unsupervised Clustering \n",
- "parent aio:Clustering\n",
- "aio:Clustering\n",
- " subject subject_label predicate predicate_label \\\n",
- "144 aio:Clustering Clustering rdfs:subClassOf None \n",
- "145 aio:Clustering Clustering rdfs:subClassOf None \n",
- "146 aio:Clustering Clustering rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "144 aio:Clustering Clustering \n",
- "145 aio:Machine_Learning Machine Learning \n",
- "146 aio:Method Method \n",
- "parent aio:Clustering\n",
- "aio:Unsupervised_Clustering\n",
- " subject subject_label predicate \\\n",
- "1027 aio:Unsupervised_Clustering Unsupervised Clustering rdfs:subClassOf \n",
- "1028 aio:Unsupervised_Clustering Unsupervised Clustering rdfs:subClassOf \n",
- "1029 aio:Unsupervised_Clustering Unsupervised Clustering rdfs:subClassOf \n",
- "1030 aio:Unsupervised_Clustering Unsupervised Clustering rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1027 None aio:Clustering Clustering \n",
- "1028 None aio:Machine_Learning Machine Learning \n",
- "1029 None aio:Method Method \n",
- "1030 None aio:Unsupervised_Clustering Unsupervised Clustering \n",
- "parent aio:Clustering\n",
- "aio:Clustering\n",
- " subject subject_label predicate predicate_label \\\n",
- "144 aio:Clustering Clustering rdfs:subClassOf None \n",
- "145 aio:Clustering Clustering rdfs:subClassOf None \n",
- "146 aio:Clustering Clustering rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "144 aio:Clustering Clustering \n",
- "145 aio:Machine_Learning Machine Learning \n",
- "146 aio:Method Method \n",
- "parent aio:Clustering\n",
- "aio:Unsupervised_Clustering\n",
- " subject subject_label predicate \\\n",
- "1027 aio:Unsupervised_Clustering Unsupervised Clustering rdfs:subClassOf \n",
- "1028 aio:Unsupervised_Clustering Unsupervised Clustering rdfs:subClassOf \n",
- "1029 aio:Unsupervised_Clustering Unsupervised Clustering rdfs:subClassOf \n",
- "1030 aio:Unsupervised_Clustering Unsupervised Clustering rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1027 None aio:Clustering Clustering \n",
- "1028 None aio:Machine_Learning Machine Learning \n",
- "1029 None aio:Method Method \n",
- "1030 None aio:Unsupervised_Clustering Unsupervised Clustering \n",
- "parent aio:Clustering\n",
- "aio:Clustering\n",
- " subject subject_label predicate predicate_label \\\n",
- "144 aio:Clustering Clustering rdfs:subClassOf None \n",
- "145 aio:Clustering Clustering rdfs:subClassOf None \n",
- "146 aio:Clustering Clustering rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "144 aio:Clustering Clustering \n",
- "145 aio:Machine_Learning Machine Learning \n",
- "146 aio:Method Method \n",
- "parent aio:Clustering\n",
- "aio:Unsupervised_Learning\n",
- " subject subject_label predicate \\\n",
- "1031 aio:Unsupervised_Learning Unsupervised Learning rdfs:subClassOf \n",
- "1032 aio:Unsupervised_Learning Unsupervised Learning rdfs:subClassOf \n",
- "1033 aio:Unsupervised_Learning Unsupervised Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1031 None aio:Machine_Learning Machine Learning \n",
- "1032 None aio:Method Method \n",
- "1033 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Unsupervised_Learning\n",
- " subject subject_label predicate \\\n",
- "1031 aio:Unsupervised_Learning Unsupervised Learning rdfs:subClassOf \n",
- "1032 aio:Unsupervised_Learning Unsupervised Learning rdfs:subClassOf \n",
- "1033 aio:Unsupervised_Learning Unsupervised Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1031 None aio:Machine_Learning Machine Learning \n",
- "1032 None aio:Method Method \n",
- "1033 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:Unsupervised_Learning\n",
- " subject subject_label predicate \\\n",
- "1031 aio:Unsupervised_Learning Unsupervised Learning rdfs:subClassOf \n",
- "1032 aio:Unsupervised_Learning Unsupervised Learning rdfs:subClassOf \n",
- "1033 aio:Unsupervised_Learning Unsupervised Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1031 None aio:Machine_Learning Machine Learning \n",
- "1032 None aio:Method Method \n",
- "1033 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Machine_Learning\n",
- "aio:UpSampling1D_Layer\n",
- " subject subject_label predicate \\\n",
- "1034 aio:UpSampling1D_Layer UpSampling1D Layer rdfs:subClassOf \n",
- "1035 aio:UpSampling1D_Layer UpSampling1D Layer rdfs:subClassOf \n",
- "1036 aio:UpSampling1D_Layer UpSampling1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1034 None aio:Layer Layer \n",
- "1035 None aio:Reshaping_Layer Reshaping Layer \n",
- "1036 None aio:UpSampling1D_Layer UpSampling1D Layer \n",
- "parent aio:Layer\n",
- "aio:UpSampling1D_Layer\n",
- " subject subject_label predicate \\\n",
- "1034 aio:UpSampling1D_Layer UpSampling1D Layer rdfs:subClassOf \n",
- "1035 aio:UpSampling1D_Layer UpSampling1D Layer rdfs:subClassOf \n",
- "1036 aio:UpSampling1D_Layer UpSampling1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1034 None aio:Layer Layer \n",
- "1035 None aio:Reshaping_Layer Reshaping Layer \n",
- "1036 None aio:UpSampling1D_Layer UpSampling1D Layer \n",
- "parent aio:Layer\n",
- "aio:UpSampling1D_Layer\n",
- " subject subject_label predicate \\\n",
- "1034 aio:UpSampling1D_Layer UpSampling1D Layer rdfs:subClassOf \n",
- "1035 aio:UpSampling1D_Layer UpSampling1D Layer rdfs:subClassOf \n",
- "1036 aio:UpSampling1D_Layer UpSampling1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1034 None aio:Layer Layer \n",
- "1035 None aio:Reshaping_Layer Reshaping Layer \n",
- "1036 None aio:UpSampling1D_Layer UpSampling1D Layer \n",
- "parent aio:Layer\n",
- "aio:UpSampling2D_Layer\n",
- " subject subject_label predicate \\\n",
- "1037 aio:UpSampling2D_Layer UpSampling2D Layer rdfs:subClassOf \n",
- "1038 aio:UpSampling2D_Layer UpSampling2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1037 None aio:Layer Layer \n",
- "1038 None aio:UpSampling2D_Layer UpSampling2D Layer \n",
- "parent aio:Layer\n",
- "aio:UpSampling2D_Layer\n",
- " subject subject_label predicate \\\n",
- "1037 aio:UpSampling2D_Layer UpSampling2D Layer rdfs:subClassOf \n",
- "1038 aio:UpSampling2D_Layer UpSampling2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1037 None aio:Layer Layer \n",
- "1038 None aio:UpSampling2D_Layer UpSampling2D Layer \n",
- "parent aio:Layer\n",
- "aio:UpSampling3D_Layer\n",
- " subject subject_label predicate \\\n",
- "1039 aio:UpSampling3D_Layer UpSampling3D Layer rdfs:subClassOf \n",
- "1040 aio:UpSampling3D_Layer UpSampling3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1039 None aio:Layer Layer \n",
- "1040 None aio:UpSampling3D_Layer UpSampling3D Layer \n",
- "parent aio:Layer\n",
- "aio:UpSampling3D_Layer\n",
- " subject subject_label predicate \\\n",
- "1039 aio:UpSampling3D_Layer UpSampling3D Layer rdfs:subClassOf \n",
- "1040 aio:UpSampling3D_Layer UpSampling3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1039 None aio:Layer Layer \n",
- "1040 None aio:UpSampling3D_Layer UpSampling3D Layer \n",
- "parent aio:Layer\n",
- "aio:Use_And_Interpretation_Bias\n",
- " subject subject_label \\\n",
- "1041 aio:Use_And_Interpretation_Bias Use And Interpretation Bias \n",
- "1042 aio:Use_And_Interpretation_Bias Use And Interpretation Bias \n",
- "1043 aio:Use_And_Interpretation_Bias Use And Interpretation Bias \n",
- "\n",
- " predicate predicate_label object \\\n",
- "1041 rdfs:subClassOf None aio:Bias \n",
- "1042 rdfs:subClassOf None aio:Computational_Bias \n",
- "1043 rdfs:subClassOf None aio:Use_And_Interpretation_Bias \n",
- "\n",
- " object_label \n",
- "1041 Bias \n",
- "1042 Computational Bias \n",
- "1043 Use And Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:Use_And_Interpretation_Bias\n",
- " subject subject_label \\\n",
- "1041 aio:Use_And_Interpretation_Bias Use And Interpretation Bias \n",
- "1042 aio:Use_And_Interpretation_Bias Use And Interpretation Bias \n",
- "1043 aio:Use_And_Interpretation_Bias Use And Interpretation Bias \n",
- "\n",
- " predicate predicate_label object \\\n",
- "1041 rdfs:subClassOf None aio:Bias \n",
- "1042 rdfs:subClassOf None aio:Computational_Bias \n",
- "1043 rdfs:subClassOf None aio:Use_And_Interpretation_Bias \n",
- "\n",
- " object_label \n",
- "1041 Bias \n",
- "1042 Computational Bias \n",
- "1043 Use And Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:Use_And_Interpretation_Bias\n",
- " subject subject_label \\\n",
- "1041 aio:Use_And_Interpretation_Bias Use And Interpretation Bias \n",
- "1042 aio:Use_And_Interpretation_Bias Use And Interpretation Bias \n",
- "1043 aio:Use_And_Interpretation_Bias Use And Interpretation Bias \n",
- "\n",
- " predicate predicate_label object \\\n",
- "1041 rdfs:subClassOf None aio:Bias \n",
- "1042 rdfs:subClassOf None aio:Computational_Bias \n",
- "1043 rdfs:subClassOf None aio:Use_And_Interpretation_Bias \n",
- "\n",
- " object_label \n",
- "1041 Bias \n",
- "1042 Computational Bias \n",
- "1043 Use And Interpretation Bias \n",
- "parent aio:Bias\n",
- "aio:User_Interaction_Bias\n",
- " subject subject_label predicate \\\n",
- "1044 aio:User_Interaction_Bias User Interaction Bias rdfs:subClassOf \n",
- "1045 aio:User_Interaction_Bias User Interaction Bias rdfs:subClassOf \n",
- "1046 aio:User_Interaction_Bias User Interaction Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1044 None aio:Bias Bias \n",
- "1045 None aio:Individual_Bias Individual Bias \n",
- "1046 None aio:User_Interaction_Bias User Interaction Bias \n",
- "parent aio:Bias\n",
- "aio:User_Interaction_Bias\n",
- " subject subject_label predicate \\\n",
- "1044 aio:User_Interaction_Bias User Interaction Bias rdfs:subClassOf \n",
- "1045 aio:User_Interaction_Bias User Interaction Bias rdfs:subClassOf \n",
- "1046 aio:User_Interaction_Bias User Interaction Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1044 None aio:Bias Bias \n",
- "1045 None aio:Individual_Bias Individual Bias \n",
- "1046 None aio:User_Interaction_Bias User Interaction Bias \n",
- "parent aio:Bias\n",
- "aio:User_Interaction_Bias\n",
- " subject subject_label predicate \\\n",
- "1044 aio:User_Interaction_Bias User Interaction Bias rdfs:subClassOf \n",
- "1045 aio:User_Interaction_Bias User Interaction Bias rdfs:subClassOf \n",
- "1046 aio:User_Interaction_Bias User Interaction Bias rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1044 None aio:Bias Bias \n",
- "1045 None aio:Individual_Bias Individual Bias \n",
- "1046 None aio:User_Interaction_Bias User Interaction Bias \n",
- "parent aio:Bias\n",
- "aio:VAE\n",
- " subject subject_label predicate predicate_label \\\n",
- "1047 aio:VAE Variational Auto Encoder rdfs:subClassOf None \n",
- "1048 aio:VAE Variational Auto Encoder rdfs:subClassOf None \n",
- "1049 aio:VAE Variational Auto Encoder rdfs:subClassOf None \n",
- "1050 aio:VAE Variational Auto Encoder rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "1047 aio:AE Auto Encoder Network \n",
- "1048 aio:Network Network \n",
- "1049 aio:UPN Unsupervised Pretrained Network \n",
- "1050 aio:VAE Variational Auto Encoder \n",
- "parent aio:AE\n",
- "aio:AE\n",
- " subject subject_label predicate predicate_label object \\\n",
- "0 aio:AE Auto Encoder Network rdfs:subClassOf None aio:AE \n",
- "1 aio:AE Auto Encoder Network rdfs:subClassOf None aio:Network \n",
- "2 aio:AE Auto Encoder Network rdfs:subClassOf None aio:UPN \n",
- "\n",
- " object_label \n",
- "0 Auto Encoder Network \n",
- "1 Network \n",
- "2 Unsupervised Pretrained Network \n",
- "parent aio:AE\n",
- "aio:VAE\n",
- " subject subject_label predicate predicate_label \\\n",
- "1047 aio:VAE Variational Auto Encoder rdfs:subClassOf None \n",
- "1048 aio:VAE Variational Auto Encoder rdfs:subClassOf None \n",
- "1049 aio:VAE Variational Auto Encoder rdfs:subClassOf None \n",
- "1050 aio:VAE Variational Auto Encoder rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "1047 aio:AE Auto Encoder Network \n",
- "1048 aio:Network Network \n",
- "1049 aio:UPN Unsupervised Pretrained Network \n",
- "1050 aio:VAE Variational Auto Encoder \n",
- "parent aio:AE\n",
- "aio:AE\n",
- " subject subject_label predicate predicate_label object \\\n",
- "0 aio:AE Auto Encoder Network rdfs:subClassOf None aio:AE \n",
- "1 aio:AE Auto Encoder Network rdfs:subClassOf None aio:Network \n",
- "2 aio:AE Auto Encoder Network rdfs:subClassOf None aio:UPN \n",
- "\n",
- " object_label \n",
- "0 Auto Encoder Network \n",
- "1 Network \n",
- "2 Unsupervised Pretrained Network \n",
- "parent aio:AE\n",
- "aio:VAE\n",
- " subject subject_label predicate predicate_label \\\n",
- "1047 aio:VAE Variational Auto Encoder rdfs:subClassOf None \n",
- "1048 aio:VAE Variational Auto Encoder rdfs:subClassOf None \n",
- "1049 aio:VAE Variational Auto Encoder rdfs:subClassOf None \n",
- "1050 aio:VAE Variational Auto Encoder rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "1047 aio:AE Auto Encoder Network \n",
- "1048 aio:Network Network \n",
- "1049 aio:UPN Unsupervised Pretrained Network \n",
- "1050 aio:VAE Variational Auto Encoder \n",
- "parent aio:AE\n",
- "aio:AE\n",
- " subject subject_label predicate predicate_label object \\\n",
- "0 aio:AE Auto Encoder Network rdfs:subClassOf None aio:AE \n",
- "1 aio:AE Auto Encoder Network rdfs:subClassOf None aio:Network \n",
- "2 aio:AE Auto Encoder Network rdfs:subClassOf None aio:UPN \n",
- "\n",
- " object_label \n",
- "0 Auto Encoder Network \n",
- "1 Network \n",
- "2 Unsupervised Pretrained Network \n",
- "parent aio:AE\n",
- "aio:VAE\n",
- " subject subject_label predicate predicate_label \\\n",
- "1047 aio:VAE Variational Auto Encoder rdfs:subClassOf None \n",
- "1048 aio:VAE Variational Auto Encoder rdfs:subClassOf None \n",
- "1049 aio:VAE Variational Auto Encoder rdfs:subClassOf None \n",
- "1050 aio:VAE Variational Auto Encoder rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "1047 aio:AE Auto Encoder Network \n",
- "1048 aio:Network Network \n",
- "1049 aio:UPN Unsupervised Pretrained Network \n",
- "1050 aio:VAE Variational Auto Encoder \n",
- "parent aio:AE\n",
- "aio:AE\n",
- " subject subject_label predicate predicate_label object \\\n",
- "0 aio:AE Auto Encoder Network rdfs:subClassOf None aio:AE \n",
- "1 aio:AE Auto Encoder Network rdfs:subClassOf None aio:Network \n",
- "2 aio:AE Auto Encoder Network rdfs:subClassOf None aio:UPN \n",
- "\n",
- " object_label \n",
- "0 Auto Encoder Network \n",
- "1 Network \n",
- "2 Unsupervised Pretrained Network \n",
- "parent aio:AE\n",
- "aio:W2V_CBOW\n",
- " subject subject_label predicate predicate_label \\\n",
- "1051 aio:W2V_CBOW word2vec-CBOW rdfs:subClassOf None \n",
- "1052 aio:W2V_CBOW word2vec-CBOW rdfs:subClassOf None \n",
- "1053 aio:W2V_CBOW word2vec-CBOW rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "1051 aio:ANN Artificial Neural Network \n",
- "1052 aio:Network Network \n",
- "1053 aio:W2V_CBOW word2vec-CBOW \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:W2V_CBOW\n",
- " subject subject_label predicate predicate_label \\\n",
- "1051 aio:W2V_CBOW word2vec-CBOW rdfs:subClassOf None \n",
- "1052 aio:W2V_CBOW word2vec-CBOW rdfs:subClassOf None \n",
- "1053 aio:W2V_CBOW word2vec-CBOW rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "1051 aio:ANN Artificial Neural Network \n",
- "1052 aio:Network Network \n",
- "1053 aio:W2V_CBOW word2vec-CBOW \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:W2V_CBOW\n",
- " subject subject_label predicate predicate_label \\\n",
- "1051 aio:W2V_CBOW word2vec-CBOW rdfs:subClassOf None \n",
- "1052 aio:W2V_CBOW word2vec-CBOW rdfs:subClassOf None \n",
- "1053 aio:W2V_CBOW word2vec-CBOW rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "1051 aio:ANN Artificial Neural Network \n",
- "1052 aio:Network Network \n",
- "1053 aio:W2V_CBOW word2vec-CBOW \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:W2V_SkipGram\n",
- " subject subject_label predicate predicate_label \\\n",
- "1054 aio:W2V_SkipGram word2vec-SkipGram rdfs:subClassOf None \n",
- "1055 aio:W2V_SkipGram word2vec-SkipGram rdfs:subClassOf None \n",
- "1056 aio:W2V_SkipGram word2vec-SkipGram rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "1054 aio:ANN Artificial Neural Network \n",
- "1055 aio:Network Network \n",
- "1056 aio:W2V_SkipGram word2vec-SkipGram \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:W2V_SkipGram\n",
- " subject subject_label predicate predicate_label \\\n",
- "1054 aio:W2V_SkipGram word2vec-SkipGram rdfs:subClassOf None \n",
- "1055 aio:W2V_SkipGram word2vec-SkipGram rdfs:subClassOf None \n",
- "1056 aio:W2V_SkipGram word2vec-SkipGram rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "1054 aio:ANN Artificial Neural Network \n",
- "1055 aio:Network Network \n",
- "1056 aio:W2V_SkipGram word2vec-SkipGram \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:W2V_SkipGram\n",
- " subject subject_label predicate predicate_label \\\n",
- "1054 aio:W2V_SkipGram word2vec-SkipGram rdfs:subClassOf None \n",
- "1055 aio:W2V_SkipGram word2vec-SkipGram rdfs:subClassOf None \n",
- "1056 aio:W2V_SkipGram word2vec-SkipGram rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "1054 aio:ANN Artificial Neural Network \n",
- "1055 aio:Network Network \n",
- "1056 aio:W2V_SkipGram word2vec-SkipGram \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Wrapper_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "1057 aio:Wrapper_Layer Wrapper Layer rdfs:subClassOf None \n",
- "1058 aio:Wrapper_Layer Wrapper Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "1057 aio:Layer Layer \n",
- "1058 aio:Wrapper_Layer Wrapper Layer \n",
- "parent aio:Layer\n",
- "aio:Wrapper_Layer\n",
- " subject subject_label predicate predicate_label \\\n",
- "1057 aio:Wrapper_Layer Wrapper Layer rdfs:subClassOf None \n",
- "1058 aio:Wrapper_Layer Wrapper Layer rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "1057 aio:Layer Layer \n",
- "1058 aio:Wrapper_Layer Wrapper Layer \n",
- "parent aio:Layer\n",
- "aio:Zero-shot_Learning\n",
- " subject subject_label predicate \\\n",
- "1059 aio:Zero-shot_Learning Zero-shot Learning rdfs:subClassOf \n",
- "1060 aio:Zero-shot_Learning Zero-shot Learning rdfs:subClassOf \n",
- "1061 aio:Zero-shot_Learning Zero-shot Learning rdfs:subClassOf \n",
- "1062 aio:Zero-shot_Learning Zero-shot Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1059 None aio:ANN Artificial Neural Network \n",
- "1060 None aio:DNN Deep Neural Network \n",
- "1061 None aio:Network Network \n",
- "1062 None aio:Zero-shot_Learning Zero-shot Learning \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Zero-shot_Learning\n",
- " subject subject_label predicate \\\n",
- "1059 aio:Zero-shot_Learning Zero-shot Learning rdfs:subClassOf \n",
- "1060 aio:Zero-shot_Learning Zero-shot Learning rdfs:subClassOf \n",
- "1061 aio:Zero-shot_Learning Zero-shot Learning rdfs:subClassOf \n",
- "1062 aio:Zero-shot_Learning Zero-shot Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1059 None aio:ANN Artificial Neural Network \n",
- "1060 None aio:DNN Deep Neural Network \n",
- "1061 None aio:Network Network \n",
- "1062 None aio:Zero-shot_Learning Zero-shot Learning \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Zero-shot_Learning\n",
- " subject subject_label predicate \\\n",
- "1059 aio:Zero-shot_Learning Zero-shot Learning rdfs:subClassOf \n",
- "1060 aio:Zero-shot_Learning Zero-shot Learning rdfs:subClassOf \n",
- "1061 aio:Zero-shot_Learning Zero-shot Learning rdfs:subClassOf \n",
- "1062 aio:Zero-shot_Learning Zero-shot Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1059 None aio:ANN Artificial Neural Network \n",
- "1060 None aio:DNN Deep Neural Network \n",
- "1061 None aio:Network Network \n",
- "1062 None aio:Zero-shot_Learning Zero-shot Learning \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:Zero-shot_Learning\n",
- " subject subject_label predicate \\\n",
- "1059 aio:Zero-shot_Learning Zero-shot Learning rdfs:subClassOf \n",
- "1060 aio:Zero-shot_Learning Zero-shot Learning rdfs:subClassOf \n",
- "1061 aio:Zero-shot_Learning Zero-shot Learning rdfs:subClassOf \n",
- "1062 aio:Zero-shot_Learning Zero-shot Learning rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1059 None aio:ANN Artificial Neural Network \n",
- "1060 None aio:DNN Deep Neural Network \n",
- "1061 None aio:Network Network \n",
- "1062 None aio:Zero-shot_Learning Zero-shot Learning \n",
- "parent aio:ANN\n",
- "aio:ANN\n",
- " subject subject_label predicate predicate_label \\\n",
- "3 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "4 aio:ANN Artificial Neural Network rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "3 aio:ANN Artificial Neural Network \n",
- "4 aio:Network Network \n",
- "parent aio:ANN\n",
- "aio:ZeroPadding1D_Layer\n",
- " subject subject_label predicate \\\n",
- "1063 aio:ZeroPadding1D_Layer ZeroPadding1D Layer rdfs:subClassOf \n",
- "1064 aio:ZeroPadding1D_Layer ZeroPadding1D Layer rdfs:subClassOf \n",
- "1065 aio:ZeroPadding1D_Layer ZeroPadding1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1063 None aio:Layer Layer \n",
- "1064 None aio:Reshaping_Layer Reshaping Layer \n",
- "1065 None aio:ZeroPadding1D_Layer ZeroPadding1D Layer \n",
- "parent aio:Layer\n",
- "aio:ZeroPadding1D_Layer\n",
- " subject subject_label predicate \\\n",
- "1063 aio:ZeroPadding1D_Layer ZeroPadding1D Layer rdfs:subClassOf \n",
- "1064 aio:ZeroPadding1D_Layer ZeroPadding1D Layer rdfs:subClassOf \n",
- "1065 aio:ZeroPadding1D_Layer ZeroPadding1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1063 None aio:Layer Layer \n",
- "1064 None aio:Reshaping_Layer Reshaping Layer \n",
- "1065 None aio:ZeroPadding1D_Layer ZeroPadding1D Layer \n",
- "parent aio:Layer\n",
- "aio:ZeroPadding1D_Layer\n",
- " subject subject_label predicate \\\n",
- "1063 aio:ZeroPadding1D_Layer ZeroPadding1D Layer rdfs:subClassOf \n",
- "1064 aio:ZeroPadding1D_Layer ZeroPadding1D Layer rdfs:subClassOf \n",
- "1065 aio:ZeroPadding1D_Layer ZeroPadding1D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1063 None aio:Layer Layer \n",
- "1064 None aio:Reshaping_Layer Reshaping Layer \n",
- "1065 None aio:ZeroPadding1D_Layer ZeroPadding1D Layer \n",
- "parent aio:Layer\n",
- "aio:ZeroPadding2D_Layer\n",
- " subject subject_label predicate \\\n",
- "1066 aio:ZeroPadding2D_Layer ZeroPadding2D Layer rdfs:subClassOf \n",
- "1067 aio:ZeroPadding2D_Layer ZeroPadding2D Layer rdfs:subClassOf \n",
- "1068 aio:ZeroPadding2D_Layer ZeroPadding2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1066 None aio:Layer Layer \n",
- "1067 None aio:Reshaping_Layer Reshaping Layer \n",
- "1068 None aio:ZeroPadding2D_Layer ZeroPadding2D Layer \n",
- "parent aio:Layer\n",
- "aio:ZeroPadding2D_Layer\n",
- " subject subject_label predicate \\\n",
- "1066 aio:ZeroPadding2D_Layer ZeroPadding2D Layer rdfs:subClassOf \n",
- "1067 aio:ZeroPadding2D_Layer ZeroPadding2D Layer rdfs:subClassOf \n",
- "1068 aio:ZeroPadding2D_Layer ZeroPadding2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1066 None aio:Layer Layer \n",
- "1067 None aio:Reshaping_Layer Reshaping Layer \n",
- "1068 None aio:ZeroPadding2D_Layer ZeroPadding2D Layer \n",
- "parent aio:Layer\n",
- "aio:ZeroPadding2D_Layer\n",
- " subject subject_label predicate \\\n",
- "1066 aio:ZeroPadding2D_Layer ZeroPadding2D Layer rdfs:subClassOf \n",
- "1067 aio:ZeroPadding2D_Layer ZeroPadding2D Layer rdfs:subClassOf \n",
- "1068 aio:ZeroPadding2D_Layer ZeroPadding2D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1066 None aio:Layer Layer \n",
- "1067 None aio:Reshaping_Layer Reshaping Layer \n",
- "1068 None aio:ZeroPadding2D_Layer ZeroPadding2D Layer \n",
- "parent aio:Layer\n",
- "aio:ZeroPadding3D_Layer\n",
- " subject subject_label predicate \\\n",
- "1069 aio:ZeroPadding3D_Layer ZeroPadding3D Layer rdfs:subClassOf \n",
- "1070 aio:ZeroPadding3D_Layer ZeroPadding3D Layer rdfs:subClassOf \n",
- "1071 aio:ZeroPadding3D_Layer ZeroPadding3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1069 None aio:Layer Layer \n",
- "1070 None aio:Reshaping_Layer Reshaping Layer \n",
- "1071 None aio:ZeroPadding3D_Layer ZeroPadding3D Layer \n",
- "parent aio:Layer\n",
- "aio:ZeroPadding3D_Layer\n",
- " subject subject_label predicate \\\n",
- "1069 aio:ZeroPadding3D_Layer ZeroPadding3D Layer rdfs:subClassOf \n",
- "1070 aio:ZeroPadding3D_Layer ZeroPadding3D Layer rdfs:subClassOf \n",
- "1071 aio:ZeroPadding3D_Layer ZeroPadding3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1069 None aio:Layer Layer \n",
- "1070 None aio:Reshaping_Layer Reshaping Layer \n",
- "1071 None aio:ZeroPadding3D_Layer ZeroPadding3D Layer \n",
- "parent aio:Layer\n",
- "aio:ZeroPadding3D_Layer\n",
- " subject subject_label predicate \\\n",
- "1069 aio:ZeroPadding3D_Layer ZeroPadding3D Layer rdfs:subClassOf \n",
- "1070 aio:ZeroPadding3D_Layer ZeroPadding3D Layer rdfs:subClassOf \n",
- "1071 aio:ZeroPadding3D_Layer ZeroPadding3D Layer rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "1069 None aio:Layer Layer \n",
- "1070 None aio:Reshaping_Layer Reshaping Layer \n",
- "1071 None aio:ZeroPadding3D_Layer ZeroPadding3D Layer \n",
- "parent aio:Layer\n",
- "aio:eLu_Function\n",
- " subject subject_label predicate predicate_label \\\n",
- "1072 aio:eLu_Function ELU Function rdfs:subClassOf None \n",
- "1073 aio:eLu_Function ELU Function rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "1072 aio:Function None \n",
- "1073 aio:eLu_Function ELU Function \n",
- "parent aio:Function\n",
- "aio:eLu_Function\n",
- " subject subject_label predicate predicate_label \\\n",
- "1072 aio:eLu_Function ELU Function rdfs:subClassOf None \n",
- "1073 aio:eLu_Function ELU Function rdfs:subClassOf None \n",
- "\n",
- " object object_label \n",
- "1072 aio:Function None \n",
- "1073 aio:eLu_Function ELU Function \n",
- "parent aio:Function\n",
- "aio:t-Distributed_Stochastic_Neighbor_embedding\n",
- " subject \\\n",
- "1074 aio:t-Distributed_Stochastic_Neighbor_embedding \n",
- "1075 aio:t-Distributed_Stochastic_Neighbor_embedding \n",
- "1076 aio:t-Distributed_Stochastic_Neighbor_embedding \n",
- "1077 aio:t-Distributed_Stochastic_Neighbor_embedding \n",
- "1078 aio:t-Distributed_Stochastic_Neighbor_embedding \n",
- "\n",
- " subject_label predicate \\\n",
- "1074 t-Distributed Stochastic Neighbor embedding rdfs:subClassOf \n",
- "1075 t-Distributed Stochastic Neighbor embedding rdfs:subClassOf \n",
- "1076 t-Distributed Stochastic Neighbor embedding rdfs:subClassOf \n",
- "1077 t-Distributed Stochastic Neighbor embedding rdfs:subClassOf \n",
- "1078 t-Distributed Stochastic Neighbor embedding rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "1074 None aio:Dimensionality_Reduction \n",
- "1075 None aio:Machine_Learning \n",
- "1076 None aio:Method \n",
- "1077 None aio:Unsupervised_Learning \n",
- "1078 None aio:t-Distributed_Stochastic_Neighbor_embedding \n",
- "\n",
- " object_label \n",
- "1074 Dimensionality Reduction \n",
- "1075 Machine Learning \n",
- "1076 Method \n",
- "1077 Unsupervised Learning \n",
- "1078 t-Distributed Stochastic Neighbor embedding \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Dimensionality_Reduction\n",
- " subject subject_label predicate \\\n",
- "275 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "276 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "277 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "278 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "275 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "276 None aio:Machine_Learning Machine Learning \n",
- "277 None aio:Method Method \n",
- "278 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:t-Distributed_Stochastic_Neighbor_embedding\n",
- " subject \\\n",
- "1074 aio:t-Distributed_Stochastic_Neighbor_embedding \n",
- "1075 aio:t-Distributed_Stochastic_Neighbor_embedding \n",
- "1076 aio:t-Distributed_Stochastic_Neighbor_embedding \n",
- "1077 aio:t-Distributed_Stochastic_Neighbor_embedding \n",
- "1078 aio:t-Distributed_Stochastic_Neighbor_embedding \n",
- "\n",
- " subject_label predicate \\\n",
- "1074 t-Distributed Stochastic Neighbor embedding rdfs:subClassOf \n",
- "1075 t-Distributed Stochastic Neighbor embedding rdfs:subClassOf \n",
- "1076 t-Distributed Stochastic Neighbor embedding rdfs:subClassOf \n",
- "1077 t-Distributed Stochastic Neighbor embedding rdfs:subClassOf \n",
- "1078 t-Distributed Stochastic Neighbor embedding rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "1074 None aio:Dimensionality_Reduction \n",
- "1075 None aio:Machine_Learning \n",
- "1076 None aio:Method \n",
- "1077 None aio:Unsupervised_Learning \n",
- "1078 None aio:t-Distributed_Stochastic_Neighbor_embedding \n",
- "\n",
- " object_label \n",
- "1074 Dimensionality Reduction \n",
- "1075 Machine Learning \n",
- "1076 Method \n",
- "1077 Unsupervised Learning \n",
- "1078 t-Distributed Stochastic Neighbor embedding \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Dimensionality_Reduction\n",
- " subject subject_label predicate \\\n",
- "275 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "276 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "277 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "278 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "275 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "276 None aio:Machine_Learning Machine Learning \n",
- "277 None aio:Method Method \n",
- "278 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:t-Distributed_Stochastic_Neighbor_embedding\n",
- " subject \\\n",
- "1074 aio:t-Distributed_Stochastic_Neighbor_embedding \n",
- "1075 aio:t-Distributed_Stochastic_Neighbor_embedding \n",
- "1076 aio:t-Distributed_Stochastic_Neighbor_embedding \n",
- "1077 aio:t-Distributed_Stochastic_Neighbor_embedding \n",
- "1078 aio:t-Distributed_Stochastic_Neighbor_embedding \n",
- "\n",
- " subject_label predicate \\\n",
- "1074 t-Distributed Stochastic Neighbor embedding rdfs:subClassOf \n",
- "1075 t-Distributed Stochastic Neighbor embedding rdfs:subClassOf \n",
- "1076 t-Distributed Stochastic Neighbor embedding rdfs:subClassOf \n",
- "1077 t-Distributed Stochastic Neighbor embedding rdfs:subClassOf \n",
- "1078 t-Distributed Stochastic Neighbor embedding rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "1074 None aio:Dimensionality_Reduction \n",
- "1075 None aio:Machine_Learning \n",
- "1076 None aio:Method \n",
- "1077 None aio:Unsupervised_Learning \n",
- "1078 None aio:t-Distributed_Stochastic_Neighbor_embedding \n",
- "\n",
- " object_label \n",
- "1074 Dimensionality Reduction \n",
- "1075 Machine Learning \n",
- "1076 Method \n",
- "1077 Unsupervised Learning \n",
- "1078 t-Distributed Stochastic Neighbor embedding \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Dimensionality_Reduction\n",
- " subject subject_label predicate \\\n",
- "275 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "276 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "277 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "278 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "275 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "276 None aio:Machine_Learning Machine Learning \n",
- "277 None aio:Method Method \n",
- "278 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:t-Distributed_Stochastic_Neighbor_embedding\n",
- " subject \\\n",
- "1074 aio:t-Distributed_Stochastic_Neighbor_embedding \n",
- "1075 aio:t-Distributed_Stochastic_Neighbor_embedding \n",
- "1076 aio:t-Distributed_Stochastic_Neighbor_embedding \n",
- "1077 aio:t-Distributed_Stochastic_Neighbor_embedding \n",
- "1078 aio:t-Distributed_Stochastic_Neighbor_embedding \n",
- "\n",
- " subject_label predicate \\\n",
- "1074 t-Distributed Stochastic Neighbor embedding rdfs:subClassOf \n",
- "1075 t-Distributed Stochastic Neighbor embedding rdfs:subClassOf \n",
- "1076 t-Distributed Stochastic Neighbor embedding rdfs:subClassOf \n",
- "1077 t-Distributed Stochastic Neighbor embedding rdfs:subClassOf \n",
- "1078 t-Distributed Stochastic Neighbor embedding rdfs:subClassOf \n",
- "\n",
- " predicate_label object \\\n",
- "1074 None aio:Dimensionality_Reduction \n",
- "1075 None aio:Machine_Learning \n",
- "1076 None aio:Method \n",
- "1077 None aio:Unsupervised_Learning \n",
- "1078 None aio:t-Distributed_Stochastic_Neighbor_embedding \n",
- "\n",
- " object_label \n",
- "1074 Dimensionality Reduction \n",
- "1075 Machine Learning \n",
- "1076 Method \n",
- "1077 Unsupervised Learning \n",
- "1078 t-Distributed Stochastic Neighbor embedding \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Dimensionality_Reduction\n",
- " subject subject_label predicate \\\n",
- "275 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "276 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "277 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "278 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "\n",
- " predicate_label object object_label \n",
- "275 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "276 None aio:Machine_Learning Machine Learning \n",
- "277 None aio:Method Method \n",
- "278 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:t-Distributed_Stochastic_Neighbor_embedding\n",
+ "\u001b[31mERROR: Could not find a version that satisfies the requirement yaml (from versions: none)\u001b[0m\u001b[31m\n",
+ "\u001b[0m\u001b[31mERROR: No matching distribution found for yaml\u001b[0m\u001b[31m\n",
+ "\u001b[0m\n",
+ "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
+ "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install yaml"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "35f54f59-325a-44e2-aa3e-35a32e4b8322",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import yaml\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "#import oak"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "e9b271b8-b757-4c61-ae2f-9177d28e4929",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#methods_reformat_annotate.out"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "3545af76-d45c-4e68-90db-ccc3a26a0ac0",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
" subject \\\n",
+ "0 aio:AE \n",
+ "1 aio:AE \n",
+ "2 aio:AE \n",
+ "3 aio:ANN \n",
+ "4 aio:ANN \n",
+ "... ... \n",
"1074 aio:t-Distributed_Stochastic_Neighbor_embedding \n",
"1075 aio:t-Distributed_Stochastic_Neighbor_embedding \n",
"1076 aio:t-Distributed_Stochastic_Neighbor_embedding \n",
@@ -18051,6 +70,12 @@
"1078 aio:t-Distributed_Stochastic_Neighbor_embedding \n",
"\n",
" subject_label predicate \\\n",
+ "0 Auto Encoder Network rdfs:subClassOf \n",
+ "1 Auto Encoder Network rdfs:subClassOf \n",
+ "2 Auto Encoder Network rdfs:subClassOf \n",
+ "3 Artificial Neural Network rdfs:subClassOf \n",
+ "4 Artificial Neural Network rdfs:subClassOf \n",
+ "... ... ... \n",
"1074 t-Distributed Stochastic Neighbor embedding rdfs:subClassOf \n",
"1075 t-Distributed Stochastic Neighbor embedding rdfs:subClassOf \n",
"1076 t-Distributed Stochastic Neighbor embedding rdfs:subClassOf \n",
@@ -18058,6 +83,12 @@
"1078 t-Distributed Stochastic Neighbor embedding rdfs:subClassOf \n",
"\n",
" predicate_label object \\\n",
+ "0 None aio:AE \n",
+ "1 None aio:Network \n",
+ "2 None aio:UPN \n",
+ "3 None aio:ANN \n",
+ "4 None aio:Network \n",
+ "... ... ... \n",
"1074 None aio:Dimensionality_Reduction \n",
"1075 None aio:Machine_Learning \n",
"1076 None aio:Method \n",
@@ -18065,28 +96,44 @@
"1078 None aio:t-Distributed_Stochastic_Neighbor_embedding \n",
"\n",
" object_label \n",
+ "0 Auto Encoder Network \n",
+ "1 Network \n",
+ "2 Unsupervised Pretrained Network \n",
+ "3 Artificial Neural Network \n",
+ "4 Network \n",
+ "... ... \n",
"1074 Dimensionality Reduction \n",
"1075 Machine Learning \n",
"1076 Method \n",
"1077 Unsupervised Learning \n",
"1078 t-Distributed Stochastic Neighbor embedding \n",
- "parent aio:Dimensionality_Reduction\n",
- "aio:Dimensionality_Reduction\n",
- " subject subject_label predicate \\\n",
- "275 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "276 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "277 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
- "278 aio:Dimensionality_Reduction Dimensionality Reduction rdfs:subClassOf \n",
"\n",
- " predicate_label object object_label \n",
- "275 None aio:Dimensionality_Reduction Dimensionality Reduction \n",
- "276 None aio:Machine_Learning Machine Learning \n",
- "277 None aio:Method Method \n",
- "278 None aio:Unsupervised_Learning Unsupervised Learning \n",
- "parent aio:Dimensionality_Reduction\n"
+ "[1079 rows x 6 columns]\n"
]
}
],
+ "source": [
+ "file_path = 'children_for_branches_v4.out'\n",
+ "\n",
+ "# Load the file into a DataFrame\n",
+ "branches_df = pd.read_csv(file_path, sep='\\t', header=0)\n",
+ "\n",
+ "# Name the columns\n",
+ "#branches_df.columns = ['ID', 'Label', 'Predicate', 'Object', 'ParentID', 'ParentLabel']\n",
+ "\n",
+ "# Remove the appended AIO branch name from the ID column\n",
+ "# This regex pattern looks for an underscore followed by any characters at the end of the string and replaces it with nothing\n",
+ "#branches_df['ID'] = branches_df['ID'].str.replace(r'_[A-Za-z]+$', '', regex=True)\n",
+ "\n",
+ "print(branches_df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "5946cdab-34dd-44b3-99b3-79ab44adccd4",
+ "metadata": {},
+ "outputs": [],
"source": [
"def find_ontology_branch(subject, branches_df):\n",
" ontology_branches = ['Network', 'Layer', 'Function', 'Machine Learning', 'Preprocessing', 'Bias', 'Model']\n",
@@ -18097,13 +144,13 @@
" # Break the loop if we're revisiting a subject, indicating a cycle\n",
" return 'Unknown'\n",
" visited_subjects.add(subject)\n",
- " print(subject)\n",
+ " #print(subject)\n",
" \n",
" row = branches_df[branches_df['subject'] == subject]\n",
- " print(row)\n",
+ " #print(row)\n",
" if not row.empty:\n",
" parent_id = row.iloc[0]['object'] # Get parent ID\n",
- " print(\"parent \"+parent_id)\n",
+ " #print(\"parent \"+parent_id)\n",
" parent_row = branches_df[branches_df['subject'] == parent_id] # Find the parent row\n",
" if not parent_row.empty:\n",
" parent_label = parent_row.iloc[0]['object_label']\n",
@@ -18124,7 +171,7 @@
},
{
"cell_type": "code",
- "execution_count": 49,
+ "execution_count": 5,
"id": "ea604c7f-102e-45f1-8240-d227c97665cb",
"metadata": {},
"outputs": [
@@ -18145,7 +192,7 @@
"Name: ontology_branch, Length: 1079, dtype: object"
]
},
- "execution_count": 49,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@@ -18156,7 +203,7 @@
},
{
"cell_type": "code",
- "execution_count": 50,
+ "execution_count": 6,
"id": "5d1e16fa-e2e4-482a-a196-b3ba86e44d06",
"metadata": {},
"outputs": [
@@ -18168,7 +215,7 @@
" dtype='object')"
]
},
- "execution_count": 50,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -18179,7 +226,7 @@
},
{
"cell_type": "code",
- "execution_count": 51,
+ "execution_count": 7,
"id": "720b9655-e563-4fe8-a862-a592f9fe0917",
"metadata": {},
"outputs": [
@@ -18272,7 +319,7 @@
"143 aio:Supervised_Learning Supervised Learning Unknown "
]
},
- "execution_count": 51,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -18284,7 +331,7 @@
},
{
"cell_type": "code",
- "execution_count": 52,
+ "execution_count": 8,
"id": "46b106f5-e094-481a-a5e8-57032450a471",
"metadata": {},
"outputs": [],
@@ -18295,7 +342,7 @@
},
{
"cell_type": "code",
- "execution_count": 53,
+ "execution_count": 9,
"id": "26278bc3-a860-4d1c-9247-17532b77a972",
"metadata": {},
"outputs": [
@@ -18303,38 +350,51 @@
"name": "stdout",
"output_type": "stream",
"text": [
- " predicate_id object_id object_label \\\n",
- "0 rdfs:label https://w3id.org/aio/Normalization Normalization \n",
- "1 rdfs:label https://w3id.org/aio/Normalization Normalization \n",
- "2 rdfs:label https://w3id.org/aio/Normalization Normalization \n",
- "3 rdfs:label https://w3id.org/aio/Normalization Normalization \n",
- "4 rdfs:label https://w3id.org/aio/Normalization Normalization \n",
- "... ... ... ... \n",
- "12051 rdfs:label https://w3id.org/aio/Layer Layer \n",
- "12052 rdfs:label https://w3id.org/aio/Network Network \n",
- "12053 rdfs:label https://w3id.org/aio/Normalization Normalization \n",
- "12054 oio:hasExactSynonym https://w3id.org/aio/Model Model \n",
- "12055 rdfs:label https://w3id.org/aio/Model Model \n",
- "\n",
- " match_string matches_whole_text subject_start subject_end \n",
- "0 Normalization False 20 32 \n",
- "1 Normalization False 25 37 \n",
- "2 Normalization False 29 41 \n",
- "3 Normalization False 21 33 \n",
- "4 Normalization False 16 28 \n",
- "... ... ... ... ... \n",
- "12051 layer False 647 651 \n",
- "12052 network False 373 379 \n",
- "12053 Normalization False 569 581 \n",
- "12054 Model False 27 31 \n",
- "12055 Model False 27 31 \n",
- "\n",
- "[12056 rows x 7 columns]\n"
+ " predicate_id object_id \\\n",
+ "0 rdfs:label https://w3id.org/aio/Normalization \n",
+ "1 rdfs:label https://w3id.org/aio/Normalization \n",
+ "2 rdfs:label https://w3id.org/aio/Normalization \n",
+ "3 rdfs:label https://w3id.org/aio/Normalization \n",
+ "4 rdfs:label https://w3id.org/aio/Normalization \n",
+ "... ... ... \n",
+ "9683 oio:hasExactSynonym https://w3id.org/aio/AutoregressiveConditional... \n",
+ "9684 rdfs:label https://w3id.org/aio/Layer \n",
+ "9685 rdfs:label https://w3id.org/aio/Network \n",
+ "9686 rdfs:label https://w3id.org/aio/Normalization \n",
+ "9687 rdfs:label https://w3id.org/aio/Model \n",
+ "\n",
+ " object_label match_string matches_whole_text subject_start \\\n",
+ "0 Normalization Normalization False 20 \n",
+ "1 Normalization Normalization False 25 \n",
+ "2 Normalization Normalization False 29 \n",
+ "3 Normalization Normalization False 21 \n",
+ "4 Normalization Normalization False 16 \n",
+ "... ... ... ... ... \n",
+ "9683 ARCH arch False 480 \n",
+ "9684 Layer layer False 647 \n",
+ "9685 Network network False 373 \n",
+ "9686 Normalization Normalization False 569 \n",
+ "9687 Model Model False 27 \n",
+ "\n",
+ " subject_end \n",
+ "0 32 \n",
+ "1 37 \n",
+ "2 41 \n",
+ "3 33 \n",
+ "4 28 \n",
+ "... ... \n",
+ "9683 483 \n",
+ "9684 651 \n",
+ "9685 379 \n",
+ "9686 581 \n",
+ "9687 31 \n",
+ "\n",
+ "[9688 rows x 7 columns]\n"
]
}
],
"source": [
- "records = [record for record in data.split('---') if record.strip()]\n",
+ "records = [record for record in data.split('--') if record.strip()]\n",
"\n",
"# Parse each YAML record into a dictionary\n",
"parsed_records = [yaml.safe_load(record) for record in records]\n",
@@ -18347,7 +407,17 @@
},
{
"cell_type": "code",
- "execution_count": 54,
+ "execution_count": 27,
+ "id": "46fb4b37-44ed-4783-9bcc-d41df3311a7c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#parsed_records"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
"id": "42c8b0be-7b92-4167-b90d-e6271576c571",
"metadata": {},
"outputs": [],
@@ -18357,23 +427,23 @@
},
{
"cell_type": "code",
- "execution_count": 55,
+ "execution_count": 12,
"id": "3c0e962c-46c7-480b-9d31-2c518568f2fd",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "array(['Normalization', 'function', 'Reinforcement Learning',\n",
+ "array(['Normalization', 'Reinforcement Learning',\n",
" 'convolutional neural network', 'layer', 'network', 'Network',\n",
" 'Convolutional Neural Network', 'attention layer', 'model',\n",
" 'self-attention', 'Parameter Efficiency',\n",
- " 'Natural Language Processing', 'GELU', 'Function',\n",
- " 'classification', 'GAN', 'Model', 'reinforcement learning',\n",
- " 'model parallelism', 'DN', 'DNN', 'self-supervised',\n",
- " 'Self-supervised', 'feature learning', 'GCN', 'clustering',\n",
- " 'Graph Convolutional Network', 'computational efficiency', 'BERT',\n",
- " 'FFN', 'MLP', 'Layer', 'Contrastive Learning', 'normalization',\n",
+ " 'Natural Language Processing', 'GELU', 'classification', 'GAN',\n",
+ " 'Model', 'reinforcement learning', 'model parallelism', 'DN',\n",
+ " 'DNN', 'self-supervised', 'Self-supervised', 'feature learning',\n",
+ " 'GCN', 'clustering', 'Graph Convolutional Network',\n",
+ " 'computational efficiency', 'BERT', 'FFN', 'MLP', 'Layer',\n",
+ " 'Contrastive Learning', 'normalization',\n",
" 'Artificial Neural Network', 'bias', 'contrastive learning',\n",
" 'Classification', 'feature extraction', 'NN',\n",
" 'Self-Supervised Learning', 'Supervised Learning',\n",
@@ -18391,11 +461,12 @@
" 'Curriculum Learning', 'domain adaptation', 'Domain Adaptation',\n",
" 'Adaptation', 'unidirectional', 'text generation',\n",
" 'vision-language model', 'Masked Language Model', 'distillation',\n",
- " 'Conv1D', 'ReLU', 'ELM', 'Adversarial Training', 'DAG',\n",
- " 'Directed Acyclic Graph', 'meta-learning', 'ridge regression',\n",
- " 'Meta-Learning', 'reasoning', 'Exponential Linear Unit', 'ELU',\n",
- " 'self-supervised learning', 'supervised learning', 'skipgram',\n",
- " 'word2vec', 'adversarial training', 'MC', 'normalization layer',\n",
+ " 'Conv1D', 'ReLU', 'ELM', 'Adversarial Training', 'BatchNorm',\n",
+ " 'DAG', 'Directed Acyclic Graph', 'meta-learning',\n",
+ " 'ridge regression', 'Meta-Learning', 'reasoning',\n",
+ " 'Exponential Linear Unit', 'ELU', 'self-supervised learning',\n",
+ " 'supervised learning', 'skipgram', 'word2vec',\n",
+ " 'adversarial training', 'MC', 'normalization layer',\n",
" 'Sparse Autoencoder', 'hidden layer', 'LSTM', 'data augmentation',\n",
" 'GRU', 'Representation Learning', 'RN', 'rectified linear unit',\n",
" 'recurrent neural network', 'Large Language Model',\n",
@@ -18405,7 +476,7 @@
" 'Dimensionality Reduction', 'VAE', 'Hopfield network',\n",
" 'pooling layer', 'Hopfield Network', 'Reasoning', 'ARMA',\n",
" 'few-shot learning', 'supervised clustering', 'tokenization',\n",
- " 'lifelong learning', 'Lifelong Learning',\n",
+ " 'lifelong learning', 'Lifelong Learning', 'batchnorm',\n",
" 'Deep Convolutional Network', 'resnet', 'gated recurrent unit',\n",
" 'activation layer', 'Activation Layer', 'PGM', 'dense layer',\n",
" 'SentencePiece', 'natural language processing',\n",
@@ -18461,18 +532,19 @@
" 'multimodal transformer', 'Text Generation', 'Transfer Learning',\n",
" 'text Generation', 'Convolutional neural network',\n",
" 'model compression', 'resizing layer', 'large language model',\n",
- " 'Restricted Boltzmann Machine', 'SOM', 'Self-Organizing Map',\n",
- " 'Kohonen network', 'external memory', 'Unsupervised learning',\n",
- " 'Data Processing', 'Graph Convolutional network',\n",
- " 'Variational Auto Encoder', 'knowledge transfer',\n",
- " 'linear regression', 'multimodal Transformer', 'random forest',\n",
- " 'DIstillation', 'HN', 'data curation', 'Convolutional layer',\n",
- " 'Convolutional Layer', 'Zero-shot Learning', 'NTM',\n",
- " 'regularization layer', 'Representation LearnINg',\n",
- " 'Transformer network', 'Decision Tree'], dtype=object)"
+ " 'machine learning task', 'Restricted Boltzmann Machine', 'SOM',\n",
+ " 'Self-Organizing Map', 'Kohonen network', 'external memory',\n",
+ " 'Unsupervised learning', 'Data Processing',\n",
+ " 'Graph Convolutional network', 'Variational Auto Encoder',\n",
+ " 'knowledge transfer', 'linear regression',\n",
+ " 'multimodal Transformer', 'random forest', 'DIstillation', 'HN',\n",
+ " 'data curation', 'Convolutional layer', 'Convolutional Layer',\n",
+ " 'Zero-shot Learning', 'NTM', 'regularization layer',\n",
+ " 'Representation LearnINg', 'Transformer network', 'Decision Tree'],\n",
+ " dtype=object)"
]
},
- "execution_count": 55,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
@@ -18483,7 +555,7 @@
},
{
"cell_type": "code",
- "execution_count": 56,
+ "execution_count": 13,
"id": "12f84fca-ae97-47dc-8601-c84f2f736498",
"metadata": {},
"outputs": [
@@ -18579,7 +651,17 @@
" | ... | \n",
" \n",
" \n",
- " | 12051 | \n",
+ " 9681 | \n",
+ " rdfs:label | \n",
+ " https://w3id.org/aio/Network | \n",
+ " Network | \n",
+ " Network | \n",
+ " False | \n",
+ " 37 | \n",
+ " 43 | \n",
+ "
\n",
+ " \n",
+ " | 9684 | \n",
" rdfs:label | \n",
" https://w3id.org/aio/Layer | \n",
" Layer | \n",
@@ -18589,7 +671,7 @@
" 651 | \n",
"
\n",
" \n",
- " | 12052 | \n",
+ " 9685 | \n",
" rdfs:label | \n",
" https://w3id.org/aio/Network | \n",
" Network | \n",
@@ -18599,7 +681,7 @@
" 379 | \n",
"
\n",
" \n",
- " | 12053 | \n",
+ " 9686 | \n",
" rdfs:label | \n",
" https://w3id.org/aio/Normalization | \n",
" Normalization | \n",
@@ -18609,17 +691,7 @@
" 581 | \n",
"
\n",
" \n",
- " | 12054 | \n",
- " oio:hasExactSynonym | \n",
- " https://w3id.org/aio/Model | \n",
- " Model | \n",
- " Model | \n",
- " False | \n",
- " 27 | \n",
- " 31 | \n",
- "
\n",
- " \n",
- " | 12055 | \n",
+ " 9687 | \n",
" rdfs:label | \n",
" https://w3id.org/aio/Model | \n",
" Model | \n",
@@ -18630,40 +702,40 @@
"
\n",
" \n",
"\n",
- "10675 rows × 7 columns
\n",
+ "8309 rows × 7 columns
\n",
""
],
"text/plain": [
- " predicate_id object_id object_label \\\n",
- "0 rdfs:label https://w3id.org/aio/Normalization Normalization \n",
- "1 rdfs:label https://w3id.org/aio/Normalization Normalization \n",
- "2 rdfs:label https://w3id.org/aio/Normalization Normalization \n",
- "3 rdfs:label https://w3id.org/aio/Normalization Normalization \n",
- "4 rdfs:label https://w3id.org/aio/Normalization Normalization \n",
- "... ... ... ... \n",
- "12051 rdfs:label https://w3id.org/aio/Layer Layer \n",
- "12052 rdfs:label https://w3id.org/aio/Network Network \n",
- "12053 rdfs:label https://w3id.org/aio/Normalization Normalization \n",
- "12054 oio:hasExactSynonym https://w3id.org/aio/Model Model \n",
- "12055 rdfs:label https://w3id.org/aio/Model Model \n",
+ " predicate_id object_id object_label \\\n",
+ "0 rdfs:label https://w3id.org/aio/Normalization Normalization \n",
+ "1 rdfs:label https://w3id.org/aio/Normalization Normalization \n",
+ "2 rdfs:label https://w3id.org/aio/Normalization Normalization \n",
+ "3 rdfs:label https://w3id.org/aio/Normalization Normalization \n",
+ "4 rdfs:label https://w3id.org/aio/Normalization Normalization \n",
+ "... ... ... ... \n",
+ "9681 rdfs:label https://w3id.org/aio/Network Network \n",
+ "9684 rdfs:label https://w3id.org/aio/Layer Layer \n",
+ "9685 rdfs:label https://w3id.org/aio/Network Network \n",
+ "9686 rdfs:label https://w3id.org/aio/Normalization Normalization \n",
+ "9687 rdfs:label https://w3id.org/aio/Model Model \n",
"\n",
- " match_string matches_whole_text subject_start subject_end \n",
- "0 Normalization False 20 32 \n",
- "1 Normalization False 25 37 \n",
- "2 Normalization False 29 41 \n",
- "3 Normalization False 21 33 \n",
- "4 Normalization False 16 28 \n",
- "... ... ... ... ... \n",
- "12051 layer False 647 651 \n",
- "12052 network False 373 379 \n",
- "12053 Normalization False 569 581 \n",
- "12054 Model False 27 31 \n",
- "12055 Model False 27 31 \n",
+ " match_string matches_whole_text subject_start subject_end \n",
+ "0 Normalization False 20 32 \n",
+ "1 Normalization False 25 37 \n",
+ "2 Normalization False 29 41 \n",
+ "3 Normalization False 21 33 \n",
+ "4 Normalization False 16 28 \n",
+ "... ... ... ... ... \n",
+ "9681 Network False 37 43 \n",
+ "9684 layer False 647 651 \n",
+ "9685 network False 373 379 \n",
+ "9686 Normalization False 569 581 \n",
+ "9687 Model False 27 31 \n",
"\n",
- "[10675 rows x 7 columns]"
+ "[8309 rows x 7 columns]"
]
},
- "execution_count": 56,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -18675,7 +747,7 @@
},
{
"cell_type": "code",
- "execution_count": 57,
+ "execution_count": 14,
"id": "f7fcf8ae-5935-4d52-92a2-dc1e179d6ed7",
"metadata": {
"scrolled": true
@@ -18683,7 +755,7 @@
"outputs": [
{
"data": {
- "image/png": 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",
"text/plain": [
""
]
@@ -18761,7 +833,7 @@
},
{
"cell_type": "code",
- "execution_count": 58,
+ "execution_count": 15,
"id": "91a30032-fcf4-4dd9-ad8c-4bd0959a2492",
"metadata": {},
"outputs": [
@@ -18771,7 +843,7 @@
"Index(['Exact Synonym', 'Name', 'Total'], dtype='object', name='predicate_id')"
]
},
- "execution_count": 58,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
@@ -18790,13 +862,13 @@
},
{
"cell_type": "code",
- "execution_count": 59,
+ "execution_count": 16,
"id": "5c528d3d-7978-40ab-a353-877809cdbaac",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
"text/plain": [
""
]
@@ -18864,7 +936,7 @@
},
{
"cell_type": "code",
- "execution_count": 60,
+ "execution_count": 17,
"id": "2b1536cf-c90e-4690-ae9b-17410e00e178",
"metadata": {},
"outputs": [
@@ -18897,7 +969,7 @@
" \n",
" \n",
" | https://w3id.org/aio/Model | \n",
- " 1609 | \n",
+ " 0 | \n",
" 1609 | \n",
" 0 | \n",
"
\n",
@@ -18920,18 +992,6 @@
" 0 | \n",
" \n",
" \n",
- " | https://w3id.org/aio/Function | \n",
- " 0 | \n",
- " 526 | \n",
- " 0 | \n",
- "
\n",
- " \n",
- " | https://w3id.org/aio/LanguageModel | \n",
- " 179 | \n",
- " 179 | \n",
- " 0 | \n",
- "
\n",
- " \n",
" | https://w3id.org/aio/GenerativeAdversarialNetwork | \n",
" 127 | \n",
" 113 | \n",
@@ -18950,18 +1010,24 @@
" 0 | \n",
"
\n",
" \n",
- " | https://w3id.org/aio/ReinforcementLearning | \n",
+ " https://w3id.org/aio/Classification | \n",
" 0 | \n",
" 192 | \n",
" 0 | \n",
"
\n",
" \n",
- " | https://w3id.org/aio/Classification | \n",
+ " https://w3id.org/aio/ReinforcementLearning | \n",
" 0 | \n",
" 192 | \n",
" 0 | \n",
"
\n",
" \n",
+ " | https://w3id.org/aio/LanguageModel | \n",
+ " 0 | \n",
+ " 179 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
" | https://w3id.org/aio/RecursiveLLM | \n",
" 0 | \n",
" 0 | \n",
@@ -19058,12 +1124,6 @@
" 0 | \n",
"
\n",
" \n",
- " | https://w3id.org/aio/MaskedLanguageModel | \n",
- " 26 | \n",
- " 26 | \n",
- " 10 | \n",
- "
\n",
- " \n",
" | https://w3id.org/aio/BidirectionalTransformerLanguageModel | \n",
" 60 | \n",
" 0 | \n",
@@ -19118,10 +1178,16 @@
" 37 | \n",
"
\n",
" \n",
- " | https://w3id.org/aio/MachineLearning | \n",
+ " https://w3id.org/aio/MaskedLanguageModel | \n",
" 0 | \n",
- " 30 | \n",
+ " 26 | \n",
+ " 10 | \n",
+ "
\n",
+ " \n",
+ " | https://w3id.org/aio/MachineLearningTask | \n",
" 0 | \n",
+ " 2 | \n",
+ " 30 | \n",
"
\n",
" \n",
" | https://w3id.org/aio/VariationalAutoEncoder | \n",
@@ -19148,12 +1214,6 @@
" 0 | \n",
"
\n",
" \n",
- " | https://w3id.org/aio/word2vec | \n",
- " 13 | \n",
- " 13 | \n",
- " 0 | \n",
- "
\n",
- " \n",
" | https://w3id.org/aio/AttentionLayer | \n",
" 0 | \n",
" 22 | \n",
@@ -19190,16 +1250,28 @@
" 0 | \n",
"
\n",
" \n",
- " | https://w3id.org/aio/AutoregressiveLanguageModel | \n",
- " 6 | \n",
- " 6 | \n",
- " 6 | \n",
+ " https://w3id.org/aio/SubwordSegmentation | \n",
+ " 3 | \n",
+ " 8 | \n",
+ " 7 | \n",
+ "
\n",
+ " \n",
+ " | https://w3id.org/aio/ELUFunction | \n",
+ " 17 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | https://w3id.org/aio/ReasoningLLM | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 17 | \n",
"
\n",
" \n",
" | Other | \n",
- " 180 | \n",
- " 286 | \n",
- " 132 | \n",
+ " 151 | \n",
+ " 297 | \n",
+ " 114 | \n",
"
\n",
" \n",
"\n",
@@ -19207,17 +1279,16 @@
],
"text/plain": [
"predicate_id Exact Synonym Name \\\n",
- "https://w3id.org/aio/Model 1609 1609 \n",
+ "https://w3id.org/aio/Model 0 1609 \n",
"https://w3id.org/aio/Network 0 1608 \n",
"https://w3id.org/aio/Layer 0 529 \n",
"https://w3id.org/aio/DeepConvolutionalNetwork 522 6 \n",
- "https://w3id.org/aio/Function 0 526 \n",
- "https://w3id.org/aio/LanguageModel 179 179 \n",
"https://w3id.org/aio/GenerativeAdversarialNetwork 127 113 \n",
"https://w3id.org/aio/Normalization 0 233 \n",
"https://w3id.org/aio/NaturalLanguageProcessing 14 202 \n",
- "https://w3id.org/aio/ReinforcementLearning 0 192 \n",
"https://w3id.org/aio/Classification 0 192 \n",
+ "https://w3id.org/aio/ReinforcementLearning 0 192 \n",
+ "https://w3id.org/aio/LanguageModel 0 179 \n",
"https://w3id.org/aio/RecursiveLLM 0 0 \n",
"https://w3id.org/aio/UnsupervisedLLM 0 0 \n",
"https://w3id.org/aio/SupervisedLearning 0 112 \n",
@@ -19234,7 +1305,6 @@
"https://w3id.org/aio/KnowledgeTransfer 0 1 \n",
"https://w3id.org/aio/SelfsupervisedLearning 0 64 \n",
"https://w3id.org/aio/LongShortTermMemory 63 0 \n",
- "https://w3id.org/aio/MaskedLanguageModel 26 26 \n",
"https://w3id.org/aio/BidirectionalTransformerLa... 60 0 \n",
"https://w3id.org/aio/GraphConvolutionalNetwork 26 32 \n",
"https://w3id.org/aio/Distillation 4 53 \n",
@@ -19244,33 +1314,34 @@
"https://w3id.org/aio/CausalLLM 0 0 \n",
"https://w3id.org/aio/Perceptron 24 14 \n",
"https://w3id.org/aio/GenerativeAdversarialNetwo... 0 0 \n",
- "https://w3id.org/aio/MachineLearning 0 30 \n",
+ "https://w3id.org/aio/MaskedLanguageModel 0 26 \n",
+ "https://w3id.org/aio/MachineLearningTask 0 2 \n",
"https://w3id.org/aio/VariationalAutoEncoder 28 1 \n",
"https://w3id.org/aio/MarkovChain 22 6 \n",
"https://w3id.org/aio/DeconvolutionalNetwork 27 0 \n",
"https://w3id.org/aio/GatedRecurrentUnit 18 8 \n",
- "https://w3id.org/aio/word2vec 13 13 \n",
"https://w3id.org/aio/AttentionLayer 0 22 \n",
"https://w3id.org/aio/ExplainableLLM 0 0 \n",
"https://w3id.org/aio/FeatureExtraction 0 19 \n",
"https://w3id.org/aio/HiddenLayer 0 20 \n",
"https://w3id.org/aio/DimensionalityReduction 3 16 \n",
"https://w3id.org/aio/ArtificialNeuralNetwork 14 5 \n",
- "https://w3id.org/aio/AutoregressiveLanguageModel 6 6 \n",
- "Other 180 286 \n",
+ "https://w3id.org/aio/SubwordSegmentation 3 8 \n",
+ "https://w3id.org/aio/ELUFunction 17 0 \n",
+ "https://w3id.org/aio/ReasoningLLM 0 0 \n",
+ "Other 151 297 \n",
"\n",
"predicate_id Related Synonym \n",
"https://w3id.org/aio/Model 0 \n",
"https://w3id.org/aio/Network 0 \n",
"https://w3id.org/aio/Layer 0 \n",
"https://w3id.org/aio/DeepConvolutionalNetwork 0 \n",
- "https://w3id.org/aio/Function 0 \n",
- "https://w3id.org/aio/LanguageModel 0 \n",
"https://w3id.org/aio/GenerativeAdversarialNetwork 0 \n",
"https://w3id.org/aio/Normalization 0 \n",
"https://w3id.org/aio/NaturalLanguageProcessing 0 \n",
- "https://w3id.org/aio/ReinforcementLearning 0 \n",
"https://w3id.org/aio/Classification 0 \n",
+ "https://w3id.org/aio/ReinforcementLearning 0 \n",
+ "https://w3id.org/aio/LanguageModel 0 \n",
"https://w3id.org/aio/RecursiveLLM 144 \n",
"https://w3id.org/aio/UnsupervisedLLM 120 \n",
"https://w3id.org/aio/SupervisedLearning 0 \n",
@@ -19287,7 +1358,6 @@
"https://w3id.org/aio/KnowledgeTransfer 65 \n",
"https://w3id.org/aio/SelfsupervisedLearning 0 \n",
"https://w3id.org/aio/LongShortTermMemory 0 \n",
- "https://w3id.org/aio/MaskedLanguageModel 10 \n",
"https://w3id.org/aio/BidirectionalTransformerLa... 0 \n",
"https://w3id.org/aio/GraphConvolutionalNetwork 0 \n",
"https://w3id.org/aio/Distillation 0 \n",
@@ -19297,23 +1367,25 @@
"https://w3id.org/aio/CausalLLM 39 \n",
"https://w3id.org/aio/Perceptron 0 \n",
"https://w3id.org/aio/GenerativeAdversarialNetwo... 37 \n",
- "https://w3id.org/aio/MachineLearning 0 \n",
+ "https://w3id.org/aio/MaskedLanguageModel 10 \n",
+ "https://w3id.org/aio/MachineLearningTask 30 \n",
"https://w3id.org/aio/VariationalAutoEncoder 0 \n",
"https://w3id.org/aio/MarkovChain 0 \n",
"https://w3id.org/aio/DeconvolutionalNetwork 0 \n",
"https://w3id.org/aio/GatedRecurrentUnit 0 \n",
- "https://w3id.org/aio/word2vec 0 \n",
"https://w3id.org/aio/AttentionLayer 0 \n",
"https://w3id.org/aio/ExplainableLLM 21 \n",
"https://w3id.org/aio/FeatureExtraction 2 \n",
"https://w3id.org/aio/HiddenLayer 0 \n",
"https://w3id.org/aio/DimensionalityReduction 0 \n",
"https://w3id.org/aio/ArtificialNeuralNetwork 0 \n",
- "https://w3id.org/aio/AutoregressiveLanguageModel 6 \n",
- "Other 132 "
+ "https://w3id.org/aio/SubwordSegmentation 7 \n",
+ "https://w3id.org/aio/ELUFunction 0 \n",
+ "https://w3id.org/aio/ReasoningLLM 17 \n",
+ "Other 114 "
]
},
- "execution_count": 60,
+ "execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
@@ -19324,29 +1396,45 @@
},
{
"cell_type": "code",
- "execution_count": 61,
- "id": "495dec8e-6da8-4323-9840-280f2ebc01c0",
+ "execution_count": 29,
+ "id": "a5654aa2-f2f4-49a8-9a86-7075861b6632",
"metadata": {},
"outputs": [
{
- "ename": "NameError",
- "evalue": "name 'id_to_branch' is not defined",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[61], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mid_to_branch\u001b[49m\n",
- "\u001b[0;31mNameError\u001b[0m: name 'id_to_branch' is not defined"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Sum of 'Exact Synonym': 1445\n",
+ "Sum of 'Name': 6137\n",
+ "Sum of 'Related Synonym': 727\n"
]
}
],
"source": [
- "id_to_branch"
+ "# Summing the values in the columns\n",
+ "sum_exact_synonym = final_df['Exact Synonym'].sum()\n",
+ "sum_name = final_df['Name'].sum()\n",
+ "sum_related_synonym = final_df['Related Synonym'].sum()\n",
+ "\n",
+ "# Printing the sums\n",
+ "print(\"Sum of 'Exact Synonym':\", sum_exact_synonym)\n",
+ "print(\"Sum of 'Name':\", sum_name)\n",
+ "print(\"Sum of 'Related Synonym':\", sum_related_synonym)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "495dec8e-6da8-4323-9840-280f2ebc01c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#id_to_branch"
]
},
{
"cell_type": "code",
- "execution_count": 62,
+ "execution_count": 20,
"id": "b13d4ed9-de78-493a-852d-4a7fd6791457",
"metadata": {},
"outputs": [
@@ -19355,19 +1443,19 @@
"output_type": "stream",
"text": [
" object_label count\n",
- "0 Model 3218\n",
+ "0 Model 1609\n",
"1 Network 1608\n",
"2 Layer 529\n",
- "3 Function 526\n",
- "4 Language Model 358\n",
+ "3 Convolutional Neural Network 308\n",
+ "4 Normalization 233\n",
".. ... ...\n",
- "209 Pretext tasks 1\n",
"210 Principal Component Analysis 1\n",
"211 TAR 1\n",
"212 Unsupervised Clustering 1\n",
- "213 Regularization Layer 1\n",
+ "213 SAE 1\n",
+ "214 Regularization Layer 1\n",
"\n",
- "[214 rows x 2 columns]\n"
+ "[215 rows x 2 columns]\n"
]
}
],
@@ -19381,7 +1469,7 @@
},
{
"cell_type": "code",
- "execution_count": 63,
+ "execution_count": 21,
"id": "9b390f70-85b5-41f8-9a18-7d0a6108278b",
"metadata": {},
"outputs": [
@@ -19389,20 +1477,20 @@
"name": "stdout",
"output_type": "stream",
"text": [
- " predicate_id object_label count\n",
- "0 oio:hasExactSynonym AE 4\n",
- "1 oio:hasExactSynonym ARCH 3\n",
- "2 oio:hasExactSynonym ARMA 4\n",
- "3 oio:hasExactSynonym Autoregressive Language Model 6\n",
- "4 oio:hasExactSynonym BERT 60\n",
- ".. ... ... ...\n",
- "216 rdfs:label Unsupervised Learning 11\n",
- "217 rdfs:label Variational Auto Encoder 1\n",
- "218 rdfs:label Zero-shot Learning 1\n",
- "219 rdfs:label node2vec 5\n",
- "220 rdfs:label word2vec 13\n",
- "\n",
- "[221 rows x 3 columns]\n"
+ " predicate_id object_label count\n",
+ "0 oio:hasExactSynonym AE 4\n",
+ "1 oio:hasExactSynonym ARCH 3\n",
+ "2 oio:hasExactSynonym ARMA 4\n",
+ "3 oio:hasExactSynonym BERT 60\n",
+ "4 oio:hasExactSynonym BatchNorm 8\n",
+ ".. ... ... ...\n",
+ "210 rdfs:label Unsupervised Learning 11\n",
+ "211 rdfs:label Variational Auto Encoder 1\n",
+ "212 rdfs:label Zero-shot Learning 1\n",
+ "213 rdfs:label node2vec 5\n",
+ "214 rdfs:label word2vec 13\n",
+ "\n",
+ "[215 rows x 3 columns]\n"
]
}
],
@@ -19414,7 +1502,7 @@
},
{
"cell_type": "code",
- "execution_count": 64,
+ "execution_count": 22,
"id": "c29385e5-ae61-4bf7-9d91-0cc54fc2d18c",
"metadata": {},
"outputs": [
@@ -19425,7 +1513,7 @@
" dtype=object)"
]
},
- "execution_count": 64,
+ "execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
@@ -19436,7 +1524,7 @@
},
{
"cell_type": "code",
- "execution_count": 65,
+ "execution_count": 23,
"id": "725a02ef-fe96-40d8-b82f-67e6cff7c4c6",
"metadata": {},
"outputs": [
@@ -19444,20 +1532,20 @@
"name": "stdout",
"output_type": "stream",
"text": [
- " predicate_id object_label count\n",
- "0 ExactSynonym AE 4\n",
- "1 ExactSynonym ARCH 3\n",
- "2 ExactSynonym ARMA 4\n",
- "3 ExactSynonym Autoregressive Language Model 6\n",
- "4 ExactSynonym BERT 60\n",
- ".. ... ... ...\n",
- "216 Label Unsupervised Learning 11\n",
- "217 Label Variational Auto Encoder 1\n",
- "218 Label Zero-shot Learning 1\n",
- "219 Label node2vec 5\n",
- "220 Label word2vec 13\n",
- "\n",
- "[221 rows x 3 columns]\n"
+ " predicate_id object_label count\n",
+ "0 ExactSynonym AE 4\n",
+ "1 ExactSynonym ARCH 3\n",
+ "2 ExactSynonym ARMA 4\n",
+ "3 ExactSynonym BERT 60\n",
+ "4 ExactSynonym BatchNorm 8\n",
+ ".. ... ... ...\n",
+ "210 Label Unsupervised Learning 11\n",
+ "211 Label Variational Auto Encoder 1\n",
+ "212 Label Zero-shot Learning 1\n",
+ "213 Label node2vec 5\n",
+ "214 Label word2vec 13\n",
+ "\n",
+ "[215 rows x 3 columns]\n"
]
}
],
@@ -19477,7 +1565,7 @@
},
{
"cell_type": "code",
- "execution_count": 66,
+ "execution_count": 24,
"id": "0caf1a3d-45ec-4fc1-b257-05fbed42c37a",
"metadata": {
"scrolled": true
@@ -19485,7 +1573,7 @@
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
@@ -19525,13 +1613,13 @@
},
{
"cell_type": "code",
- "execution_count": 67,
+ "execution_count": 25,
"id": "a5e59bce-9ef4-4f72-a04a-acbe06f5d1e5",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
""
]
@@ -19583,13 +1671,13 @@
},
{
"cell_type": "code",
- "execution_count": 68,
+ "execution_count": 26,
"id": "e541b9f9-69c0-4ba2-89a2-dc4c7c30b277",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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rltGnTx+7g6lLj/fVV1/ZHUxd2n/ixInG448/bgQFBRkWi8Xo37//Nfv/8ssv1nF+/fVXw9fX13B3dzcWLFhgGIb9r+ul3nvvPcNisRjjxo0rUDD1xx9/GHfd9X/svXVcVd3WNvzbAorS3d3d3d2pmKCCiBhggJQIBnZ33DZ2tyg2ioWUHYgIiq0oKH19f/Ctedbaa6N4n/N973me9x7/KDvWnmuuGWNe4xrXcISsrCzx7/7OIenv+isAc94dOXIEqqqqGDp0aI+vQX9/06ZN2LZtGzgcDtTU1LBv376/5SfMnj0bvr6+sLa2/tugd1ZWFlRVVaGlpQVfX98/2uPp97RkyRJERkbC1NQU06ZN67HPwh08jo6OBofDwaZNm3rcDvrYnz17NgYMGAAzMzMICAjAwcEB9+/f7/G16Nbe3o5JkybB19cXnz9//lvXAIBjx4792wGw7du3Q1FREVeuXPm35uKdO3fw/v37v/XdtLQ0iIiIQFNTExwOB0uXLu3RtejPJzs7G/369YOfnx84HA4CAgJw9+7dP27LvHnzkJCQgPj4+D9aq6nPUvO5o6MDY8eO/VvAFABMnToVI0eOhK2tLYSEhGBmZvbHwBT12aKiIixduhQpKSk4derU3wJ4f/78iejoaFhaWjKCsX9imZmZUFBQgJ6eHnx9fQlo/n+aVPHfbv+AUv9LjZpAxcXF0NTU/CNgaufOnXB0dGREcOvr66Gmpoa6urq/3abi4mKMHz8eBQUFf/xdaiKfOXMGYWFhiI2Nxdq1a8n7PV0w6AvX9OnT8ddff/2tw9bkyZNhbGyMCRMmIDAwEHJychgwYACePHnyR9e5ffs2+vTpg4yMDCQmJsLAwAAODg7k/Z7cV0pKCvT09JCZmYmQkBDIysrC2dn5twcv+rWfP3+OqqoqvHnzhvwdEBDQI2CqvLwcY8aMgYGBAfbs2QMAmDRpEsaOHQvgXyBiZ2dnj+5nypQpcHZ2RlRUFPT19aGoqIjU1NTfRkLp1965cyfU1dWxe/du7N27F4qKivD29sbt27d/+/t0S0tLg42NDWJiYqCrq4s+ffogJyenx9+nxtuhQ4cgKSmJKVOm/C1mBdA15gIDA+Hp6QllZWXIyspi+/btf7TpTpo0Cfb29hg4cCCsrKwgJiaGjIyM3zKmjh8/juDgYNjb26O8vBwAcOXKFdjb26O9vZ3c559suOnp6ZCTk8OiRYswfvx4yMnJwdHRkQF88bLt27fDwcEBgYGBBAy4cuUKjIyMWIB1T+3gwYMQERGBvr4+1NXVYWVlRQ7Wv3JeHzx4gJEjR8LR0ZEcANLS0jBq1CjGd/+UBfDz509Mnjz5bwFT9Hm/aNGivwVMlZWVQUFBASYmJuBwONi5c+cftYEy6n4eP34MIyMjBAYG/i1gatCgQbC0tERkZCR8fHwYAPjvrLy8HMLCwhASEoKNjQ05GP0pMHXv3j3069cPs2fPJn05a9YsCAgIoLCwsMfXoY+FGzduIDAwsMfA1IsXL9C7d29MmzaNca05c+bA2tqaONA9MarvT548CQEBAairqzNYfz09tFHXaW9vx+rVq+Hg4PBHwBSv948ePfpHwFR38+v06dPw8/ODr68vioqKfnsdyvLy8iAhIYGzZ8+isLAQixcvhrCwMIYPH97tdw4ePAgZGRly369evYKAgACEhYURGBj4t6Ll9LUVADZu3AgOh4Pc3Ny/dcCnmNASEhJ/C5j6d/wV+n0sXrwY0dHRUFNTA4fDQWRkZI+uQVl2djYkJCSwa9curFmzBiEhIRAREekRMEW//qJFiyAnJ4dp06Zh2LBh6NWrF+bOnfvbPZV+jYMHD0JRURH79+/Hpk2bYGxsDGNj4z/2lTMzMyEvL4+FCxfi8OHD4HA4GDx48B+BH+np6bC3t0dUVBRUVFTQp08fLF++/I/asXz5cggJCeHChQuorq7Gxo0b4e7uDjs7Ozx48OCPrkXZsWPHoKCggFu3bgHo2TOmf6ampgYcDgfJycl/BExxZzVs2bIFHA4H9vb2uHbt2t9i5xUUFEBGRgZLly7t0Rykj/vbt2/D1tYW169fx/fv37F48WKIiIhg1qxZPQ5sVFVVITAwkJznampqoKGhAR8fnz8mH1CsbAsLix4D3dx9RvnlHR0dmDBhAqysrP4ImFqzZg3ExcVx584dVFVV4dGjRzA0NISxsfEfA1OHDh2CiIgIYmNj4ePjAxsbG0RFRaG1tfWX33vy5AmLFfXu3TtISkqScwzQc3/l9OnT0NTUxLFjx7Bx40ZYWlpCT08PHz58+KPr/N9o/4BS/0uN7lAWFRX9ETB169YtuLi4ICQkhABT79+/h56eHllouJ2l39nx48dhYmICdXV1EnH5U+T58uXL4OfnR3x8PDw9PWFiYoKEhATyfk+vd+LECfTp0wcuLi5QVFSEp6fnHx1yLl26BDk5OQbddd26dXB0dERUVBSqqqp6dJ2KigoUFBRg4cKFAIDW1lYUFRVBS0sL9vb2Pbqv4uJiyMnJMVIuDh06BENDQ7i6unZ78KI/u9zcXJiYmEBfXx/y8vLYsGEDgH8BU97e3ti3bx/rGvR23blzBwkJCdDX18eJEyewZMkSzJo1C9+/f2ek/fzu0HTkyBFISUnhzp075PpTpkyBtbU10tLSegQ6FBQUYP78+Ywo4atXr6CtrQ0vL68eA1NHjx6FqKgorl27hs7OTrx69QqzZs2CmJgYZsyY0aNrAF2MOhEREWzbtq1Hz4OX5efnQ1xcHBUVFfjy5Qs6OjoQGhoKDQ0NbN++vUeR2WPHjkFSUhKlpaWkHWlpaTAyMkJmZuZvnd+zZ88iJCQENjY2qKysxPnz52FhYfG3WCcVFRWQkZFhANQ1NTVQVVWFk5MTAb54WWdnJ/bt2wdXV1f4+vri/fv3BHzv7jDBq3+p1378+IH4+Hhs374d3759w5UrV2BnZwcNDY1ugamOjg7y2oMHDxATEwN7e3vs3r0bCxcuxIIFC/D582c0Njaivb0dzc3NePnyJc+2vXjxAsuXL8ezZ8/Q0NDAeC8pKQkCAgK/jeA/ffoUKSkpuHXrFut35s+fDw6HgyVLlvxRisvixYvB4XCgp6fHuO+eGvVZ6t9Hjx4RYOrKlSs9ugZ1wDx69Cji4uJw8uRJBAcHw9PTs0eshpKSEoiKiiIjIwMlJSWwt7eHqakpOUz0FHj5+PEjpKSk4OLiwliDvn//DiUlJaxfv/6313jx4gXu3LnDAqWvXbtG1tlfpWx2dnZi48aNUFRUZDBU5s6dC3FxcZ7Bnl+tK9R7t27dwoEDB5CUlARbW1ssXbqUfOZXz/vjx4+sQ1lraytWr14NOzu7HgFT9+/fh5eXFzZu3IgzZ84w3jt58iQMDAwQExPzyzWb3sb9+/djzZo1yM7Oxtu3bwEAFy5cIIypngBTP3/+REBAAGbPnk1ea25uxsGDByEoKIgxY8aQ17nvq7a2FgDIb1dXV6OiogJKSkrw9fVlAVPd9S/1faB7YGrOnDm/nY8/f/5krc9PnjyBv7//HwNT/wl/Begar2JiYjh58iQuX76MWbNmQUVFBSEhIT26xtu3b6Grq8tiAUVHR0NMTAz79+/v0X749OlTzJgxA+fOnSOvrVu3jvRtT66xd+9e7NixA3/99Rd57eXLl7C2toaRkRFev37922sAXcC5np4eAexv374NAQEBwrDriR04cADCwsK4du0a2tra8OTJE8LK6WnqXGtrKwYOHIgJEyYwXj927BjMzc3h6Oj4y1Q++jjlnhtUQOF3AAH3d2fOnImUlBTIy8uDw+Fg1KhRf5xKnpmZCWlpaaxduxbZ2dkwNDSErq5uj4ApelvWrVuHhQsXQlBQELKyslixYkWPweHFixdj4sSJrL5dtmwZREREMHv27N8CUwsWLICdnR0CAgLw6dMn8vqLFy+goaEBX1/fbkH87u5zyZIlJNX/d8YtuxATEwNfX18sWLAAQFdfTZgwAdbW1pg/f36PgKmJEyciIiKCfB/oSg/W09ODjY0Nbty40aP16enTp9DS0iJ78YsXLyAkJIT09PRffq+9vR1mZmbgcDhwd3fH+fPnyTlly5YtkJaW5nn+oRt3+86fP49ly5aRe3r06BFsbW3/AaZ6YP+AUv/LjBro379/R0dHBzmkXb16tUfAFOWk37lzBx4eHvD19cWZM2fw5MkTqKmp4ePHj3+rXaWlpRg8eDAEBQWJUwP0/JDz9OlTbNmyhSycnz59wvr166Gjo0NYCT25Xm1tLVJSUrBx40YAXQeC6OhoWFtb91jz4dy5c5CWlsajR48Yry9ZsgSCgoIYOHDgb3Pw3759Cw0NDfDx8WHWrFnk9fb2duLoOTk5/bYtJ0+ehLi4OCMq9/PnT2zbtg38/Py/TeXLy8uDjIwMzp07h8bGRkRGRkJcXJxExJ49e4agoCCYmZnh/PnzPK9x+PBhbN68maS56evrQ1hYGAoKCtDW1oaKigq0tbWhpqYGCwsLxmbKbevXr4e2tjbjcN7Q0IC4uDiIi4sjKyur20hoZ2cn3r17Bw6HQ/Q36Pbq1Svo6OjAx8enR+kcy5cvh6mpKeO1t2/fYsqUKeBwOFi8ePFvrwF0sSmoFIXv37/j8uXLGDFiBCZMmIBDhw716BoLFy6Era0tfvz4wThI+/r6QkFBAdu2bfuto7Zr1y5oaGiwWFFJSUno27cvMjMzWe/RN87Hjx8TxpSjoyNycnLg6uqK9PR0LFmyBCtWrMCiRYuQmZn5Wyp5aWkp5OXlCUBNOaqPHj2CiIgIwsLCeDKmqLH89u1b7Nq1C66urggJCcGBAwcQHh6OXbt24fr16yguLsalS5dw/PjxX+q9Xb9+Hebm5ggODmboO1RUVMDW1pYBTPFaW3bu3IlNmzahsrISMTExcHZ2hrCwMAQEBGBkZAQVFRWoqqoS9hXlkFD28eNHaGlpgcPhQEFBAX5+fpg4cSIuXryInz9/oqWlBXl5eeDn5+8WsGhoaIC5uTk4HA40NTVhYWGBIUOGYOPGjcRh3rFjBzgcDlatWvXbcUI98+PHjyMvLw+6urpwcHAg844XQEe3Fy9eEICTeo/6zqNHj2BsbIygoKBuU1Nqa2tx6tQpxmuvXr2ClpYWtm/fjlevXiE4OBheXl7dBhMoXRYxMTEkJSWR1yh2n6mpKYkM9xSYSkxMhJ6eHlauXEkOD+Xl5RAQEMDp06d/+d26ujqyLunp6WHEiBFYt24dYaZS6ZJBQUG/BNsaGhqwZs0amJmZYeLEiZg/fz6kpaVZgA7Q/V5IPd/6+np8+PCBrMevX79GYmIibG1tGeyKAwcOsObQ06dPwcfHBx0dHfj6+mLv3r2EBQEA27Ztg6OjIwYPHkz6mbs9bW1t8Pf3B4fDgZGREcTFxeHm5obBgwfjxo0baGpqwtWrV2FqaoqEhITfBtSmTp0KZWVlhIeHw8rKCgoKCoTle+rUKQQEBCAgIKDbfYyylpYWGBkZIT4+nvF6c3MzYmNjweFwGMAUtz148AC9e/dm+BPXrl2DkpIS/P39Sf+vXr2aHObo1tDQAH19fUZaGzcwRWnG/UqP7PHjx/D09MTQoUNx6dIlBiP89evX8Pf3h5iYGGF3/w4I+k/4Kz9+/EBISAjy8vLIa01NTdizZw9kZGQwaNAg8np3gYTXr19DUVGRrId0kNjS0hKampo4dOjQLw99ly5dAofDgaSkJGutWb9+PXr16oV58+b9kjH15s0bSElJgcPhMA7lQBcwZWNjA1NTU57MaO6+vnnzJqysrAB0Ma+EhYUJa7GhoYEBnHVn8+bNg52dHeO12tpaJCQkoFevXj1O5Rs5ciR8fX1Z4FF6ejo4HA6cnZ15rt30e1q4cCFWrlzJCC4dP34c5ubmJMW1J77/3LlzISUlhQsXLuDChQvYuHEjhISEMHLkyB4DU0+fPoWSkhKOHDlCXmtsbISVlRX09PRQVFTUoz1gxowZEBcXx4EDB3DgwAEMGTIEMjIyWLFiRY9YRuPHjweHw4GjoyPr88uXL4eEhASmTp36S5Dr9u3bEBYWhpiYGNHgpMZcdXU1dHR0YGlpyTqbcGuFXbx4EWVlZeQZ5+bmgo+Pj5yLfmfp6elQVFREZmYmAXLHjRtHfmvChAmws7PDtGnTugV3qTYNHz6cAWpTgbOdO3eCw+HAycmJ+Ga/mtNFRUUwMjIC0LWfqqqqYvTo0eT9GzdudHsWohi1Tk5OGDRoEOLi4gi4GxwcjKFDh3YLMNPbtHz5ckyePBkWFhaYOHEi43OPHz+GnZ0dDA0N/1a6//8t9g8o9b/I6CluoaGhcHNzg5eXF3FG6Kl8v0PTGxoaUF9fDw8PD0RERCAvLw+amppYvnw5Vq9eTfL5Fy9ezHKMuDcbql1Pnz7FsGHDYG5uji1btnT7eW57/vw5TExMIC8vzxDU/vr1KzZs2ABtbe1fOomUlZWVwc/PD3Z2dozNsqSkBDExMbC2tmYdcnhFfq5duwZNTU0Smaba39raCj09PVhbWyM2NpYR7eS2pqYm7Ny5EwYGBnB3d2e819HRgevXr0NCQgLe3t4820LZixcveEYN3759Cz09PcjKysLHxwft7e2MBZk6tPn7+5PUnCNHjkBCQoKkRVIb1v379zFlyhSWGCfQ9Uz5+flJpLCiogKJiYnQ0tJCTEwMnj59ikuXLuHEiRM4d+4cg0VGvx/q2vv374eenh6LTVdVVQUZGRlYW1sjJyeH3AuvPnnw4AFkZGTg6upKDlPU5169egUREREkJyezvsdtJ0+ehJKSEitKX1RUhL59+6Jfv34M57o7Gz9+PFRVVXH58mVERETA19cXnp6ecHV1hYeHxy8ZStT95+XlQUtLi7xOOct37txB7969YWlpyUjz4tW3e/fuhZKSEtE+o5z5d+/eQVZWFpaWlpgzZw65Nv15jx8/noAq1PoiJiYGaWlpxMXFwd7eHg4ODvDw8EBoaChrrHHbx48fISYmhvnz5zPa+eXLF1haWqJv377w8vLiyYzbuXMn3N3d8eLFC+zevRtubm4QEREBHx8fXFxcoKSkBFVVVWhoaEBXV/eXWm9nzpyBubk5REREyHyl2ltRUQFHR0eIiYkxQFLq/ZqaGggJCZGIGMWYsrS0xODBg/Hs2TPcvHkTp0+fxrVr13i24/Xr10hNTYWVlRXs7Oywbds2WFhYQEdHB8rKyhgzZgy2bduG8PBwSEpK8jyEfv/+HevWrYO5uTnMzc1x+fJlhISEQF9fH9LS0vD19cWuXbsQEREBcXFxrFy5kudhi7qv5uZmhiNZWloKDQ0NODg4MJ7rpUuXWAeDtrY2+Pn5QVJSkjhe3MDUw4cPIS0tzXAYKXv58iU56A0YMACHDx8mUcs9e/bAzc0NX758QWlpKUJCQuDv749du3axrkP9JneqR0dHB65evfpHwBR9HiQnJ0NdXR3btm1DaWkplJWVe7SWAICTkxM4HA5SUlLg7e0NGxsbiImJwcfHB1u2bMHixYsRFhaG4OBgnql8VBu/ffuGlStXwtjYGBwOhxxY6YfIlJQUeHl5AeC9h1HMB2NjY8jJyWH+/Pn4/Pkz3rx5g8TERNjb2yM5OZmkd7x48YLRltOnT4PD4UBXVxe2trZwcHCAiIgI/Pz8kJ2djfLycuTk5CA4OBhxcXEsBiBlz58/J476+fPnMX/+fHh7e0NdXR0yMjKYPn06PDw8oKmpicjIyG5Te3ft2gUFBQXy/sWLF8HhcBj9eObMGdjY2GDy5Mnkte58j0WLFsHCwoIFBixYsABRUVGQlJTE9OnTeX4XAOLi4iAqKso4CF+/fh2qqqowMDBAdHQ0+Pn5yQGdbo2NjVi7di2UlZUZTHAKmKKeYUJCApycnPDjxw/WGtva2opRo0YRINTU1BQKCgoYMGAA5s6dizdv3hC/R0JCghSI6K4//o6/wss6Ozvh4ODA0txraWnB0KFDweFw4Obmxrg2L3NxcSHjG+had9ra2jB48GBoa2tDQkKCBNa6O8jOnTuX6ERyf4Zio23fvp3Rdu77vnHjBiwsLGBra0tAe/r+oKqq+kttTTq4raamhvnz50NMTIwhTXHlyhX4+Pj8NnVuz549UFJSYmk/FRQUoFevXhASEsLq1asZ7edly5cvh76+Pk6dOsUIAO7YsQMRERGIiIjAqFGjGHsEnYH77ds3ZGRkwNnZGdLS0pg+fTrJKrCwsGCkQ/3K2traEBISwtLOO3PmDPr27YvExMRu1xW6PX78GHJyckQPl/Ip3r9/DwUFBTg7O+P69evdfr+zsxOfPn2CiYkJVq1axXhv7NixEBUVxYoVKxjB1u7G3IwZM8DhcLBu3TrWHjx79mz4+Pj8Ns23oqICQkJCiIyMJKxo6jvPnz/HgAEDePrqAJCRkQFjY2MoKirCw8MD3t7e5NnNnj0b/Pz8vwUvi4uLoaWlRQK7165dAz8/P+tMN3ToUIwaNeq391NcXAwxMTHMnTuX8frRo0eRlJQEPT09+Pn5/bJNVDu8vLzw5MkTqKioYPTo0WTPvHXrFiZPntytL1hdXY3k5GSsWrUK58+fR3Z2NoSFhbFt2zYsWrQIwsLCZC2n3wf9/3l5eSSgamRkBCUlJRbj/8mTJ1BXV8fgwYN/ez//t9o/oNT/Mjt27Bj69u2L2bNn4+DBg/D390efPn0Icn79+nXo6ekhICCgWy2lAwcOgMPhEMfFw8MD2tra6Nu3L7y9vWFhYQF7e3vY2dnBwMCAoaNEn6SbN2/GtGnTMGjQILKAVVVVITo6Go6Ojgxq8q8Q8NevXyMzMxNycnIMJw3oAs/++usvSElJ/fZwcObMGbi4uEBYWJjFOrh79y5iY2OhpaXFc/Fpbm5mOP2enp4M8AToikoNHDgQOTk50NLSwuXLl3neHz1t6ODBg5CXl2doKlC/ffPmTZ70+ubmZrKRfP36Ff3794e3tzcjWv/27VsMHDgQmzdvhomJCezs7BAbG8s45NfX10NOTg6PHz/G5cuXGdG5Hz9+YNq0aaxqZvTDGyXcze00lJWVEVbByZMnwcu6Ay7fvn0LRUVFREVFMVh5FRUVGDBgAMaNGwdLS0s8f/6c1SednZ2kfWVlZWTjpg5U1G+8e/euR5WDHjx4AFtbW4wdO5YRGXzw4AGGDRuGefPmwdjY+LfpgN+/f4eFhQWUlZUxdOhQAmZevHgRhoaGhC3xq7bU19dDSkqKVHWj7Nq1a0hISICvry8MDAzQ0tLSrUMCAAYGBnB1dWU4lA8fPkR0dDTGjBkDVVVVVvrp58+f0b9/f0aKaEFBAQYOHAgLCwuSssJt7e3tjLbU19ejpaWFzKP58+dDWVmZpIsCXc5tYmIicVTmzJnDuI/GxkZ4eHhgyZIl5PU9e/YgMDAQ1tbWxMmnwIZfsfKo3zt37hy0tbVhZ2fHAifu3r0LLy8vFlPk8uXLWL9+PaZOncp4nQKmHBwcfkn5fvPmDWFg1dTUIDc3F2ZmZgTg+vz5M2bNmkUOtxQTSk1NjWeE+OvXr9i6dSvk5eUZ83HTpk1IT0+Hqqoq7OzswOFwoKyszIrUUv176tQpDBkyBIaGhkhNTSVMvtLSUujo6MDBwQEPHjxAZmYmtLS0GGOXskePHsHZ2Rna2tosYIoCtY4fPw4xMTHGWGtvb0d5eTmMjY1hZ2cHc3NzxMXFQVNTE7t27cK+ffsQGhqKS5cukWfj6uqK8PBwBvj09OlTjB07FkFBQZg+fTrpL+rZdnZ2/jEwRX8vKSkJKioqEBcXR2xsLHm9J5F/W1tbWFlZ4cqVK2hubsa+ffswffp0qKqqwtbWloAIvr6+aGxsRFNTE2OuUr/x/ft3rFq1CiYmJqyATE5ODoSEhLotSnLu3DkICQlhxYoV+PDhA3Jzc8HhcAhjpLa2FtnZ2bC3t4eVlVW3VTH3798PZWVlZGVl4dq1aygpKUFGRgZMTExIEElRUZFowVBj7NOnT3j+/DlhU1KpJ0FBQSQa/fDhQ+zatQsDBw6Eg4MDOBwOpKWl8fr1a8LqpduiRYsIY3rXrl0QFRUlB/uGhgYy165fv84zde7GjRs4e/Ys+Vx5eTl8fHwQGRlJWGhfv35FaGgoVq9ejdzcXFhZWaG+vr5b32XcuHEQFBRkBAuqq6sRExOD2NhYnoAUZQ0NDdiyZQvL56GDwjk5OTxBIMo3KC8vR0xMDMLCwpCTk4Pi4mLExsZCX18fampqZH7169cP/fr1Y6zj/66/Qr3GyxYtWgRHR0cWY23hwoUICQnByJEj0dHRwfj+gwcP8OLFC5IOdP78eRgaGjIAn46ODgwePBj37t2Dq6srKdTwq3mZlZUFfn5+VvVgoOtQTBdypvcH3Y+6ffs2VFVV4e7uzlhjgC5/prt15fDhw5CUlCTs5Li4OPTp04dRHbS5uRkhISGIiIj4bcrn3bt3YWlpiZSUFMbhu6ysDNHR0cjNzYWpqSnu3bvHuMaxY8dw8OBBBtvS19cX+vr62Lt3L16/fo2GhgaEhYVhwYIFmD9/PhQVFclcPXnyJGFWjh49mrDL6+rqsGvXLtjY2MDExARDhgxBZmYmNDU1e1TQpLm5mbAkKaP6MikpiWdVTF5zsa2tDSoqKoy0ufb2djQ1NcHd3R0SEhIwMTH5JUPp69evMDQ0JP4xHYRzdXWFhoYGVq9ejW/fvrG0sJ4+fcrYbydPngwBAQH89ddfLGCKrs9H2e3bt3Hq1Ck8fPiQ7Ke3b99G3759ERUVxQKmuPuKsqVLl0JaWhrXr19HZ2cnYb7R9RBnz57NYGBmZWWxmH6nT58mrEhuVt+XL19Iaj59DtP75MCBA5g3bx4KCwtJIHDWrFnQ0NBAbm4uvn//jpqaGgQGBmLBggW4du0a+vbtywAO6annxcXFALr2FRkZGXA4HFaK5KRJk+Du7k7OFE+fPsXu3bsZn9m5cyc0NDTIme7o0aOwtbVFWloaBAQEyP7Dy6qrqzFy5EjcuHEDnZ2deP/+PVxdXaGlpcUCiV+9evVviez/b7d/QKn/RdbY2AhfX19SCvbVq1fQ0NAgizo1kS9cuAALCwueQoxv375Feno6IyJQXl4ODw8PBAYGskCG7jR+pk6dCiUlJYwePRoxMTHo1asXYUXcv38fMTExcHFxwZo1a1jf5bWxvHnzBjNmzICamhpLaPrr16/Ytm3bL9N0KLt48SI8PT3h5OTEKFEOdC1wiYmJrKjwvHnz4OHhgbCwMJKv3NbWBktLS2hpaWH+/PnIz8+Hl5cX0UVQVVUluczU/Vy5cgWzZ89GfHw8Tp06RQ7MBw8ehKqqKvr37//b9ufl5SEgIAB2dnYEWKupqYGzszNcXFwwadIk7N+/H+7u7ggMDERDQwM0NTURHh4OQUFBTJ48mfHMYmJi4Ofnh379+jEiHa9fv4aLiwtx1rifyc+fP+Hu7k4qf3B/pqKiAmPGjIGcnBwjWsxtK1asIKlsVDSN0oEJDg7G7t27UVxcDD8/P4wZMwZfvnxh0YyXLFmCQYMGwdXVFTNnziQRxbt370JISAj9+/dngWsAOyVi1apVSEpKwpgxY0ia1Z49e6Crq4vo6Gjs3LkTpaWl8PPzw/Dhw1FeXg5RUVECPtAZNrt378aJEycYjC9uEfz09HQ4OTkRZ4jelk2bNmH8+PFYvXo1AY8PHjwIcXFxDBw4EBUVFSgvL0dAQABSU1Px8eNH8PHxMVJGli9fjv79+yM5OZkwBh4+fAh1dXXCCjx79iz8/PwQExMDABAREWFoyqxcuRJqampwc3NjrRcFBQUICAiAg4MD4wBMj+ZTNmPGDNjY2MDIyAgrVqxAfX09vn79ipSUFEhISGD06NGYP38+3N3dYW5ujo6ODvj7+zNSc8+dO4eoqCgMGjSIcYDq6OjA7t274ejoiJCQkN8ermpqavDs2TPGenHhwgUYGhryLBDAnS76/ft39O/fHxwOhxx86AAcJX5uaGjIUyD869ev8Pf3R3BwMHFWKWBKR0cHM2fOZHy+qqoKhYWFSEpKYjFF6M7Nt2/fsHXrVsjIyLBSj96+fYsHDx5g5syZ3abN0QMamzZtQkREBKSlpckBorKyEsbGxlBTU4Oamtov06mePn0Ke3t7nsAU8K9UDmrs37lzBzo6Omhra8PBgwcRERGByMhInDhxAjt27ICrqyvCwsLA4XDg4eFBrlVRUcFwmsvLyyEjI4OwsDBERkaiT58+jL6gC/JfvXoVzs7OUFFRIWAEt9H7l/7/rKwsiIqKYvny5d0eZl69eoVNmzZh48aNDECXSjGiaxK+efOGaNwMGzYMDx48wLNnz6Cnp4fExESeKX0UY8rMzIzMk7lz50JQUJAnE5rqs/j4eJJa8PLlS+jq6rJYaz9//sSPHz8Yh6mmpiZ8+PAB58+fJ2vB0aNHoaioiHHjxjFSEoqKirBx40b4+vrCxMSErMkPHz6En58fwsPDMXHiRDK3qquriTYRnWHc2tqK1tZWHDlyBC9fvkR+fj44HA42btzIONCNGTMGMTExuHHjBkRERBhMk2XLliE3N5cnexTo8lUkJSUhKysLaWlpsoZeu3YNwcHBUFJSgomJCQwMDEh6yKZNm6Cnp0f659q1a1i4cCHWr1/PSIHmBUwB7DWloaEBL1++xKdPn8j6QwHNvIJxQNfBPDY2lgRkgK6908zMjLTh7t27GDp0KBwdHRmaY8eOHcOiRYtgaWlJDnKU5MB/wl+h9+/Vq1dx8OBBFBUVoaGhAW/evIGtrS2Z39T9h4eH85R2SEtLg46ODoSEhDBq1Chy6N2xYwd0dHRgYGCA2NhYWFlZQV9fHx0dHZg8eTK8vb1ZxU9mzpyJ3NxcXL58mbyXnp7eLTAFMH3cpUuXYujQobCwsMCyZcvI3kwBUx4eHjwPnLxeu3PnDsOPKywshJ+fH9kjly5dCh8fHxgbG5NADv0669atQ3p6OlJTUwlwvWHDBjKfjx07hgcPHiAgIACjRo0ic4POtk1NTYWoqCj09PQgICDACLKEh4fD2NgY0tLSMDAwgI6ODoAuP1lLS4uAIWPHjoWioiK8vLwY+wU9CHjr1i34+flBW1sbHA6HzM/fAW0rV66EiooKSy9v8eLFiIqKQr9+/ZCbm8u6xsuXL/H161cyz7Zs2QJVVVXG3trW1oa4uDhUVFRAVVWVABndtYUKfFFGjYvY2FgYGxtDRUWFADwdHR2YNm0aLCws0LdvX3h4eDD6lipisnnzZhbATl+n0tLSoKamBmlpaWhra8Pd3Z34ALdv34aQkBAGDRrECiTSK8Z1dnaiubkZQ4YMIQHAkydPQlhYmGQ4NDU1kbG1detWtLW14f379xAREYGLiwsDjLl27RpMTEywcuVKiIqKMopjnDlzBt7e3gxQlD5m09LSICkpSRi6CQkJeP78OZqamrBkyRKIiYlBVlYWysrKMDMzQ3t7O0pKSqChocFanw4dOgQ5OTkkJSURn+/q1auQkZFBbGwsysvLcevWLaSkpEBMTIwEAVpbWwkgFx0djbNnz5L2TZgwARYWFmQve/jwIZYvXw4dHR1wOByemrg7duxAr169oK+vzwjgfP36Fa6urtDW1ubJcvwHmOJt/4BS/0Ntzpw5LGG6T58+QUNDAw8ePMDHjx+hpKTEcGa2bt1KDtu88nzLyspgYWEBc3NzUl6aWqApxlRwcDDj4MsLQDp58iRUVFQIdbGkpAQcDofBHHj48CGCgoIwZswYngfHW7duYdOmTVi4cCGZ0J8+fcKMGTOgp6fHAqa420H9/fjxY9y8eZNE14GuA2hgYCC8vb0ZhwUArPL2CxcuhJSUFNLS0jBw4ECIiIggOzubvD98+HDY2dlBR0cH/v7+pF8dHBwYNNhDhw6hX79+8Pf3J7ozo0ePJqDFgQMHoKWlBR8fH0Z76G1ZsGABZGVlkZGRgaioKPTq1YtoGdTW1iItLQ1mZmYwNjaGn58fieZ4enpi9erVOHr0KPr06YPU1FQScVy3bh3U1NQYIqPfvn1DQEAAI/LHyx4/fkxSGahnRD/Q3717FxMnTmRsUPT7yc7OhrS0NKKiouDo6MgQ671//z7s7OygpaUFFRUVODo6EtaAubk5ieplZGRAUlISs2fPxuDBg+Hi4gJDQ0OyOZSVlUFMTAzu7u4svSR6W3Jycgjgo6GhAVVVVRKBOXz4MCIiItCvXz/o6OjAysoKLS0taGtrg4WFBY4fP87YKOXl5WFpaQkjIyN4enoSx5uywsJCpKamQkxMjDi19PGbnZ0NSUlJ+Pr6QkdHB56eniSqXFhYCG1tbcjJyUFRURHW1tZobm7GmzdvoKWlRaJJs2bNgqSkJEaOHAlnZ2fo6uoSB+T169dwd3eHtrY2VFVVCXOqubkZJiYmDBbh5cuXYWJiAgkJCeKE0hmDhYWFsLe3Zx386X1LiUVu2bKFlNkdPXo03rx5g58/fyI/Px+mpqZwdXVFREQEub6Pjw+ysrLINanKKmJiYsQ5oKdxHjhwAKampoiMjOyWiXfo0CFoampCU1MTvXv3xpgxY8hYOX/+PIyMjODu7v5b8Xaq7DYdAGhrayO/U1lZiTFjxnQLhi5evBguLi4YOnQoATVevXqF3Nxc6Ovr/7LC47Nnz5Cfn8/TQaKAKTk5OcYz6W4e09krXl5ehKnV0NAAWVlZTJo0idX2q1ev/rZaI8AEprjZdFlZWfD19cXXr19RXl4OERERokkBdLFwfH19ERQUhJqaGnz9+hWXLl2Cp6dnt4dHKq2BGjNNTU0YPXo0+Pj4GHpHdDbDhQsX4OPjw3DqX758idzcXHKgoY8lej8mJyeTCDm3VlhFRQXU1NRga2sLKSkpaGlpMSKzdnZ20NTURFFREWusUYed9evXQ0REBH/99RckJCQQHx+PRYsWMT775csXrFy5ElZWVlBTU2MBUm/fvmUASx0dHaSwx8+fP6GoqIiEhAQyDrZu3coT0Hry5AmGDx8OfX19CAoKQkREBEOHDkVdXR3OnTsHeXl5TJgwgQV4UmwvoKt6oYSEBDIyMniyr6qrq6GtrQ0nJycCevE6IGZmZqJPnz7YsGEDYcjduHGDlFjfvHkz+WxTUxOCgoIYLGr6M7x48SLMzMxw8eJF1NbWIi4uDtLS0uQar1+/RmFhIdLT07FmzRqyNlFMvO/fv+PEiRPg4+ODm5sbOBwO/Pz8GMG7cePGQUREpNsqtvfv34ebmxu0tbUJIEH5EXSgeeDAgaivr8f9+/cxffp0SElJMfqbqjLJrWVC6Xk6Ojoy0tGAroNTfX09a37+XX+F29LT06GmpgZra2vo6emRNDTqno2MjKCpqQkTExMYGhqitbWVHKSBLvBMXV0dZ86cwcaNG0lxC+rw/+jRI8THxyMmJgbjx48nc2fo0KGIiYkha3JqaiokJSURHh4OVVVVmJmZYcKECWR8ZWZmQlBQkFGwgNunTE9Ph5SUFDIyMjBq1Choa2sjJCSEBDdv374NLS0tGBsbs8Ztd0BHSEgIXFxcyN8XL15ESkoKFBUV4evri/j4eLI+0MGx7OxsiIqKIjQ0FHJycjA0NCRgxbZt2xAYGAgBAQHo6OjA3NwcbW1taGpqgrGxMTmE19bWwtraGuXl5Xjx4gV27doFQUFBRnrd1atXsWXLFuzevZvMm3HjxkFBQYHBdKNYuBkZGYw9mfu+S0tLkZycDCUlJcKy5QYwjx49ivfv36OzsxPPnz9HVFQU3N3dSSbA169fERISgq1bt2LRokXQ0NDAq1evyPOaNm0aTE1NoaSkhNzcXDx58gQtLS2YM2cOZGRkEBwcjMzMTDg5OcHY2BidnZ0YPHgwBg4cyGhLSUkJHj58SOYGlRbGzcAbNGgQ7ty5g8DAQNja2gLoCg5ISkri6NGjOHHiBLKysqCrq4sRI0aQ60+dOpWlC0cfc+vXr4ekpCQuX76M+vp6HDx4kKTlU/OwtLQUHA6HVGMFus6G6urqjABWZ2cnKVhEAVIUmNTW1oZ169axpEva2tpQXV0NXV1dODo6kjX5zZs3CA4OhqCgIOMs9PPnT4SEhGDw4MGslH2gC4QNCQkhAczNmzfD0dERQ4cOJYDT27dvsW/fPpw7d458Ny0tDdbW1gyZiytXrkBYWBhbt25l7G/t7e04efIkFBUVoaKiQsTSubOCvn79igsXLkBXVxdWVlaIi4tDZ2cn6uvrMWLECCxevJihofnu3btfFmkKDQ0lVYrp/nFDQwM8PDwgLCzMIjv8Y7ztH1Dqf6C1tbUhMzMTHA6HUfUD6KpwMX36dKioqCAxMZFMkA8fPiAyMhL5+fkAeINJp0+fhre3NwQFBUkVkLa2NrLAlJaWwsLCAlFRUYyqCtzXys/PR3h4OABg9+7djMhlQ0MDASmqqqrItenXOHDgAMTExIgQoaCgIBYvXozGxkZ8/PgRM2bMgImJCYPmTDfqWgcOHICqqipUVFQIUEAt5oWFhQgMDIS/vz8DKadvSnfv3sXq1auJrsSXL1+watUq9OrVi7EJfPr0iZFqNn36dCgoKJD7pAQI6eye/fv3w8LCAmPGjMG3b9/Q1NREDue80qGeP3+OnJwchiOwfPlycDgchi5PW1sboy3p6emQk5NDVVUV7t27h4yMDMYm1tHRgdTUVJiamsLc3BwDBgyAnZ0dzMzMGNE5el44Pcr76tUruLq6QkVFhTAW6IcsuiNFf8Y1NTWYPn062aCqq6sRHx8PPj4+Ajg1NDSgpqYGjx8/Jt/NysqCqqoqampqcP/+fejp6TF0P65fv47+/fvD2tqagCglJSWsqCnd3r17h+HDhxPmR2trK/z9/SEvL0/STn/+/Imqqio8efKEtCU1NRXq6upks7548SJkZGQI++/o0aMQERGBjo4OSYN6+/Ytxo8fD3t7e+I00DfusrIyxMfHExbF5cuXMWDAANja2jK0Y4qLi1FWVkbuKSMjA/r6+qirq0NZWRmys7PJHH727BmmTp0KRUVFRlSrqqoKNTU15H6ys7OhpqbGqN5G/RYVBaaM/owpAJuX3bhxA8nJyQyHh6rONWrUKALacDMup06dCgUFBUbBgB8/fpC0L3ppdroTTDEqKKOPuatXr0JISAhr167FnTt3cPjwYRgaGmLAgAEEQD937hyUlZUJ+49+jfb2dkY7Hz9+jMDAQMjKyhKnhw5M8WKR0q+1evVqODg4dAtMcQv1A10sLQMDA+jr60NZWRnLly9nifZ/+fKFAFPd6XdwM4O+ffsGExMTlJaWoqamhrBcKTtx4gTPaN/Tp09ZwD73+y4uLlBQUMD+/ftx4MABpKenQ0JCApWVlXj48CGEhYUJkEQfV4cOHYKnpyeCgoJIqld344xihJqYmDBeHzFiBHr37o3r16/zBAg7OjpYAZpFixZBS0sL6enp5BnSf5fexilTpkBERAQbNmxgsLf69euHjIwMNDU1obCwEEpKSggKCmL0u42NDQGS6denAK7Pnz9DTU0NR48eRW1tLWbNmgUHBwdYW1tj1apVhHlJRZmtrKwYGhZNTU1QUFBgjC+gq9qRg4MDlJSUMH78eLLOt7S0YMCAAZg5cyZjTaqoqICCggISExOxbds2PHr0COnp6dDQ0ICenh5evHiBgoICKCgoIDk5mSdj+d27d7CwsGCBnNzPkwKm3NzcWPsgfd/JyMiAkJAQtmzZgqamJnz58gVTpkyBnp4eFi5ciIaGBty5cwcBAQGkQih9rQO6AjIzZsxgHKyALkF7CkTnrh715MkTpKSkQFRUFJWVlXj79i2GDh1K/LAXL17A0dER3t7eDD2rmJgYKCgosK5HB2SPHTsGFxcXyMrKMljcTU1NOHXqFNTU1CArKwsTExPY2NgwnnV5eTn69euHzMxMxvXp6exDhgyBs7Mzg73JC6z+T/grQBdrR0FBgaxPubm56NevH0kTraurw+XLlzFjxgysXbuWzCuqTefPn8eECRMYrP3i4mIEBATAx8eHJVAOdK2PkydPhqSkJAHsCgoKoKSkRIDp1tZWLFiwAPb29khPTydjIikpCa6urjx947KyMmhrazOqhhYWFiI4OBj9+/fH69ev0dnZiaKiIvTv37/bIMC3b98Y13/+/DmUlZVZfjy3VhJ9zXn37h0GDhyIu3fvorOzE9++fYO9vT0jLe779++4f/8+7t27h87OTrx58wYpKSnQ0tLC69evMWfOHAwZMgSjR49mrbmCgoKstDigy7caM2YMYbFT32tra0NsbCwGDx4MLS0tRkU6XuDEw4cPYWpqyspUSE1NhYyMDCQkJKCuro4NGzagra0NJSUlGDp0KISEhGBqagptbW0YGxsD6ALg9PT0SH/t3r0b8vLy2LdvH1JSUmBtbY1BgwYR+ZKzZ8/Cw8MDAQEBGDZsGFnfQ0JCCHDd2dmJqVOnQlVVFWJiYggLCyNM1fPnz0NdXR2ampoIDg6Gubk5NDQ0AHTJEdjZ2eHLly/w9fVlZIE0NDSQttJZnGvWrEFbWxtLQ7CjowPx8fGMQA3QJYjv7e2NhIQEEnR++vQp4xkeO3YMwcHBsLe3J+OhpaUFI0eOhIODA0M3FvhX0QO6b0h/XpQmV3h4OGFMHThwAObm5ggMDER+fj527NhBWLFtbW0oKChgrO35+fmIjIzEgAEDGG3dsWMHHB0dMWzYMBZwdP/+fSQmJkJMTIylzTR79mwCIlJtpbf5y5cvKCkpwePHjwnDkz7vqDa8fPkS8+bNg5qaGvT19bFnzx5MmjQJI0aMIPswvb2/SgX29vaGoqIiLly4wPjOly9fMG7cuH+YUT20f0Cp/6HW2NiIvLw8QmUHuiZMWloaxMTEGOWHgS4nzsDA4JdoL9C16Do5OUFPT48saPTUlPLy8m5Lm1O2ZMkSODg4oKCggKHtAHRtIvHx8QznnD7RHz58CHl5eWzdupVEWOfMmQMpKSkSxa+rq0NaWhrs7OxYEWrKiouLISIigi1btuDevXuorKyEg4MD1NXVSSWHgoICODs7IyIiAvHx8YxF4/Lly0THgn7oo7Q8+Pn5WWyGJ0+ewNLSEuLi4oxI8LNnz6CmpsYqg75v3z7069ePsLh+/vyJb9++ITU1leHoFRYWgsPhQEZGhkVjXrFiBXr16oVFixYxaMBlZWWIiIiAiooKSktLMXXqVOjo6CAxMRF2dnbo1asX2YQ7Ojpw4sQJTJkyBcnJyVi6dCnD4aDGUUFBAYYPHw5HR0dMnz6dgB5Uqp+qqioBpuh9uXLlSkabDx06BA6HA319fUbFszdv3iA+Ph78/PwMoBAA7O3tYW9vzwAAbt26BSEhIZam07lz52BiYsLIlads+/btjH7666+/ICYmBhsbG1Z6nb+/PxQVFVllgy9fvgwLCwtISUmR59zc3Ixx48YREd3a2lqoq6uTNCS6MP6HDx/w/v17liO6f/9+WFtbw9HRkaGFVFRUhKioKNjb27PSZxMSEjBq1ChISEigrKwMp06dgpycHLS0tBiATnV1NalORZ+PQFe6WXR0NKSlpVFaWoo7d+7g/PnzePr0KTmwX79+nURvKeOuzjNu3DgWI1FLSwsyMjKsKoNr1qyBg4MDRo8ezYr2p6SkQFlZGaWlpSRCRddwoKL43WmtAF0Uc24nYPr06SzBzGvXrkFPT49Q91taWnDx4kXCnKGPfYqJN378eMImefbsGcLCwqCgoEDWS14sq48fP7LSvFpbWwlAxw1MzZo1iwhQ0+379+8ICQnBzJkzUVRUhIiICJiZmRE9A6qfvn37hu3bt6NXr14s5kR9fT0CAwMZ1SPr6+vh5OSETZs2QVNTE/Hx8WTMV1dXY8SIETh58iTrwJacnAwOh8Oar3RraGhAdHQ0AY0CAwNRWVmJiooKSElJQUpKijGHuQ9J3t7eCA4O/mW6YFtbG1auXAlBQUHMnj0bQFfadZ8+feDu7o6oqCgoKysjNDQUeXl5qKioYImgV1dX48KFC2hvb8ecOXNgbW2N1NRUnsAUfWzNnj2bzLVXr14R9ifdbGxsoKuri69fvzLuz9XVFaKiogSE/vLlC6SkpEgq9eLFixEbG8vod3FxcXJgmjdvHiNowm0nT56EkJAQIyX50qVLJNWJOsx1dHQgMzMTampqDFCJAtgyMzNZ43rfvn0wMzODra0tGhsbsX//fqipqWHUqFGsdJLi4mKYmpri7t27PB17+v29fPkSEhIS8Pf3Z2n0AF1rx4YNG8DhcCAhIUFSUl6+fInU1FTIy8tDTEwMpqam8PLyQmtrK8LCwsi4oK7l4uICDoeDyMhI1lqWmJgIBQUFrFq1Ck1NTejo6EBrayvmzJkDDw8PlJeX4+rVq4iKioKnpydjDXv27BmcnZ3h5eXFYMlyswsfPHgAERERhgYcxXygtPTo1tTUhBMnTqCkpISRKllTUwMJCQmSgk3ZypUrkZuby/DdhgwZAnd3d8IEW716Navq8J/6K91ZQkIC0tLSAHSxjUVFRQkTqbGxkcF8iI+Px7p164iv+ejRI+jq6qJfv34s0LC4uBiBgYEICAjAnj17yOvPnz8ngUy6/7V582bo6OgwgJ6vX78iPT0dtra2jHnT2dmJ+Ph4ht4p0MV8lZOTYwliFxQUQFxcnKEfSpmDgwOjfStWrICxsTFmz55N9vefP38iOjqaaNNR2QnUGD18+DADNF+9ejWUlJTg7OzM8MObm5sJK7WsrIwxX3x8fKCqqgoZGRmUlpaio6MD8+fPR+/evVnV+oCuNVdISIjB6gG69Aap9DFqLdi7dy8jdX/cuHHQ0NBgAFMAWNXGtLS0GKDNxYsXYWNjgytXruDt27cYPXo0DA0NsWTJErS0tODbt2+4cOECZsyYgfXr15P5mpSUBD8/PzQ0NKCoqAjJyckMJu3evXvh7OyMgQMH8tTQbWtrQ0pKCmRkZIgveunSJWhra+Pq1avYvXs3hgwZAisrK/IsP3/+jKlTpyIpKQnm5uYIDQ0FAERHRyMyMhLfvn2DgYEBS2+ysbERwcHBrFTc9evXw97enqWjFhcXB09PT9Z6mZ2dDQMDA5YeFX19vnDhAgYNGgRbW1sSTKLAJXNzc7x58wbNzc149+4dAgIC4OjoyBM0ycrKQkxMDHR1dcHhcODq6kqe5Z49exAdHQ0RERG4u7tjyJAhaG1tRW5uLoYPH86QccjOzoa8vDw0NTVZGlX5+flwdXVFQEAA2Tc6Ojpw7NgxJCYm8tTeCw8PZxQ6oI93Xoykp0+fIjk5Ga6urjAzM0NUVBTJhGhra0NtbS3CwsJgYmJCKsJy+03053Dr1i1cuHABT548YfS7u7s7lJWVWcAUZf8AU7+3f0Cp/2FGnxgvXrwgubEUA6qxsRGhoaEwMzPD2LFjsWzZMowYMYKRKgQwq6dVVFSgqKiIvHf58mX4+/vDzs6Oweb4FUq8dOlS4oi/evUKNjY24HA4DECCKgdMUSWpCkZ0u3btGrS1tRksKqArHalfv35k0Xr79i1xsouKilh52evXr4eLiwuLrWBra8soP3rx4kVcvHgRQUFBDMf0xYsXyMnJQd++fYmoMmWNjY2kJDPl3HV2dqK2thb29vYsMKSsrAxSUlLk8EA/YJubmxOWANV3Tk5OrAWNEqPlTtkEurSQOBwOT+G+Z8+eobCwEKKiogRca2pqwo4dOyAgIICJEyd2K9RKX0CPHTuG3r17IyEhgVTEcHd3x7Zt2wB0gTDe3t4swdSzZ8/C2tqaReMdNmwYYTBQ/Qd0AVNjxowBh8Mhh9CPHz9i3rx5WLBgAcP5f/HiBSwsLLBhwwbGs+vo6ICamhrRVqNs7969MDU1ZYyrt2/fwt3dHfz8/KQt9PeDgoLQq1cvhjDny5cvIS8vzyoD//TpUxQVFeHbt2+wsrIi6VPHjx9H7969ISkpScCZzZs3IywsjOGA5ufnw8nJCeLi4iyR4mvXrmHw4MHQ1NQk750/fx729vbIyckhjkdRURFGjBgBQUFBltB2dXU1MjIywMfHx0jRq6mpwcaNG/H48WOkpaVBWVkZKioq6Nu3L4YPH07GzfXr11ksIsru3buHyZMnsw53M2bMgLy8PKKjo1nVKNetWwdtbW3Gc/r58ycKCwtRU1ODI0eOwMjICBoaGlBQUEBWVhYBDg8ePIh+/frxZALt2LEDXl5eDMYg0MVqodhe7e3tZEzu2rUL/fr141m6G/jX86MOWVSKEeWkPnz4EAMGDEDv3r15OlBPnz4FHx8fdHR04Ovri7179zLSybZt2wZHR0cMGjSIOPLV1dWYP38+I/WVGieXLl2CkpISqqur8fnzZzx8+BDh4eFQUlKCl5cXrly5QkD/Xbt2scDWN2/eICwsDO7u7oyDAcWiDAsLY3w+MzMTRkZG3fbP6NGjISIiwhDKpbeXslevXuH9+/doaGhAWVkZARZtbGzg6+vLYFzR17/Dhw/D398fLi4urIPFkydPCFjW0tJCylO7urpCXl6etOnbt294+vQpEhMTYWJiAlVVVQbw+/r1a0hLS0NHRwfHjh1DR0cHZs2a1S0w1dLSgqysLBIooay6uho2NjYIDQ0l84aq8mVra4uQkBDExsZi6dKlBOyIiIggQFBLSwvCw8MRFRWFlpYWXLp0iQHEjxo1CvLy8iguLsa6deugp6cHExMTlqA/PXXm3LlzZPw2Njais7MTK1asgIWFBQwNDTFixAiEhIQwQHbqeXEDbJ2dnYxnQ5VopwJj69evh6GhIWuur1ixAiIiIjydcnoRAwqco3TfuC03NxcSEhLYv38//vrrLwwfPhwCAgIE7GhtbcX79+9x9uxZPHz4kPTB6dOnGVW3KBs6dCiEhYVx/Phx1to1ePBgBAcHM7Toxo8fT1gXN2/ehJKSEgQEBFiaX1VVVfDw8IC1tTVrn6DuuX///hAUFMSFCxfIb+Tk5IDD4SAtLQ1//fUX7t+//0vg58ePHzhz5gxMTEwwcOBA4q/NnTsXYmJiLLCkoqICQUFB8PPzQ3V1Nfr378/q5z/xV+j3w/13dHQ09u3bh6KiIggLCzP0ODds2IA9e/agtbUVDQ0NWLx4Mav/T506BUtLSzg4ODD8U6CLgWtra8uopFhcXIzBgwezmFsHDx6Enp4eeW5UW589e8YSev7w4QPy8vJYbbl79y7k5OTIc6a/b2hoyPI1WlpasGXLFga77/Xr1xg7dixcXV0hIyOD5cuXo6qqChUVFejVqxfrWW3YsAEWFhYMf6SqqopUp6XmKvV+c3MznJycICoqyljz7927R1JrqX2hqakJ69atAx8fH6vtQJfvSNfuA7pY9yNHjiT3XlVVBQMDAwQHBzOCEuPHj4e2tjYWLVqEx48fw8vLC87OzuT9devWQUREhIBAO3fuxKRJk1ggTlJSEgGmuNe3IUOGIDg4GGJiYqisrCTpu+Li4gyGH9Dl97m4uGDIkCGMAPP9+/eRkZEBTU1N0peHDh1CQkICo6pyRUUF4uLiYGlpyUodLy8vx5s3bzBx4kRISkri/v37+PHjB+Lj4xEeHs6q9paSkoKAgADGGvrhwweyLtLX3xUrVsDQ0BAFBQWMc8yhQ4dgbW3NCsjTn9WJEycwceJE9OnTh8GYunr1KsTExGBhYQE9PT04OzvD0tKSp2bZsmXLIC4ujqKiIpSWluLUqVNQV1eHg4MDA2R88+YNWltbybx68OABuT+637xy5Uro6OggKSmJRWxYv349EhMTWZXteMnMUNcyNTVl+MkdHR149+4d4uLiGCnoFRUVkJaWxpAhQ5CRkYGRI0dCV1cX/Pz8WLduHWs/i4uLA4fDgaSkJAGs6etbVlYWlJWVoa2tTSpw0+ebh4cH1NTUcPr06R4VP/nHmPYPKPU/1A4fPgxzc3MMHDgQgoKCpMwo0OWAZ2ZmwtvbG7a2toiJiWFUAKAmGCVYqaGhAWFhYfj7+5NF8cKFCwRB765KH902bdoEOzs7VFVVobm5GUuXLoWZmRliYmLw8OFDnD59GgEBATA1NUVrayuqq6thYmLCSqk4deoUBAUFSUoUtSi1trZCTU2NoRfR2dmJM2fOQExMjHUAnTZtGtTU1MjflGN1/fp1KCkp8dTNAP4l8gd0AS28tAaoPj506BALPKIW95s3bzJKlQ8dOhRycnKMalWtra1wcHAg9HTuBWzPnj0MJtHUqVMhICDAU5ti//79PCvFAMCRI0ego6PDSh1Yu3YtOBwOZs2axbOiF2UfP36Eo6MjQ4SU0hhxd3cnG8OzZ88QEhLCYOjQwUy641JRUUEOQty6SrW1tZg/fz7PSMP8+fMZQtxRUVEwMjLC+fPnyfe/fv0Ka2trln4GvW+uX79OAID379/D2toaRkZGxImgb0JTpkwhmzX1feo5X79+nXVYLygogI2NDWElUpTrtLQ0AqrSHRH6YfzEiRNwdnaGr68vA7gAuubk9OnTyfcogdvW1lbGoai0tBTDhg2Djo4OS2D32bNnWLNmDetw2NnZiXXr1kFGRgaXLl3Cx48fceDAASLeTwGExcXFEBAQYKXh0Ptsx44dDIA0JycH5ubmmDZtGitievjwYZ5siIsXL0JQUBCLFi1CUVERZs6cCVtbW4b+wJEjR8DhcFht+fLlC3F66JVOdu7cCQ6HQ8BH6vXCwkIYGBiwDtKdnZ34/PkzHB0dGeWKP378iIiICDg6OpJ1lSqvTh/7lJ09exYcDgfa2tqwtbWFg4MDRERE4Ofnh+zsbJSXlyMnJwfBwcGIjY0ljlB3ulbfv3/HiBEjGIB/dHQ09PT0EB4eDmVlZRgYGDDWH8qo8VtXV4eYmBg4Ozsz0mNiY2PRt29fLFy4EHPnzkViYiJERER47gH05xUbG8sTmAK61vCsrCzS71VVVQxR3efPn8PU1BS+vr4Mph39/vfs2YOIiAiWqDl34KO1tZWwH4cOHcq6FvXMuasGXrp0Cb169YKNjQ2Cg4Nx6NChboGpHz9+YPz48eDj4+sWhPT390doaCji4+MhIyODAwcOEKA1Ly8PcnJykJWVRXR0NOtAv2rVKkhKSpK1Yty4cRg6dCj69+8POTk5FguXPqfq6+t5jsEzZ86gd+/eGDVqFNFNvHz5MiZNmoRBgwYhJyeHBV7SATZuUIDeZqoCImW8yrTn5+dDTEwM9+/f51kIAeg6aIwbN67bIMmXL19gZmbGYt5SosF//fUXT7YYfT9cvnw5YmJiGHorQUFBkJWVxZkzZ1hzji4vsHbtWhgYGBA9PKBrDOrq6iIoKIghXA90jYOAgIBu2emfP3+Gu7s7nJyccOPGDcyZMwdiYmIYP3481q1bB1NTUzg5OUFdXR1TpkxhpcneuXMHenp6+PDhA/bv3w9PT08MGjQIEyZMgKysLItVTdm9e/eIf/Xv+Cu8+pee4puTk4PevXtDUFCQsR98+fIFnp6emDNnDutZb968GVOmTCF/nzhxAra2thgyZAhhNlB2//59llA2XRuNmps1NTWQkZHBiBEjGAz9p0+fwsTEhOxt3G3ZtGkTQxh79OjRkJCQIKnEQNczNDY25ulrUDZ79myyf3R2duLjx4+YOXMmHB0doa6ujlmzZsHe3h4RERGstGpqvSopKSHg5MuXL6Gnpwd7e3uSTkW1vbm5GQkJCaw9tb29HRs2bICqqirxuZubm7Fs2TJwOByWVh3dKHAgKSmJpDxSa+G5c+eIFiTdv5s8eTJ0dHSgqakJW1tbhih4bGwsI+js6elJioZwz7/k5GSYmZkhNzeX3P+jR4/g4+MDf39/RlrX6tWroa6ujpCQEBbQun//fujr62P69OnktZiYGIwZMwZ1dXXo7OwkQLK4uDirkjcFTNnY2DCY5q9evcLSpUuhqqrKqKR44cIFSEpKYuzYscRHaGxshKurK2Flc2tunT9/niEC39nZCVdXV5iammLfvn14/fo1Pn78CG9vb4SGhna7Tk6ePBna2trIzMwkgUxbW1vSVzU1Nfjrr7+wYMECHDx4kIwV7r4fNWoUq2DKgwcPoKSkBB8fH55pu/Q2HT58GEZGRowCSvPnz4eFhQUmT57c7brIXekS6DoTPHz4EHV1dQwmZVxcHPErfvz4gRkzZkBdXZ2M8draWmhqarIqhFPFtnr37s1i8b9//x47d+7kKVcwZ84cKCoqEj9l/PjxEBERwZQpUxj7p7GxMWNP/Md6bv+AUv8DraysDH379sWGDRvw6dMnVFZWIiUlhQFM0SO6vKKT169fh7CwMDZv3ozy8nKUl5fDwMAADg4OxGGj0tu8vLzQ0tJCFghei+HDhw8J3R3ockxXrFgBGxsbCAoKwsrKCqGhoQxEnqKflpaWMtI3HBwc4ObmRoAS6nBoZGTEswoRtRG8fPmSbFz379+HoqIiKwp08+ZNqKurE8YN/V7q6+shJiYGW1tbBjA1bdo0ohvCy6gUNzpgERgYCBsbG+KM1dXVwd3dHTIyMjh8+DARP5SQkGBtoB0dHfj06RMRTaVHW1JSUtC7d+9uRVN5HWRv3rwJPj4+EomjizGLiIiAw+GQkr68rKmpCQYGBuTZ0ll2GhoaLE0rXnbv3j1wOBwkJSWR1yoqKhAREQF5eXmegt8AO0UsKSkJgoKCDMfAxcUFBgYGGDNmDJYuXQpPT0+S204ZXRPr1q1bJEWCcgLfv38PCwsLGBsb8wSmqNx1OsW6ubkZ2traMDQ0ZGzQR48ehaioKOnvzMxMjBw5kvwWvV1Xr16FvLw8Y9M8ePAgfH19ERgYyEpNpIw+px88eIB+/fohIiKCvHb79m3ExsbCwMCg2+qH3I5rTEwMywk5e/YsLCwsSApFe3s77t271y0Nua6uDl5eXnBycmIAYllZWbCwsOAJTNHbQjHHxo4dywAVgC7Wj6WlJUnDaWlpwYkTJ0gEHGD2bUVFBaytrbFs2TLyenR0NMTFxXH16lVGdSdTU1OeVdR+/PgBU1NTMt6o8fj582eoqqqS9BSqPXR7//497ty5Q7RTVFRUMG3aNFy7dg0lJSXIyMiAiYkJTExMIC8vD0VFRTJHqIN7bW0tdu7ciY0bNzIAmXnz5kFdXR1AV+qLvLw8cTrPnDmD1NRUhlPFTfUHupzT6OhoODo6EsZUZ2cnsrKyYG9vDxsbGwwbNuyXpevpNnz4cBYw1draSlL8Kioq0NHRgQsXLhCGJfXcewJM0UF1imnFi7XR2NiIDRs2oFevXgxdLjorkZfFxcXB3Nwc/fv3h5ubG44ePcoCpr59+4aUlBT069ePcTjltidPnsDHx4cAq9xGgb50AIneNktLSwwcOBBA1z5MVcDitW9RVlNTAzExMfDz82Ps2LHIyspCVVUVWXcuXLiAvn37YtSoUTyBI15GAWx+fn4MYIr+++7u7oy5yj1uP378iFevXkFYWJjBbOSOiicmJvJkbVDX/PTpEzQ1NUlqFX2+ubm5QUlJCStWrGAwe7ht+/btkJOTw4QJExjjOjAwEHJycigoKGDtOfR2rlixAo6OjoiNjSWAze3bt6Gjo4OIiAgWw5V7P6T6Zc2aNfj58yc+fvxI9L1ERUUZc6etrQ3Pnz9Heno6vL29GX4CpUVFL32+e/duODs7o3fv3oyDbXf9+Z/wV+h9M2PGDLi7uxMW7vfv3zFo0CBISEigpqYGnz9/Rm1tLfz9/Rk+FmU/f/7EmDFjYGVlhdz/t6Ia0BV8sLW1xeDBg1nAH9UG+n70+vVrWFtbw8bGhgACV65cgaCgIKKiorB7925cu3YN/v7+sLGx4alJ097ejhEjRsDKyooE49rb2xEZGYm+ffti2rRpmDt3Lvz8/GBqatqt7kxnZyemT58ODofDAvOePHlCKghyOBwEBgayfOyOjg5cunQJHA4Hq1evJmvgy5cvoa2tDUdHRxYwRb8HutXV1cHAwABWVlYMLcdly5aBj4+PlRFAGbWvNDY2Yvr06bCzs0NKSgphgZ0/fx6Ojo4sYKqoqIikQwNd4+zMmTNkftEBz+HDh0NFRQXbtm1jsWOGDx/OAu+/f/9OtBPp2QMrV66Eubk5kpKSWCwlelu+ffuG/Px81ly/ePEiAgICoKGhwdIsq6ysREREBGJjYxnPuLa2FsePH4e9vT2MjIzIfZ04cQJKSkqwtbUlwSh6JUV6ILi+vh7t7e1Eb5G+H/v7+8PY2BiioqKkEBV1De5nfvfuXaioqDD0ug4fPgwfHx/Y2dmRNY87cM3LnwsODoa3tzf5mxrjlGyMpaVlt/IpQBeQOnDgQLi5uZH9HujyXSwtLZGSksJK86YbPYVVV1cX+vr60NTUxKhRo/D69WtcuHABVlZWMDY2hqWlJby9vSEpKckI2uzZswcODg6EtMBdoTEkJASysrLd6uJxZybRC30dPXoU4uLiGDJkCPr27Yvk5GSGL/pPqt7fs39Aqf+Bdvz4cRgZGTEcyy9fvmDy5MngcDg8I+TctnTpUri4uDA0g758+QJdXV1GJbYLFy50m7bB7VQsWLAAenp6jLxgoOtw+OHDB3LQamlpIe99//4d6urqDM2QgoIC2NrawtnZGU+ePEFFRQVyc3MhLy/PoH3SBY6rqqrA4XCwZMkSNDY24vv370hPT4e9vT3RZ2hoaMD06dNhYGCAd+/eMRYcaoO9c+cOqTZBB6ays7MhLi7OYOnQmTmUXbx4kehuhYaGwsPDg6RR1dfXY+TIkVBUVISOjg4sLCzIAsrLeXz06BGkpKQQFBTEcASpQxE9AkHvb17PKSoqCm5ubgxNhLq6OowdOxaFhYU8waS6ujo0Nzfjw4cPsLS0JMAJfcxER0cjNDSUJ/OGbi0tLcjPz4eQkBCD2VJeXo7+/ftDSUmJBcDcv3+fABhTp07Fw4cP8eHDB+Tk5EBERIQRMU9PTyfMvpiYGJ4i7XRbtGgR+Pn5MX/+fAYwZWlpCXNzcwZDjerXJ0+eYMKECYiIiCAHzZqaGhgZGcHW1pbMk0ePHiE0NBRKSkpwcHCAsLAwg8ZM2fHjx3Hr1i3k5OTA2NiYIZ5/4MAB+Pr6IiQkhCVkzX0/jY2N2LNnDzQ0NDBgwADy+q1bt0i5Yu7UTvo1qGc3fPhwDBo0iPEa0BXplZOTYzHtukvpvXLlChlv9CjUtGnTYG1tjaSkJBYAxH1PCQkJZB2i/0ZGRgbU1NR+eQgAutIyf/78iYiICLi6umLdunXo6OhAfX09hg8fjl69esHa2poIf3KnEVNOSkNDA4yNjQmYSjHTgC4wKDIyknX/QBdQ6OTkBB8fHwIWbt26FcrKyhg/fjwDmCsqKsLGjRuJUCgFJt2/fx9mZmaIjo5mgF+U+fr6QlpaGgoKCizmJ/353bt3D7KysoiJiUF2djaqq6uJk/bu3TsigEo/NFFMPu7S9VT/3rlzB9u3b8eqVasYB8WRI0cSYKq9vR0TJkwgqSZVVVWYP38+Y27R21pVVcUTmOJeWyorKyEkJMTSmtm7dy+5r9bWVpKewkufh27UPZ46dQojR47E2bNnERkZCScnJ0YqH1UNVFBQ8JeAFGXPnz+Hr68vAgICGIAO/SDE3b/UuF64cCEsLS3JmuLj48PQc+Nl58+fh5mZGTgcDsaOHQt7e3uoqqpCR0cHeXl5KCoqQkFBAfj4+JCVlcUo8/0rsI4OTNHXoo6ODtTW1iIgIIAcOjo7O1njlrr2nDlzwMfHx0i5Arr2hmnTpkFTU5NnQIBuERERsLCwIMATVYhlxIgRUFNTg5ubG+NAz8v27NkDJSUljBs3jgFMhYSEgMPh4ObNm6zfp683y5YtYwFTt27dgo6ODqKiolisMsq4+4Vq39evX+Hv7w9dXV2cO3eOZ7vpB3VK54temZSyc+fOwc3NDZGRkWTP4QY6/hP+CvfvpqWlQVpaGgUFBYSFBXTt7x4eHujXrx+0tbVhZWUFe3t7UmWPe26/f/8eqampsLOzY7Bajh49CgcHB/j6+rJAcvpaum3bNnR0dODMmTMICgqCvb09AaZu3rwJa2trqKurw8DAgOiNUX1KWV5eHq5cuYL6+npMmDABdnZ2DGA5JyeHMNyGDRvG8DXoz66uro6kiS9cuJAnMAV0AdR0xjCv55+eno4+ffpg7dq1DGBKR0cHLi4uLB+9u7H/9u1bmJiYwMzMjAFMrVy5kufZISsrCwMHDiTj//v378jKyuoWmIqMjGQUnqGM27/ctGkTIiIiGGzwiIgIGBsbY+fOnSxgmVdBpI8fPyI8PBy2trYMfc6lS5fCwsICSUlJPEEP7jG3fv16DBkyhPx96dIlhISEwMvLi8X8ff78OSMj4fbt28SXuXnzJlxcXKCnp0fWhZKSEmzevBmTJk3CkiVLyHePHj2K2bNno7OzE4mJiVBSUkJHRwdev36NrKwsiIiIYPXq1eR3i4uLsXPnThw9erRbZhPQldbKS2d1586dEBYWhrOzM8vX6W6sUKAadzrk1q1bMXLkSAwZMuSXYxboAsmGDBkCJycnBjC1YMECKCkpsVivAPMZX7p0ieHrL168GHx8fOTsU15ejn379mH06NFYtmwZiyWckpICMzMznm0DugLAffr0Yenncbfj3r17aGpqwpEjR0iaOb39EyZMgKSkJEaPHs3I/vkHmPpz+weU+h9o58+fR69evcjmTE2e27dvg5+fHxwOhwVY0CdYZ2dXdQlzc3PyGuX0XLt2jaU/xcvmzJmDhIQEhm5NZWUlTExMiIYCHc2nfp9+sL1z5w6+fv2K4uJi6OnpISoqirC0CgsL4eLiAkFBQejo6EBbW5shksodqQC6NlBBQUGyUNTV1SEzMxMKCgqQk5ODlZUVZGRkWGKra9aswZo1awjj6s6dO9DS0mIBU0lJSfD29mb05YcPHyAtLY09e/bg5MmT4OfnJ2LUVVVVCAwMhLu7O4PZ9OTJE9TX15M8eXpb3r17h/b2dtJ3jx49gpiYGAuYio+Ph5ubG/mbfo0NGzZg0qRJGD58OA4fPozGxkaUlJQgKCgIJiYm2LhxI44dOwZfX1+4u7uT+6GDfM+ePYOYmBg5fG3cuJGnblVYWNgvBQHp0ez29nbs3LkTvXv3ZgBTFRUVcHd3JyAEdbCRlpbGkiVLMHbsWHA4HDLe37x5g+zsbBYw1dbWxhhf9MqRAFiOzqJFi0j1QspR//DhA5SVlYloLF0kVkZGBuHh4Rg8eDD4+flJBLW2thZ6enqwtrYmDvmNGzewcuVKZGZmkkM4vS0zZ85Enz598Pr1a9TV1WHmzJnQ19dnAFMHDx6EpaUlUlNTefZtW1sb6d/Gxkbs27cPKioqDGDq9u3bCA8PZ7GOKFu/fj15xosWLUKfPn1YB25K64o7xZPbAaen0F65cgWRkZEsYGrChAks4WbKbt26RSjQubm5kJWVJRRv6reOHj0KY2NjlsbEkydPsGDBAgBdgrxWVlb49u0bPn/+jKFDh8Le3p5RIe3QoUOYO3culi5dymIq1tTUMMS79+7dCz4+Pob+EtAltpmYmMi6l/v370NcXBxZWVmoqalhOI/5+flQVFREcnIyQx8N6HqGFKPp/v37kJCQQHZ2NiP4cPz4ccJEmDt3LkRERAiI1d1BfubMmeBwONDT04OqqioMDQ2hqamJGTNm4NKlS3jx4gWGDRuGkJAQlgg+Lzt48CAkJSURFhYGCwsLWFlZMUCzuLg4SEhIwMfHB8LCwrh79y4qKyuhpaXFEiemjBuYCgwM5CmeXldXBw6HwxrP8+fPB4fDYYBzLS0tRBCbm6306tUrVmrr+/fvoa+vj9WrV+P9+/eIjIyEs7MzAaaysrJgaGjIE2DuzroDdICuyGt4eDi2bNnCYgfU1tZCQkKCrAdnz56Fqakpo/oqtzU3N6OgoAB2dnZwc3NDe3s7KioqkJOTQwTV/f39ISkpCQ6Hg5kzZ/ZY86I7xlR6ejrMzMzIHtzduD137hxWrVqFjIwMCAoKwtXVFbNmzUJ2djb69+9PiiwA7HWlurqarOt3796FhYUFQkJCGGBA//79UVFRwUhXouz06dPYv38/bt26RV7fvXs3lJWVMXbsWAbIMXXqVPKZDx8+MMBzbr0VR0dHJCYmEo2qkpISSEtLIyYmhrXXdNcvhw8fxrVr19DU1AQ3NzfY29vj1KlTpA+42ayUzhfFoqNs8eLFJGi0Z88eeHp6IiwsjJGmSN3Tv+uvcAPg169fh46ODgEYmpqa8OrVKxw8eJD03/79+7Fr1y6cPn2agDfUunj37l08evSIACvv3r3DlClTWMDUnj17GAUYgK5xpaqqisePHxMNHQpsPX36NPz9/WFvb0+eMcXWevLkCbnOo0ePyBqRlJQEcXFxsjbX1dVh3LhxsLW1ZcgXfP36lQEuc/saM2fOxNChQ0kQsKmpCQsWLGABU93JP3D/HwDRg+QGpkRERDBmzBjyOe4UqiVLluDSpUvEx+EFTDU3NzMkICi7e/cuo1IZ0MUy6g6YcnFxgaura7fyGJRt2bIFZmZmiIuLYwAo4eHhMDExwe7du1lrIi8QprKyEiNHjoSjoyMDPFm2bBmsra0RExPDAN8BNkNz7ty5MDAwYLA4CwsLERoaCi8vL57VuTs6OjBt2jQYGxvj2LFj5FkVFxfD2dmZAUxxW3t7O6ZPnw4ZGRm4uLhAWlqasQa9efOGAFPd7cfcwVaqXVVVVbCyssL69esZvndHRwesrKygrKyMUaNG8eyLmzdv4uTJk3jy5Ak+ffqEpqYmjBkzBnZ2dmTM1tfXIygoiMFopT+XLVu2YPbs2ViyZAnxZe7cuUOAKXqaa35+PqvKH2XUvU2ZMoW0t66uDhoaGkhMTCSf48UAp9uSJUsgJibGEj+n913fvn0ZsjDc/ZKcnAw5OTl8/vyZZOJMnjwZgwYNImM/IyMDNjY2iIyM/GWA5x/7vf0DSv0PtE+fPsHDwwPDhw9nIMN1dXUYMmQISxCasoKCAuKAXLt2DX369GGlpBUVFUFbW5tnBQO6bdq0CWFhYVBSUkJAQAABK0aPHg1bW1ue33nz5g10dHTw4MEDFBQUQEhIiDi4169fh6amJgYMGMBwpK5evYp79+4xKte8fPmSgAZHjhyBvr4+Obzm5uaiV69eBKz48eMHqqursXTpUuzbt491X1OnToWMjAy2bdvG2ERKSkqgoaHBEB1/9+4dK+rY2NiIBQsWoHfv3ujduzehdlLfoRw9Dw8PFqDDbTNnzoS9vT0sLCywdu1a4gQ8fPgQYmJiCA4O5il8zH0/FGJPUVsHDx6MhoYG3L9/H8nJyRAWFoaJiQlcXV3JZsrrcBIcHAw7OzviRE+bNo1UpZg1axaSkpIYh2LuNi1cuJAIUpaWlpI+yc/PR+/evRkR82fPnrHasGDBAoiLi6Nv376EjkxdnwKmREVFGaVs6e2gt2XZsmUIDw/HmDFjGE4GL2Dqy5cvjKhnRUUF+vbtSyLTHR0dmDBhAiZOnEg2xVevXsHCwgIWFhbdOiP0e503bx6jkl59fT0BpugMkEuXLvF8NvPmzUNkZCQ8PDxImmBzczMBpujixHTBX24zMzODk5MT+Ts8PBxycnK4dOkS6urq8O3bN3h7eyMsLKzbzXb69OnQ1taGubk5hg0bRl6/fPkyIiMj4e7uzgAAuOcQ9az09PQQHBxMPmdjYwMzMzO8ePGCOFiUrgUdfOzs7MTWrVvB4XAwZMgQcDgcRlSODkzRS4931x6gC1jx8PBAbW0tOjo6MHv2bPTq1Qvx8fHIy8vDuHHjICwszNId+PTpE5ydnVmaFNzAlJKSEpKTk3mKOX/69ImhPUEZBbx4eHiQQ4acnFy3KU90mzJlCoSEhLBr1y4UFhZi0aJF8PDwgJiYGLy8vGBhYQEVFRUoKCiQohm8rLKyEoqKikRjr7S0FIKCggwwFehi3XE4HJSVleHx48eQlpZGeno6SzOFVx9VVVVBRUUFkZGRPJ1OExMTGBoaEpBnwYIFkJaWJhF67gDMpk2bGPvhq1evICUlRVJm9u3bR8DQ48ePw8XFBe/fv8fDhw/JHNu/fz/Rg/lTe/r0KSnRTWeVPXz4EMHBweDn54erqysyMzPx7ds34ujOmzePpBPX19dDQUEBKSkprHlI/U2lUpw9exZ6enrw8vIi8546jC9duhRxcXFQUlJi6Ez29D4oYKq0tBQLFiyAsLAwSe/pbtzOmzcPHA4HwcHB2LhxI9EJUldXh4WFBRITE1ki1EBXxSZra2sICQnB19eXjPPjx4/DzMwMSkpK6N+/P8zMzKCnp8fSDQO6xr28vDykpKRgYWGBlJQUsuft3r0bKioqGD9+PIsBRFVBTEpKYgDg9GsvWLAAFhYWWLduHWl3eXk5K23od/PZ1dUVN27cQGNjI9zd3eHs7IwjR47wXG95CenPmzcPoqKiDMBy79698PX1hYeHB+M5/7v+ysiRI1nVwy5cuAA5OTm8fv0aDx48wNSpU6GtrQ1xcXHo6uqyGLb0fkxPT4esrCzU1NRgampK/EEKmLK3t2ek8lFG388sLCwgJyfHU/uOAqYcHBx4asRQ19m3bx969+4NERERMhaoNlLAlL29PQOYooz7OVH3tG/fPgaLq62tDXPnzkWvXr1YAQ7u66xatQqDBg3C1KlTGQLoFDC1bt060q9U6hf3NTIyMiAiIgJTU1Pw8/Nj4sSJBPx4+/YtTE1NYWlpydqDeIE/Bw4cgKqqKmlLd8DUqVOneApW87KdO3fCysoKI0aMYABT/fv3h6ysLE8wCOjyn16/fk1Aq4qKCsTExLCAqVmzZmHkyJE8mdRA1/yvq6vDly9fsHz5cpiYmDDG9vnz5xEeHs4S1Aa61iYq3Zeb9V1SUgInJydGKh8v8/DwAIfDQXJyMivg9+bNG0ybNo2VncGrP6jsE8pGjBgBbW1tFBQUkHHx9u1bREZGYt++fTyfR1paGpSUlKCkpARpaWnis3/8+BGTJ0+GpKQkFBQUoKGhARMTE57nhtTUVEhLS8PW1hY6OjowMzMjIM6dO3cwdOhQuLq6soC29vZ2rFixAoMGDWKtFYmJiVi7di2+fPkCRUVFJCQkkGd45MgRHD58uFvJEOBflZrz8vKI70EH/B8+fAhjY+NuZTLq6+sxZswYBnMb6MoQGTRoENEwjIiIQGFh4S8lbv6xntk/oNR/sVEDu6SkBPv27cO6detIRHLPnj2klPidO3fw+vVrZGZmwtbWlqdeRGdnJ1JTU6Gmpobnz5+js7OTVJ6gDvVNTU2YPn06DA0NGRVquttUvnz5gocPHyIqKgp2dnbQ19cnGzJ14KZPzidPnmDYsGGQlJRkCMxRiwoFTEVFRXW7SABdmwklAsrHx8eiHNOBKV4OEWXr169nlHIHuqJT1EJTVlYGLS0t6OrqMpxR7gXn6tWr4HA4DCZRZ2cnw9ELDQ2FhYUFQxOL3q+bNm2ClJQUNmzYgAEDBsDKygqJiYnEwaVS+ezt7Rn0eLpOSlFREdTV1RkHny1btsDd3R1xcXHEcXj37h3ev3/PYEjRI7HUvZaVlcHV1RXLli0jbc3Pz4e7uztsbW0REBDAEJqk38+8efMgJiaGiRMnwtzcHNLS0sjPzyeOxM6dO9GvXz9SCpl+Der3jxw5AmlpacjJyWHx4sWsiNebN29ItSJu3SRucExUVJSIbzo7OzP0ExYvXgx+fn5MmzaNMV7a29u7LfE+aNAgchjy8/PD3r17CTClra1NnhH3WCkoKACHw4GUlBSp+kN9hgKm6OlilNE33rlz50JGRgYTJ06En58fBAQEiDPW0tKCffv2QV1dHZ6ent1eg7IHDx7AyMiIONtfvnzB0KFDISIiAjU1NRgbG8PMzIzBeqQ/5z179kBGRgbbt2/HjBkzSH4/ZVeuXMGAAQNgZGTEcK65QUOgi12mqqpKAI/q6mrY29tDVlYWLi4u8Pf3h6ioaLcsTqpqSv/+/clrVLup+3J1dcWSJUt4zmc61b+oqAiurq4kLaCxsZGkkDg7OyMgIIAnY+bBgwfQ0tLClStXWOsm/Z537twJVVVVIvpKt4cPH0JLSwsXL14k11i3bh0EBASwZs0aksp1+vRpLFmyBM7Ozr8NIgBdmmESEhKEqdTW1obXr19j+fLlSEhIgISEBGRlZXkCZZQdPHiQVC998eIF1NTUGI48FVCgKuG0tbVh+PDhrHn+48cP1NTU4PHjxwyBeWqMVldXM/qF2/G2tbUl0W0pKSmGhgZlN2/e5Fm57OXLlyR109LSEvHx8VBTU8OGDRuwb98+BAcHE7bvgwcP4O3tjcDAwF/uJb+zR48eYcCAATzFXSsqKpCQkAAtLS2oqqoiNTUV9+7dQ0lJCZSVlXH8+HEAXfted0BSZWUlPD09UV1dTbRbjIyM4OzszHP//lVFt18ZBbDJyspCQECAwYj41bhdtWoVfHx8EBISQlIlKACOV4pDXl4eqVa6f/9+pKSkQF1dnTDyqDSX8ePHIyUlhYwb+hpH7V93795FdXU1ZsyYAVtbWyQkJJB1Yc+ePeDn58fixYtZbfjrr78gICCAzMzMboEpKk0Z6P4g0tP5XFxcjKamJlKWvLviI3Qh/dGjRzMO8PRnvX37doSGhrL0Uq5cufK3/BXgX5W2gH+VXq+trYWLiws0NTUhKSmJhIQE7NixAx8/foS4uDjRAOPuI2q9v3LlCvbv34/hw4ejd+/e5AD47t07pKamQlNTk6zD9O9T7aCYoGpqaqioqGDtc6dPn0ZgYCC0tbV5zr/Ozk4cPXoUHA4HvXr1wqJFixiscaALmJowYQLU1dV/GVw8c+YMlJWVGay/t2/f4saNG2QMUSAtvW/p9zV37lyIiooiISEBqqqq8PDwYATeMjMz0adPHyxatIjBJuKucEyNKaBLj1FPTw8JCQlkjX779i3k5OQwYsSIbu+HssLCQoSFhcHKygpXr14F8C9gyt7eHlOnTuWZckcfj8ePH8f+/fsZ6X07duzgCUxlZGTwXBemTZsGFRUV6OnpwdfXl5xTKioqMHz4cDg5OTGAKXoWCb2fSktLISUlRXzKr1+/YtmyZSxg6uTJk0hPT2eBYoaGhjh27BiALsD/wYMHWL58OZmHpaWl0NfX58lQb21tRWtrK8aPH49x48ZBVVUVc+bMIfsg1WaqWiN3dga9LUuXLkVYWBicnZ2RlpZGgJegoCDo6Ohg7NixWLlyJdzc3ODu7s4z02Tjxo2QlpbGpUuX8OXLFxw6dAgRERFEX7izsxPV1dXYuHEjg01Hn2cfP35ETEwMKioq8PPnT9y4cQPW1tbQ1NQk59GSkhL4+/vzZJcXFxcTggU98EOdWVVUVJCcnEx+s7W1FdHR0cjIyCDrwPPnzzFt2jRMnTqVkc45cOBACAkJYdWqVYxzLdAFLpqYmLCK3ABdaYri4uKwtLRkVQtcvXo1+vXrRyQXDAwMWGvGP/b37B9Q6r/cDhw4ADExMdjb20NISIiUn+3s7CTRMA6HA319fUhKSjIObLxE8AIDA0may9OnTzFt2jQICgpCV1cXlpaWkJaWZqTv0BevEydOYNOmTdi0aRNjA6KETanIJofDYQkmU7Z7925wOBxGRKqtrY1sQNevX4eenh6jEiAvo4TdbWxsyGt0fY7c3FwiMtudc5eSkoLhw4cD6FrQtmzZAnNzc3h5eZGD8Y0bNzBw4ECeGyTVN42NjTh79izRDdi0aRN5n/rey5cvMXjwYNbiRv1GUlISI81p6dKlsLe3x5gxY8jhrLKyEr6+vozoN91OnDgBeXl5huPV1taG5cuXM9hkvO4B6BofHA4HiYmJBFTMzs6Gs7MzI+L3/ft3dHR0dEudffnyJRISEojzAnRVXZGXl8f27duJI7Vx40a4ubnx1Augfuf79++YN28elJWVkZeXx4o8/fjxg1XWlW4lJSVISEgglYs+f/6M2NhYODo6Mg4iubm5cHJyYrWhu8h0v379MHv2bGzatAn6+vrQ1tbGo0eP8PLlS9jY2LCqSlL24cMHpKenk2gnwKRi19fXIyUlBUOHDuW5udXU1CAtLY0B8OTk5ICPj4+wHltaWrBt2zZERETwPJAuWbIEe/fuJcBZVlYWgoODGWBEQUEB9u7diz179nSrYbB//35s2bKFMGs6Ojpw/fp1spZQdu7cOWRlZfGcQ0VFRbh79y6+f/+Ozs5OTJ48Gf3792f03/Lly5GRkYFp06ax9IjolpaWhiFDhoCfn58RXafAjM+fPyM0NBTe3t6sKl3U2I+PjycAx/Tp06GlpcU4wFNrTHelinft2gV+fv5f6to0NTWhrq4OR44c4Vn1Lz8/H3x8fIznX1tbS+bTvXv34OXlBVdXV8yePRtKSkos8Xjqu48fP2bsCXFxcejbty/27t3LWhefPn3KEC599eoVNm3ahI0bN5LfPnbsGMLCwvDq1SsoKyszKj1dvXoV2dnZjDna0tICV1dXRsrKqVOnMHr0aAgLC0NCQgK+vr6MQwN3n9G13OhVEF1cXMDhcLBs2TJWH2dkZMDAwIDlhNLvNTIyEuHh4Th8+DCOHDkCd3d3hIeHg8PhwM7Ojoybx48fdyuE+ifGLYRPt+bmZnz58gWpqalwcnKCgIAAcnNzIS0tDXNz89+CSCUlJRAWFiZzsaWlBWfOnIGxsTFcXV0ZxRmAf89xfvz4MUJDQ1kAWU/HrZWVFU/xfGp9+fz5Mzw9PRmHy0+fPmHDhg3Q1NTEzp07ebaLnu504MABhISEID4+nsVqtrGxwZgxY8jnCwsLWSA19Z38/HxwOBxkZGQwgCnqu2vXriUaSd1ZT/vFwsIC79+/x6dPn7rdPyijhPT79u3LAtTo84fXuGlqasLZs2dJOllP/RX6+v/XX3/B0tKS7Kv37t3DmjVrcObMGbKufP36FXZ2dizBaKBLkDovL4/B8qytrcWIESPQu3dvsr+9efMGK1eu/KU2y+3bt/Ho0SPY2dlBR0cH169fZ33+3LlzSE5O5skoouzdu3fYtWsXevXqhby8PFaK1KdPn7B06dJfBigpFmB9fT3u37+P7OxsqKurQ0tLCw4ODgSo37lzJ89qyaWlpRg1ahS5/9raWgwePBguLi4Mhsm4cePg4uLCc59Zs2YNhg0bhsGDBzPaSgFTY8aMIcDU58+fWffTXRD6ypUriIiIgLm5OQOYys7OhqamJqsQDr1vpkyZAhkZGSgqKsLIyIiRbrhjxw5YW1sjNjaWleJMn1cnT56EvLw8jh49ihUrVsDV1RXKyspk76NS+XR0dAhgBPyLcUpnplMB54aGBobW2rJly2BqaspIEeNOpX38+DF0dHRw5swZXLhwAQkJCTAzM4OKigpMTExI6mtlZeVvNZeALj9DRUWFAUwB/5Il6Y59k5mZCWlpaaSlpSE9PR2SkpJwd3cnrNOcnBwEBQXB0tISERER3WZFjBo1CnFxcYzXrly5Ag8PD5Y0B9C1z9PHzMaNG6GkpAQvLy/GfltWVgZra2toa2szKidy9yfdiouLERgYSJ5VY2MjPDw8ICEhQc4bra2tyMrKgrKyMmE4l5eXQ1ZWFv7+/nBycoKWlhbxh5ubmxEQEAABAQGEh4fjyJEj2Lp1K5KSkiAqKkrOmS0tLaSdHR0dOHv2LDw8PCAqKkpSi+l7+Pr16zF58mRGUOQfDal/3/4Bpf5LjNeide/ePSgoKGDz5s1obGxEe3s7Jk+eDEdHR8JsaGxsxPXr13HlyhWejvO5c+cYehrz5s2DqqoqOTh8+/YN5eXlWLRoEbZt28aKTlOWnp4ODQ0NmJubw87ODqampjwrL9TW1mLbtm2kwhX9Oo2Njbhz5w527dqF2NhYSElJkc+0traSCV1cXAwLCwsGI4gyKnd/xYoVGD9+PJycnBAREUGi2HSwLCMjA5KSkvj8+TOjf6nFeeLEibC1tUVaWhocHBwQERGBsWPHYuzYsTAzM2Ok6wFsfQfuQ3pDQwNmzJgBDofDiA5u27YN5eXlPBfjgoIC6OnpQU5ODidOnGBcb9myZXBwcMDYsWNZ5brz8/NhZmbG0Pe4dOkStLS0yMZO1/Hq27fvbwXwL1y4ACkpKXh6emLYsGEYO3Ysvn79ChUVFcbmxL3w0v+mIo56enpEuJ6yuLg4kh7EDWjR+7KhoYEFPs2YMQPKysqYP38+ea9///6MfqGDm0BXFNzGxgaGhoaMNNd3794hLi4OTk5ODMZUd5s/d4l3bmo5pUFEgUy8nE26USXl+fn5yTOnM94+fvzIUxvl8uXL4HA4UFZWZgkz5ubmgp+fnxzk6M4cvR2PHz+GkJAQZGRkMGbMGBw6dAgNDQ1QU1NjVCrjNm4R1/v370NDQ4OVKtfR0YHi4mLo6+vD2tqa53Uoq6urg5iYGGRkZJCeno67d++ipqYGqqqqPdI24mUdHR1Yv349+Pj4WGkf9fX1aG5uZjHugK50ZgEBARgYGGDixIlITEzE9+/f4ejoyEhJ7A5Apez69esQFBTkWSWUshUrVsDHxwcAeKazFRUVoU+fPgSk5hUl3bBhA6ytrdHR0cFixlKfp6rWLFu2jAFIx8bGQkhICHv37u22UllFRQXU1NRga2sLKSkpaGlp4dixY6iuroaAgAB69+7NSlGcMGECgoODWffk5+cHa2trPH36lBxghgwZgj179mDfvn2wt7cnkXHufuXWchMQEGAAU5QDSq+mOH36dAgKCv6SbQt0zYWAgAD4+vriyZMnRMA0ODiYgDv/J6KeHz58wNatW+Hm5oZ+/fpBQkKCJ7jGPRZnzJgBfX19Ami0traioKAA5ubmMDU17bF+VE+MFwjTk3G7ceNG2NjYMNLxKU0lyhobG6GpqYmcnBzG9b98+QJfX1+GHiH39YEugHnEiBFQVlaGo6Mj43ONjY1YuHAh7O3tWcGm7sAGCpjKzMxktBvoKsgwePDgX4JSf9IvfwJ+diekz81Cpf7/9u1bBtjV3NxMmMa/8ld4WXV1NYyNjeHt7c3ai5qbm1FbW4uQkBBS3Y5+rcrKSvTv3x8cDgcpKSmMNtbV1WHkyJHo27cvCgoKGNflVViD3u+dnZ2wsrJi6FsBXUxpXv4f0LX+cj/TjRs3olevXpg3bx7Zy0ePHs0ATLjbQjHPL126BEVFRQQEBEBaWhqxsbHYtGkTjh07Bi0tLUYa0MKFCxmBxV27dsHOzg4WFhYM4fLq6moMGTKEFOyg3y/AHPvV1dVYuHAhBAQEoKury2Lh7t69G0ZGRhg4cCAjzZTXYfro0aPYuXMntm/fTn7jxo0bLGDq69ev2LBhQ7cH8ufPn8PNzQ2VlZV4/vw5Vq9eDT09Pca+mp+fD1VVVeTl5THujbLt27dj/fr1JFgMdJ2PnJycGEGZkpISzJ49m9GWCxcuICIiAp6enoR1+vDhQ1haWpKxQD1nKpVPVlYWCxYsYAGGFCjh4uICbW1t8PPzIzk5GadPn8bnz59hZWXFAonp4+3EiRNYv3499uzZw9iTp0+fDjU1NeTk5KCkpATe3t4MzV/u/njw4AE0NDQYrLNXr17BwMCA+BZA13OlA2+8grdjx45FYGAgaw2bNWsWFBQUGAzh5cuXw8LCgqwx7e3tpDqmrKws8Seo3ysrK4OtrS2EhIQYazxdEuXKlSsoLi7Ghw8fcP/+fZibmyM8PJz42BcvXoSuri6UlJTg4+MDf39/yMrKEjCJktfIzMwEAFLhk7uieFZWFkxMTMDHxwdDQ0OEh4eT4MjJkycxbtw42NnZEVCvvb0dly9fJnIBVD9wFyfhvqd/7N+zf0Cp/wKjFr7q6moGwn/8+HFoamoywJmmpiYkJyfD2NiYZwlzyjo7O/H582eoqamBw+Fg0qRJRCA3JCQEHh4ePW7fsmXLoKCgQJz89evXg8PhQFdXlzhR9AXt3bt3cHBwwJYtW8jidOrUKSQnJ5PI2sOHDzFs2DBISUkxKsIdP34cX758+WVUmW6bN2+Gra0twsPDGYsepSHy8eNHxsaydu1abN++HS0tLairq8PQoUPh4OCAZcuWkejRgQMH4OzszPPASN1PYWEhhg4dioiICEyZMoW839TURICp9PR0TJgwAUJCQixQCehiQ125coWkPMbFxbE0S5YvXw4tLS0CQnZ2duLdu3cwNDRE79694e7uTpgdzc3NJDJOd27r6upgampK0sW4jQ4uZmRkQE5ODjdu3ICHhweCg4MRExMDMTExsqF3Z1Rp3sGDB4PD4WD79u2sjS4+Ph4cDodR1YT+fGbPng0PDw9ISkpi8uTJZLwAXYcudXV1IkAsKyvb7WHg8OHDOH36NAICAiAqKsqqevP+/XvEx8dDR0eHkcLQ3SGUV2S6s7OrEhvVvwcOHOB5Txs3biTC87t27UJzczM6Ozsxbtw4CAgIkEgyr3QvyubNm4f79+8jPT2dcYigf4ZKY6DmOff7lE2cOBGysrLYunUrtLS0kJeXR3RGuqscRbe8vDzMmDEDe/fuhZ6eHitNsKOjAzdu3IC4uDhGjhzZbTuampowcuRIyMvLY/HixdDQ0MChQ4eQl5cHYWFhworiBRZS/6+srMTZs2cZIFBLSwvWr18Pfn5+5OTk4OfPn5gxYwbs7e1ZzAH6GrpixQqoq6sjPz8fMTExpPywhoYGo09/ZXV1dZCVlUVoaCiDZUBve0pKCqZOndrteKutreV5DbqlpKSgf//++PbtG89rnDp1Cv369cPKlStZrDCgS3dCXFwc27ZtYzlZVHWvjIwMNDU1obCwkBy0gK5UYwEBASxcuBA1NTV4/vw5pk6dCgkJCQZ7hmpXcXExTExMoKioCBkZGWzZsoWRbhgWFgY/Pz9WG7vTcps0aRIDiHN3d4e6ujpKS0sxffp09OnT57dCu5Q9ffoUvr6+8PX1ZUXp//827uf47t073Lp1q9uy2dyA4rVr12Bra8tIZW5ra8Px48fh6OjY7Vj6T1lPx21UVBSZhydOnACHw4G3tzdh2DQ1NSEmJgaDBg1iXWfcuHGsaq/0fps0aRKio6NRXl6OtLQ0qKiosMBpqrT9qFGjfgm80K+7Y8cOCAgIYNy4cbh06RKeP39O9kpeWkX/br/01H4lpE+3Q4cOQVdXl6SDlZSUELHx3NzcHvkrQFdq2ezZswF0pe9RzHJKz6qzsxNbtmyBl5cXg0FGPa9p06Zh1KhRuHjxIgYMGABRUVGyZtCBqdDQUJIWySvlaNWqVRg1ahTc3d1x8uRJBsPE2toaOjo6WLZsGXx9faGqqsqTsTJv3jw4ODhAR0cHkZGRePnyJQMkpFLB7ezsGJpl3NeZPXs2hg8fTvzHQ4cOYfbs2Thw4ADx5z58+AAzMzPiz+zcuRMxMTGMcXzz5k24ublBVFSUEegBuphr0dHRMDAwIPqMFChA2YQJE0gVt/Xr10NGRgYZGRmsynybNm3CkCFDGPcQHR3NAHuTk5MhLi4OAwMDiIqKwtramrC3iouL0b9/f1hZWbF8Sm5gavPmzfDx8UF0dDTpv2/fvmHTpk3Q1dVFdHQ0+SylgeTk5ETSp4Guc5Genh5PVuy9e/fg4uLCCLTzasu1a9cQHh4Od3d3nD59Grdu3YK5uTlPxnNjYyMOHDjA8C2zs7NhaGjICByfPHmSpTXl7OxM2si9v6enp0NOTg5ubm6Qk5NDVFQUg0U4e/Zs6OnpQVtbG/b29owzEPfeUFFRAUVFRTLmqM9WVVWhb9++rPHD3R90W7ZsGaSkpHD58mXG7xw5cgR2dnYMH4kiRwD/Eib/+fMnzpw5Ay0tLZ7ZBrdv30ZcXBxrDlZWVpKiK3x8fAgMDERVVRXu3bsHZ2dnhISEEBD369evmDFjBlJTU7Fs2TICqD579gzCwsIsnTuq6rKzszOGDBlCgKampiZUV1fj58+f5Ly4efNmqKqqYuXKlTxlQK5cuQJLS0tYWVkRYKqn59N/7M/tH1Dqv8Rev34NaWlpGBgYkCjt+fPnoaSkRNJqqEWysbERvXv35lnFiNu2b98OMTExhIeHIz4+HuHh4Thw4ABcXFwYebfdWX19PWJjY4kg5okTJyAqKopZs2bB2toaBgYGZDOgb3L29vYErDl8+DAEBQUxa9YshuDs48ePER0dDQkJCezatQuZmZmQkJBgpZnRUfetW7di3759RM/l58+f2LJlC+zt7REWFoa3b98iJycHJiYmLNAuLS0NcnJy2LBhA2lzS0sLIxLQ0tKCoKAgREREdAtQHDlyBGJiYhg9ejTmz58PSUlJDBw4kHHNNWvWwNDQEO7u7jyrClHRV8r5y8rKgrm5OWbMmMGqLLZv3z7GhkLlU6urqyMxMRHOzs7EOairq4OysjLs7OywYsUKHD58GH5+frCwsOAZFS4pKUFwcDBxNIEuUIliI8yZMwdhYWHgcDiIiYlhVfSgjKpyRUUeAgMDIScnh8LCQlYEYe7cuTw3yOzsbMjKymLz5s04ceIEdHV14e/vzxDJXrt2LSZMmIDRo0czctvpbaEEZN+9e4cXL14gODgYrq6urPlSX1/fbVt4GT0yTU9NnD59OjQ0NFjOH9AlPC8rK4vs7GwMHz4cWlpaGD16NDo7u8R0J0yYwJNZQx97W7ZsgZCQEK5du4aOjg4kJCRASEiIVa4Y6HI4eeW2nzlzhjjEra2tsLS0RG5uLtEDCQ8PBx8fH4KCglgMSHrf7tu3D8rKyqisrMSPHz9w+PBhqKqqIjQ0lPWde/fu8ezbp0+fEofmzZs3hAF39epVaGpqIiYmBn379sWQIUN+CQwfPnwYampqMDAwgLq6OmxtbckzaG1txebNm8HhcGBqagoxMTECVFBtunXrFvz8/DBnzhwAXeNo3LhxBABetGgR3N3dweFwMHz48F+yIeh26NAh9OnTBzExMYwDa1NTEzIzM6GmptbtoY+ygwcPonfv3qxrNDQ08ASA6H1DidNTh/HGxkZUV1dj7dq1jJSoqKgoKCkpMQ7C3Wmo2djYQEdHB1+/fkVjYyM2b94MQUFB0v+GhoYoLS1lpXVT9v37d5SVlTFAd0rHJjo6GmlpaYyx8istN3Nzc+jr68PLy4sA5a6uriQtnLt65O+su8py/812584dqKurY/v27Yy028GDB8PY2Jjx2ba2tm5T2P/T9ifjtqOjA6tXr4a4uDhMTU3h5uZGxuLx48chLi6OlJQUwnJtbGyEi4sLo0AG3aqqqmBra0uYO58+fcLkyZNhb2/PYoH+/PmTwTShp/DQjT6GDx48CFNTU8jKysLCwgLm5uYMTcX/VL/8qXUnpE+1vby8HMrKypgzZw4OHz4Mc3NzGBkZ4ezZs2Rt5+Wv8LK8vDzIy8sTUPnly5ewsLCAt7c32V+KiooIc4auVVVYWAh9fX2STlxZWYmgoCCG6D7V5vfv33cLGFJg4OTJkzF27FhISEhg1qxZDOZPaGgoPDw84O/vzzNtadq0aVBQUMCaNWtw8+ZNyMrKIiAgALdu3SKfO3HiBKKiojB+/HgWuEZZWloa5OXlkZ+fz5OBS2mUBgYGwsnJicG2p/5/6tQpwoSkUjm9vb1ZgZDnz58jNzeX55769u1bjB49mqGtt2zZMigpKSE7O7tbBl5HRwe+f/+OmTNnQlJSEjNmzMDnz59hbW2N0tJSfPr0CR8/foSjoyNMTU3J2nrp0iV4eHgQParugk7UfmdnZ8d47/v379i8eTMMDAwQGBhIXv/x4weWL1/OCJS0tLSQqqIGBgYsMP7BgwfQ09NDWFgYqy30/1+9ehVhYWHw9fVFYmIitLW1MWXKFGRlZSE7OxvTp09HYmIiYw9ob2/HtGnTIC8vjzNnzrDS5IGuNaO6uhoBAQEwMzPjyZhZunQplJWVCYi1YsUK8PHxwc/Pj0FEKCkpQXFxMUMygT5uKRDt1atXEBISIsw5ap61trbCysqKJY5O74djx47h2LFjjIBvcHAw5OXlcfz4cVRVVeHTp0/w8vJCUFAQz2d77tw5hi4alS5uaGgINze3bs9O1DyqqKiAkJAQpkyZgtLSUqxYsQJqamoYNGgQgK5nRQFTdDYYt505cwYcDgepqalkH5w3bx769OmDqVOnIjs7G8rKyrC1tSV7IL1t+/fvh5CQEA4ePMh4ffTo0cQv7OjowOXLl2FjYwMbGxuems3/2H/O/gGl/kvs0qVL6NWrF2xsbBAWFoY9e/bg58+fUFZWZtBcga4Iqrm5ebfMl5KSEpSVlZGFberUqZgyZQqKioowevRoiIqKQklJCe7u7jwF3rjt9OnTqK2tRWlpKdTV1UlqzapVq8DhcCAuLs5IL7hw4QJB8auqqqCnp8ezQhrQtdEmJiZCXl4epqamrJQvaqE4dOgQFBUVYWlpCQcHBxgZGZH7b25uRn5+PiwsLCAvLw8VFRUGfRvoEqaTlZVliBO3tLQQtPzHjx/YsmULAgMDu60uAXQ5Dbq6uqR6ClUViY+PD25ubgwq+KdPn3hGPo8dO4bNmzezypCmpaXB0tKSJzBFOTHUhvfo0SMYGxsjLS0NAwYMgKOjI3FIPnz4gKCgIJiamsLY2BghISFkk6cDdffu3cOZM2eQmpoKLS0teHl54fbt29iwYQNLZH3u3Lk8KzoCXc7mrFmzWCXWfX19oaCgwNLsoIz+2tmzZ6Gvr09Yczdu3ICAgAD09PTg6urKiFBxl2KmW2VlJZYtW8Zw6OgVhboDcnsKTPGqQCUoKMjTkT9//jy0tLTIWDxy5AgEBQUZZXF//PhBBLh52fnz5zFx4kSWjsqoUaMgLCzME5gC2IK/Li4uMDQ0JHpyx44dw7Bhw/D8+XN8+/YN27Ztg6WlJVxdXbt1KM6dO4eUlBRGOnBzczMOHToETU1NhIeHMz5PT1el+vfFixfw8PCAnZ0dccb279+PAQMGkPSSvLw8KCgoQFlZmQUsU207f/48xMXFsWnTJnR0dODmzZtEC4ieqnnv3j1s27YNL1++ZDgTZWVlePLkCRITE+Ho6Ag7Ozvcv38fS5YswaBBg8h6VlZWhkWLFpFIW0+MilTz8/NDX18fsbGxGDt2LEJDQxm0819Ze3s74xpxcXEYM2YMcR5/dY2Ojg4EBwdj0qRJeP78OSZOnAgPDw+oqKhAWloa48aNI5/lji7z0lCbO3cu0e8LDQ1FbGwsDh06hAsXLuDkyZO4d+8e3r59i7q6OkRFRTGc3V8xUdra2pCdnQ1FRUWWVlhPtNwoMJIKYvTv379bEfzfWXcH+/82o8b/nTt3MHHiRMJ8yc7ORkdHB54/fw5HR0eyP/3/nX74p+O2pKQEcnJymDp1KgICAuDq6kr2zD179kBeXh4ODg5wc3ODo6MjjIyMeB765s6di6CgIPTv358BwL1//x6TJk2Cvb09SQ2iGzfoNG7cOFY6F/0zNTU1uHPnDsrKynjKF/yn+uVPrTsh/dLSUuzYsYOwDSlzdXUlwBTVnx8/fvwtU+v+/ftwdnZmpPu9fPkSlpaW8Pb27pY5s2vXLiQnJ7MAxfLycoSEhEBFRYWnf8G9fuTn50NdXZ2AIyUlJeBwOJCVlUVGRgYjPZEuv0AfM4WFhTA2Nibg5aVLlyAkJARpaWmYmpri1q1bxMegM2m4x92pU6egoKDAAME/fPiAyspK4lfPmDED3t7esLa2ZqWKASCp7qNGjSKA/d27d+Hp6ckCLHj1FI6deAABAABJREFUK9AVtJKUlIS1tTVqamoY71FgSE5Ozi91yj59+oRly5ZBQkICYWFhiIyMxM+fP0n/t7a2wszMDL6+vuQ73CmevNb6169fY+7cuRAXFydFCij7/v07Vq5cicGDB/P8bl5eHgmct7a24sKFCzA1NYWtrS2L4fTixQvGfXe371y8eBERERHQ1NSEmJgYJkyYQFLCKI1B+vN58uQJDAwMCHPr69evePbsGdavX4+ioiJ0dnZi27ZtsLCwgLu7O3nG3CmiY8eOJamHhw4dIv1hZGQEJycnlnQHwE4RXb58OSZMmEAyZ3Jzc6GiooJ9+/aRz7S0tMDExKTb81ZaWhokJCSgoqICXV1dpKamkvcGDBgAZWVlyMjIwMzMDObm5owCN3T78uULkpKS0KdPH+L3Nzc34/Tp0zA2Noanp2e3z6Cqqgri4uIs3eGUlBRIS0sT3+vatWtwdnZGZGQkI0ujs7MT79+/x507d/DmzRucPXsWSkpKyMrKQmpqKqSkpBjyGkVFReBwOIRYQdmHDx/g6enJShMPCgqCqKgoevXqhZkzZwLoGk9XrlyBmpoaq2jLP/aftX9Aqf8ii4uLg7m5Ofr37w9XV1ecOnUKN27cgLS0NAYPHoyysjI8e/YM2dnZUFBQ4LnJdHZ2wtzcHA4ODkhPT0dbWxtOnTqFqKgo4vhv3boVLi4ukJSUZIBS3Klj3AvR2rVrERgYSByXvXv3YtSoUUhJSWFFuamFs7KykqRWdHddoOsQ0p2Td/nyZUhLS5OFtrCwEH369IGYmBjZtFtbW1FdXY3Tp0/zFPSeOHEixo8fD6ALCKOqfkRFRWHr1q1oaWlBQkICYmNjeVaXoKftUeXPa2troaGhgcTERJSVlUFCQgJRUVE8GTP0++zduzc4HA5hZNB/Jz09HTY2NpgyZQrjEM19OP/8+TMSEhKwYcMG3L59G8HBwXB0dCQ0+ra2Nnz69Alv3rwhbX/16hUCAwNx9OhRov304MEDtLW14enTp3BwcIC7uztiYmJgaGjI04nntmvXrkFVVRViYmJEA4IeyfLz84OysjJOnDjBKmNLt8rKSpL+d+bMGUhISGD79u149OgRxMTE4O3t/VtNLKoKopCQEAGlqHH5/PlzBAUFwcfHhwUG/ql1V4GKexPeuXMniRAeOHAAIiIiZAx///4dFy9eRGdnJ5qamnhu4FQ+u7S0NNmU6fMsPj4e4uLiLMrxlStXyME6NTUV+/fvR2VlJdavXw9BQUEMGTIES5cuxeDBg0laY3t7O759+8YzVaKzs6uQgbi4ODgcDkv8srm5GYcPH4a2tjacnZ0Z771+/ZqkHx07dgynTp3CyZMnkZaWRgSEt27dilGjRhHB3aamJrx48YKkuhw7dowBFDQ0NGDKlCmE3ffq1SuoqalhxIgRMDc3h5mZGYuJVFdXh7CwMBw/fhwHDhwgrL7m5mbcu3cP/v7+MDAwQHZ2NqSkpDB27FjG/f8du3XrFgYMGABzc3O4uLggPT2dAZj1xG7evInIyEiYmZnB2dkZGRkZv6yOB3Q9u7S0NNjY2ICPjw8DBgxAfn4+Pn36hClTpnRbuIEybg01GRkZHDhwADU1NTh8+DBmzZoFGRkZkkpLWVVVFRwcHBAUFPTbVLhNmzZhzJgxkJGR6fZA3lMtN+7U3L9rv6qQ93/a6PqAdCsuLsbSpUshKysLJycnJCYmwsfHhyHU+3/Cfjdu6XtednY2goKCsGPHDtja2sLNzY3sfdeuXcOaNWswduxYzJ8/n+feDPwrvU5VVZWwQqg17MOHD5gyZQo0NTUZYArwr36tqamBsrIyg0n4/4X9nfncU+NOKWlvb4e6ujo4HA4GDhzIeK+jowOurq4wMzPDsWPHWCxQ+prHnd4bFxcHAwMDxms1NTVQUlJiVY2lrkUVJfDy8mLtdeXl5QgPDwc/Pz/Lp+UFSq1cuRJAl+aRmJgYdu3ahaVLl4KPjw85OTmstZ97rbtz5w4Bbc+ePQspKSls27YNX79+haysLPz8/HD16tVuGTeU7d+/Hy4uLmhqakJlZSVmzJgBDQ0NaGtrIzw8HN++fcPp06cxc+bMbsdtR0cH5s6dC2dnZyQkJDCAKS8vLwQGBhLh7O7s9On/h72vDqtqW79+loqg0t3d3d1IIwaIHagYWJiIfRDswu5CRCREBSxsVGxFPXYhCIIiLT2+P3jWvGvtQM+5nnu9v4/xl272Xnutudae853jHe94c+Dm5gYxMTGi1mLGYBs3bkTXrl1/WBlBE1NKSkowNDQkr9OJW9pzlJM85Oyy9/btW7x7946oDisqKhAXFwcDAwPi+0Ojvr6epzdWU1MTpk2bBoqiSNzX1NSE3NxcWFhYwN7enqcfIqcv4eHDh5GQkMD6XV++fBkhISHw9PTkq4yln5n79+9DVlYW9+7dw9WrVxEZGQkTExNISUnBysoKubm5aGpq4tsQ5uTJkyguLsbdu3dRVlaGgoICaGpqEr+jxMREiIiIwMHBgcubjYk5c+ZAXl4eO3fuJMnily9fYvLkyRAXF8f06dMRHx+P3r17w9jYmEst39bWhqKiIri7u6OgoABPnz7F5s2bISMjQ/ZFQPs9Tk9PR3p6OrkefqVqFRUViIqKQteuXVnE1JkzZyAtLc06LhOHDh2CrKws5syZw1I3Hjx4EBoaGnjz5g15Fq5fvw5jY2MMHTqUPIdPnz6Fk5MTvL290b9/f/JZOh7ft28f6/vu378PHR0drvF9/vw5pKWlWYndU6dOwcHBASUlJdi/fz+6d+9OFOd018ZOM/N/Fp2k1H8BnAstvehnZ2dj9OjROHv2LAYMGABXV1ccPnwYd+7cgZaWFhQVFaGhoQENDQ2SnWF2cUlMTERsbCwqKyuxdu1a2Nvbk8yPp6cn+vTpQ77z9evXLCnq1atX4e7u3uHEOH/+fEhLS6OtrQ21tbXo27cvKwPHK4N55swZCAkJkUCROcHdu3eP1SaZ1zjV19dj1qxZmDdvHoD2zaWqqiqGDBlCWtdzZuc4g4nW1lYMGzYMJiYmWLFiBZycnNCnTx9EREQgLCyMyE3pDmAAt6k5HSQ3NTXh0aNHaGtrQ0hICIYNG4ampibU19fDwcEBFEUhMDCQ78TV0tKC8+fPEy8eZotTGhMnTkR4eDj57qNHj0JfXx8JCQksL5a9e/dCRkYGZWVluH37NmkNy1QqMMeyoKAAYWFhMDQ0hJCQEFnsmee6du1ajBw5krSMZnYs4YXi4mIsXboUkpKSGDt2LHmdGTBYWVmxyrseP35MiM0FCxbgzJkzaG5uxpcvX1BXVwcfHx8sW7aMXL+9vT3k5eURHR3d4blUVVVh1apVEBQUJIQFs6PQmzdvYGdnxzNw/qvg14EKAFHjpaamon///jh16hSEhYVZ5t0nT57E9OnTWVl5zt8B7X0iJyeH/v37k3mCeb9CQkLQu3dv8v+ioiJStjBs2DB06dKF+A4A7Zm/4OBg9OvXD9LS0hAWFuYiEPj9Hu/evUsMzDmDuYaGBiQlJSE0NJR8vrKyEkFBQQgJCcGWLVtAURTLdysrKws2NjYIDQ2Furo6dHR0WEFKW1sbaac+ZMgQVkY6MzMTjx8/JqUGtKdAdnY2MdpnHuvdu3fw9vaGqakpBAUFWWo1GqtXr8bIkSOhoqICiqK4lH9/B78igOnoGEzlzMGDB5GQkEA2DQUFBcSzgn7f6NGjMXLkyB+actIeanQHU058+fIFqampXCQbP48b5pz87NkzBAcHY/z48T9UoP3Iy83MzIw8U79CFfQ7e0VkZWXBw8MDffr0weLFi1lrRlVVFRYtWoQBAwaAoih0796dp5fYfxK8ntvr169zKZFOnz4NPz8/vHz5EufOnYOlpSXc3d35qnb4PbvHjx+HgIAApk+fzpXhLy0tRUJCAs9zys3NJcTXz5bo/jv4T25qqqurYWVlBVVVVdy+fZtL2WJqagpHR0eWuoz5nsOHD2PatGms3/nXr1+hp6dHfo/09ZSWlvJsVkAfc+jQoVBQUMC+ffu4CIU7d+4gOjqaNTbMJOm+ffvw+fNnFBUV4dOnTygqKoKlpSVpVPLlyxdIS0tDUFCQRUAwlZOrV69GRkYGWltbUVxcjPr6evTu3ZsoJaqrq2FnZ0fKtTnPnxP0WtO/f3/IyspixIgR2L17N/bv3w81NTUuXzt+z1Zrayvxt2ISU/fv34epqSnLs5TX2NIqIiMjI5iYmBAVEZNMPHr0KNdzx4sMKi0txYYNGyAgIMAVb509exYaGhpcxCHznBYuXAg9PT2oqKhAUVERu3btQl1dHSorKxEXFwdDQ0OS1OUcA05UVlZi4cKF6NKlC7E0oYkpa2traGpqcs3XnN6NUlJSMDU1hZqaGtzd3cnfmObntKcoXcbLOcb29vZQVFREjx49MHXqVJw6dQrV1dXQ0tLiMtNm3uPFixejV69eePfuHbkXmzdvZpUp79u3D/7+/pg5cybfuCsnJwfKyso8CbTCwkLs3LkTJiYm8Pb2JvsRznP59u0bHj58iLCwMJLYoA3qOyKQmMe4ePEiTp8+zVJ1VVVVYfr06VzE1M2bNzuc5zZv3gwLCwtERkaiqqoKlZWVkJSUxKJFi7jem5+fT565J0+eQFxcHPPnz8eHDx9Ya0FaWhrk5eUxc+ZMFjFN+4Fxltfm5eVBTEyM1RCloqKCte5s2rQJFEVxxcidxNQ/h05S6j8MeuIpLCzk2vSUlZVBX18fW7ZswefPnzFgwAC4ubkhJycHTU1NuHv3Lq5cuULUL3S7bTc3N2JyTGeAmpub8fHjR4SFhcHU1BRjxoyBoKAgX1nn8+fP4ebm1mGW++XLlzA0NISYmBgMDQ1hYGDAU03Eeb3m5ubw9vbmCianTJmCOXPmcGXiOCXXDx48wNWrV1FdXQ0bGxtEREQAaF8kafKEltcyJ/bm5mYSmNMbZAsLC6xZs4YEK0eOHIGTkxPLS4LTWJk2zGaWBNbU1MDOzo61uaVNUGllCL9FpqGhAbm5uZCRkUFISAh5nbOTDND+nJibm4OiKMjIyGD06NEICQnBhw8f8P37d0yaNIkors6dO4eQkBDo6uryLWOhvay0tbVZzx/zHrx//x6xsbGQlpZmmezyu56SkhIsW7YM6urqrEwYM/Cks2mvXr0CRVFYuXIlIiMj0atXL5bHRmVlJUxMTLBlyxYA7cTM6NGjcfLkyR9KxYH2RYX+LdCKKKZ/Q3Fx8S/rQkXfr2PHjpHW1lFRUfDx8SFG+mJiYqxzAdrHxc/PDyNHjuQZGDJBG+dbWFhg1qxZPIkpzs/m5uZCVVUV3bp1I8Fca2srOV+aUAgNDQVFUZgyZQrr8x2Nz82bN6GlpYWBAwdyldry6vh3+PBhGBoaokuXLoTcYMrSnz9/jo0bN5JM+ogRI7i+/8iRI7C3t8fw4cO5OqplZ2fD1taWqA3Onz+P4OBgeHl5EVKKHuOUlBRSEspUlzED28ePH2PdunWQkZHhazD9V/CjbPuvOEZaWhokJSXRt29fWFhYwMLCgivwLy4u/sveNfy6e/1o496R+XJrayumTp0KDw8PLnLiZ87jZ73c/q+BLmeePXs2QkJCYG1tjcDAQNa9aGlpIR6LPzLf/k+A87nNysoCRVHQ0NDAmjVrWL9BX19f0pb85MmTsLW1hZeXFxcxxZwbvnz5wmU/kJycjG7dumHevHk8vfUA7g3FxIkTQVEUDA0Nucrm/wn8ijmho+NWVlbi+/fvpAymsrISmpqasLGx4YoLWltb+TZkOHz4MPz8/ODv74+ePXti6dKlxN9l0qRJGDRoEBlLpokxUzHC6UEZFBQEMzMzHD16lG8XK7rrlZSUFN6/f4+oqCjIysqyFIwPHjyAgYEBKfd/8eIFpk6dir1797JKxSmKImU9oqKiLBK8rKwMRkZGJDn3/ft3TJw4scNSsBcvXuDevXskrjx9+jRmzZqF5ORkkuT98uULzMzMWAle5jFOnjyJtWvXIikpiXiStbS0EGJqwoQJhJh68eIFT/Xyixcv8P79ezImNDFlYWEBGxsboirhJP94Gb5XVFSgrq6O3PvPnz9j3bp16N69O2bMmIEnT57gzz//hJ+fH5ycnPjGBytXroSUlBQyMzNx8eJFxMTEQFRUFEuXLkVbW3uDnvj4eEhJSbG66DGPxzSgBtpJj3nz5nERU1lZWRg9ejRfcuDLly/o378/Hj9+jG/fviE3Nxfa2tqsjsCXLl2Cq6srpkyZwjqH7OxsJCcnY9u2bSR2zMjIwLVr11jvc3FxIdfB+Tt+//49/vjjD6LCof++atUqWFlZ4c6dO2hqakJwcDBR/nGOBY2EhARW0wCAew7jLGfkVKIaGxvDyckJZmZmrGeiqqoKu3btgry8PEaMGME6BvOaYmJioK6uDkNDQ0hKSmLMmDEkUV9VVYWoqCgICgpyVTNwnifzGjZs2AArKyuMGjUKcnJyrG6+vObFr1+/wtnZmavrL/NaExMToaSkhGnTpqG4uBixsbEQFBTkuR96+/YtunbtSkr0eH3vxYsX4enp+ZcV7p34++gkpf4LKCwshJSUFCiKQkBAAFJSUgize/LkSbi4uKCsrAx//vknBgwYAA8PD56ZfaA94NDW1iYti3lh9+7dRP3i6+vLs+MEwH8zwZwoX758iQkTJuDgwYNkMmBmx65du4bFixdj586dJGOfkZEBMzMzuLm54c8//8SlS5cQHR0NcXFxrm5N9HGOHz8OPT09VtnalStXYGtrSzabDx8+RHBwMGJiYvD8+XMus2vaq2DmzJlk8WcGuU1NTQgICMDAgQM7NDXv2bMnlixZwhqThoYGaGhokLLK2bNnQ1VVlQTJzHM5cuQIli9fjvnz55O/t7W1ITc3F1JSUggNDSXv5ST52tracOjQIYSGhsLGxgaJiYkYM2YMjIyMMGrUKDg6OrIUcDk5OaS9OhP0/8+ePYudO3di5MiRcHR0ZHkVcZKGzPJB5vWcO3cOBw4cwPHjx0nwVFRUhGXLlsHAwIC1IeZljH748GF0794dPXv25GplXVJSAmdnZwwaNAhr166Fr68vbG1tWf5EzHM5e/YsMjIyWBL32tpa0lGI2aXuZ0itv4qWlhZs2LABFEXB09MTIiIiLN+yM2fOEFP806dPIycnB97e3jAxMWH9fpjv37ZtG06ePEkWwpqaGixcuBB2dnaYPXs2GVPO66H/f+/ePdjZ2cHe3h59+vQhgTvTdJbGpk2b+HYV2rNnD+bOnYvx48cjPz+fZNjy8vKgpaWFsLAwLmKKBv17evPmDbS0tKClpYXhw4cT8oh5H5uamlBaWooRI0aQOWP//v2s+Sw5ORnW1tZcxFRCQgIkJSVZyrtJkybxvKazZ8/iwIED6Nu3L9zd3VkBFCfR8p8yhv53UVBQAEVFRRIc379/H0JCQqzf4JkzZzBw4EAYGBj8Zc+ln+3u9TOfa2xsxOTJk9G1a9d/6zx+5OX2fw1PnjzBwYMHiTKloaEBKSkpsLCwgL+/P0+vmt8Nzc3N2LFjB9TV1SEuLo7Y2Fioqqpi+PDhyMnJwaVLl9C/f3+8f/8ezc3NyMzMhIaGBkvVypyb4uPjYWNjAy0tLfj4+ODly5fk+o8cOQIBAQHExMT8lPKJNjPu0qULl3ff/wro+TYrKwsBAQGwsLBAQEAA2chXVVVBS0sL1tbWrPWJCc74ycDAAA8fPkRTUxN27twJLy8vqKqqYtasWaQDM7NBB/Pza9euxeDBg2FmZob9+/cT64iWlhYEBgbC3NwcKSkpPEuwgPa5wt/fH9LS0hARESENVOjrvHLlCqSkpLBlyxZcu3YNQUFBLE9Den05ceIEBAQEICoqSuJG+jwbGxuhr68PT09P7NixA15eXrCwsGDFGsy4cMGCBTAwMICamhq0tbWxYMECluVEc3Mzampq4O/vD2dnZ3Ic5jHmzp0LZWVlODo6wtnZGc7OzqQkuaWlBStXroSzszPCwsJY8Rfzt7106VKYmJhAS0sLRkZGpLS/ubkZFy5cgJWVFezt7fmuYcz7tGbNGri6usLR0RFhYWEktigrK8O6devQq1cvCAkJYfr06RgwYABfr9Xv37/D09OTJOdoJCQkQFBQkCjui4qKcODAAZ7k2Pr16+Hj4wMnJyfW7762thbz5s1D165dear7OWPdrVu3wsDAAH369CHkYWtrK/Ly8qCtrQ0bGxvy3nv37rHOYc6cOVBRUYGnpydUVFRgbm7OMo+vra3Fhw8fEBAQwNfUnO4oKi8vTzoW0qA9xPT09KChoQEjIyO+vk30/zds2ABTU1OeTUKSk5O5TOyZ47F7924oKSlhzZo1mDFjBnr16sWqaADa54YNGzYgICCAZ1y8YsUKyMnJEZP2NWvWgKIoDBo0iIxvVVUVRo8eDRcXF67P87o2Ghs2bIC6ujpMTExIvMvvvU+fPoWWlhauXLnCdZ7MvePhw4ehqqoKfX199OrViygWeR138uTJ6NatG0stR6OhoQFBQUEYPHjwf9yb8f9ndJJS/wW8f/8e1tbWcHBwgKWlJcaNGwc1NTXs3LkTKSkpCAoKIsqfp0+fonfv3ujTpw9XZ5iGhgZUVVVBX18fKioqCAwMZJVuMQmB4uJiJCYm/rBcoqNORKWlpfD19WWZVTInwKysLAgICMDT0xO9evWCv78/WTBzc3Ph4OAAUVFRaGlpwczMjLUxYU4qR48ehYCAAFcJDS2XvnHjBoD2ckK6LToT8+fPh5ycHHbs2IFTp05BRkYG3t7exOequroau3fvRmBgIIyNjVmLAvN6ioqKYGBgQCTinNd86dIlSEpKQl1dHSoqKjw3SNHR0VBRUYGPjw/c3NwgIyPD2tzl5uZCTk4OHh4eXJ9lqmiSkpLg6emJoKAgtLS04MaNG1i5ciWEhIQgLCzMUy3ADKq+fv2K+vp6sog+efIEgwcPhqOjI5mQgX+Z2jO/nzkhR0dHQ0tLixiQOzs7E7Pkjx8/Ii4uDsbGxlzqG/o6gPb7SPtqrVy5kis7nZubC1dXV1haWnJ1z2Gey7x586CmpgZTU1PIy8ujX79+JDtcX1+PpUuXolu3br/Mc6Yj0LJ/WmpPX2tzczNycnKgra0NVVVVWFtbo3///jw3knPnzoWamhrMzMzg6OgILy8vEgjQxJSjoyMiIiJ4qpI4/3/27Fn07t0b/v7+5DdDg9ObhjO4io6OhoyMDCZMmAAnJydYWVlh3bp1ZA66fv06dHV14eXl1eGc8u3bN7x69QqJiYkk2KaJKebmgHnuNTU1CA8Ph5WVFekCCbCJKZoMq6iogJqaGlRUVODq6goRERFCQDM7OdXW1pIgn+7I6ObmxjK+z8jI4Nmh5XdGWloa7O3tAbRfl5qaGqs98suXL9Ha2oqMjIy/rSj6uybgzLXk0qVLmDt3Lnr06PG3iSR+Xm7/l/HhwwdYWVlBQkKCeO4B7Wt/amoqLCwsEBQU9FuXHdKoqKjAvn37oKGhgQkTJqC0tBQTJkyAj48PJCUl0aVLF5JEaG5uxtWrV/l2aVVQUMDu3btRUFAAFRUVuLu749q1a+T9ycnJoCiKSxnO/F1zzpuTJk1Cz549Waa6/0s4deoUKbfNzMwkCjB6rqyuriYt52mShxeePn2KUaNGcZlsFxYW4ty5czAyMoKnpycoisKECRO4Ph8TEwMZGRksX74c8+bNg5aWFiZMmEB+9y0tLQgODoaCggLxwOSFBQsWkM09ZyMEoN0vUVJSknR347W5z8nJAUVR6NatG8tqgn7v69evYWRkBCsrK/j4+PAlXVavXk26CQPtptBycnJkbBsbG7F06VK4ubmxTM05japVVVXJWrxmzRp0794d+vr6ZKxbWlowf/58jB8/nidBsHjxYsjKyiI7OxsFBQXo378/KIoiMVxzczMuXrwIFRUVLvKBEzExMZCTk8PWrVuRlJQEdXV12NvbE/KjrKyMlC8x557m5mau9bGyspJYTABs5X1YWBi8vb35Jkrpc1FQUEB8fDwSExNJ91o6nqutrcX8+fNBURTLV5ATLS0tOHz4MIyMjKCmpsb6W2trK65fvw49PT2ef9u7dy/k5eVJ/JCWlsb6vra2NiQmJsLGxgaurq58OzKWlJRg6tSp6NKlCw4dOkTeQ4/ZrVu3sHfvXmzevJlvB2kmsrOzSVkqM06rq6uDv78/3+qXs2fPYuPGjYTIo5vSCAsLs2IEoH18eTUFoJuX0PuwzMxMiIuLY968eZCSksKgQYPI88LPG5U+7uXLlzFt2jREREQQnyYA2L59OywsLDB16tQO1elJSUno1q1bh9UFdXV1KCoqQlZWFtTV1QkBz1lBQ+PRo0dwdXWFkJAQduzYgerqatTU1OD69evw9vZmNdb4VYnsTnSMTlLqv4SXL1+Sbg8ZGRk4fvw43N3d0a9fP9JFig40nz9/zrelKwBSKkTXFTOZ/b97bpxZ7tLSUri4uEBTU5O1+NMTRFFRESZMmICdO3cCaM/YBwcHw93dnUUs5efn4+3bt1wZJvo4x44dIxlLT09PbNiwgbyvpKQEAwcORK9eveDg4ABhYWGurN+TJ09gaGhIMhS3bt1C9+7dWYqZqqoqjBkzBoMHDyYTTmxsLFcpwKNHj6Curs5qV8y5GH/58gX37t3j2Sp227ZtUFJSIl44tLm4rKwsywcrKysL/v7+HU7obW1tSE5Ohr29PYKCgshC8OeffxL5Pa/sAdCuvqNb+urq6uLQoUNobGzEkydPMHToUDg5OWHVqlVYunQpKIri+6ytW7cOCgoKZGO6cuVKUBQFAwMD8pmioiLMnTsXQ4cO7XDxaGtrw/79+0FRFGlDzImqqiqeCyXQHiTKy8sT1cy2bdtAURT8/PzIfayvr8fMmTPh5OT0y0kGzkVu4cKFmDlzJrp06YKVK1eSv9EBS1VVFWkAwOua6C45tKpp2bJl6N69O6sMoKamhizqvMY2LS0NW7duRWxsLHk+zp49Cx8fHwQFBZHfcr9+/UgGnRd27doFNTU1sok4c+YMKIoinmw0uXPx4kWEhYXxNLB/+vQp8vLyWJ5kiYmJcHFxwZAhQwgxtWbNGlY7Xvp3VFJSgqioKNjZ2bEM95nEFF1OW1hYiKioKCxatIgorZjPvoODA8zNzaGhoUHKKN+/f4+goCB4enoiLi4OixYtAkVRrFKW3w2FhYXYs2cPdu3aRcrYTpw4gb59+6KwsBDKysoYP348eeauXr2KmJgYnr+tv4q/awJOE0kSEhLo3r07yxfs76AjL7f/i6iqqsKaNWugpaUFPz8/1t8aGxuRnp4ODQ0Nltr2d0ZlZSX27NnD8g9paGjAwoUL4eHhwZOwZG76Ll++DFNTU7K+X7hwAcLCwlBUVISOjg6uXbtG5tXz58/ztBfIzc3F+PHj0bdvX2zYsIGl1pkwYQJ69uzJsxvW74z6+nr079+frD3FxcVQV1cnpBE9ht++fYOFhQXLm5KJo0ePQlFREerq6oQ84dx019XVITMzEwsXLuRal1NSUqClpUXImps3b4KiKGhpaWH06NHE37ClpQWzZ89mHZtzjS4sLMS9e/cQGBgIJSUlQkIzv/PVq1e4f/8+zwQHjQ8fPiA1NZWUv/JCdXU1a11meovW1tayNv/Z2dkQFRUl6lQ6Hs7JySHNhTjP5fv37xg6dCix1zh16hRERUURHR2NgIAA6OrqEvKDmXxjrq23bt1ieYaeOnUK4uLi8PT0RJcuXUiChbb66Mj3Jjs7G6ampiTeOHnyJERERCAjIwN9fX0SnxcXF+Po0aN81dTM9XLEiBHQ19cniSv6+mmVFT9kZmZCT0+PxCdnzpxBjx490LNnT/j4+BBiqrq6Gtu3b/+hZUhNTQ3S0tIgJyfH8jKlz52OWzhVlNHR0YiKigLQrrYUExMjPqD19fWoq6tDWVkZy9ScXzKgqqoKo0aNgpCQEJmr+Pmtcb6emJiIjRs3srzR5syZg27dumHVqlW4dOkS7ty5Ax8fH1hYWPBUaxUWFhJrE+Yeqrm5Genp6RAREeHZDIOzIoIei4qKCuTn50NVVZVYa9D7BW9vb1aSk1e8n5GRAVFRUYwdOxaLFi0iogV67k1ISICNjQ1Gjx7Nt0vk9evXISQkxFJnciIhIQHe3t4A/lUVwxzbdevWYfjw4QgMDMThw4dRWVmJZ8+eES9GNTU1SElJkW6i/wsq5P9r6CSl/ot4/vw5/P394ePjgxcvXqC2thY3b95EUFAQ2TTyk3Teu3cPe/bswfbt24ni6PXr1zA1NYWfnx/JQMXExPBUrfwINDFFq528vb1hYGDA80d669YthIWFwdnZmZWBe/ToESmVYSoSaHC+duzYMVAURbqEBAYGEr8kGk+fPsX27dsRGxvL1WWF/ruFhQWAdqNpYWFhVsezkydPoqWlhVVD/+DBA4SEhHB1FXn8+DHk5OSIag3414Sbk5PToWqgsrISMTExJFNy4sQJiIiIYOvWrQgLC4O8vDwuXbrE9bkfEVNHjx6Fk5MTfH19WaWA/EiX06dPo2fPnli9ejVev36NUaNGoUePHmShLCgowKRJk2BoaAhjY2O+6oPi4mIMGDCAtFXNzs6GsLAw5s6dC2traxgbGxOPmLKyMi7DeKC9tDMnJwefPn0i10mTScuWLSNEypAhQ1gZVM4x+fjxI0aNGkUWJ7oLz9KlS6GiogJ/f3+i3mpoaOCp+Pp3wDyflJQU5ObmksV148aN6NKlC1atWsX6DNOPjPMYtP8BTZhkZWVBVFQUM2bMgLu7O8zMzIhiitmthnk9c+bMgbKyMoKDg2FmZgYVFRUyPidPnkRgYCDU1dVhaWkJVVVVvmUtTU1NSEhIIKVCdPvizZs3Y8SIEZCVlcWqVau41G3MQDo9PZ2owmRkZODk5EQC6b1798LT0xOWlpakpJgmlrdv3w4vLy9SXkxnHDsippjlg5z3NycnBz169MCGDRvw9OlTzJw5ExRFkWf//fv3GDVqFOzs7GBkZPRbl4I9evQIampqsLW1hZSUFLS0tHDixAm8e/cOAgIC6N69O5fXwpQpUxAUFMSlsP27+LtqnF9NJP0nzKj/W+A1R1VXV2Pr1q3Q1dXlUj80NjbixIkTfEmG3xHV1dXYs2cPV5dLmuzuaJ6+desW2SieO3cO0tLSpHuusrIyXF1dkZubyzczfvz4cYiJiWHYsGFYvHgxBAQEMHXqVFZThMjISFAUxerK9DuCeY1fv36FtrY2cnNzUV5eDiUlJZYa4sCBA2R+62h8W1paiNfgsmXLyFzcUYKJOb45OTkkZjtx4gTExcVx4MAB7Nu3D927d8fYsWO54ibOkvzy8nKWKXFjYyO8vb2hpKREVCwAsHz5clYykDkvFBQU4ObNmyz1xsGDByEgIECa5gDA1KlTWYowTjU2rZy1trbG69evcenSJQgLCxNCqqGhAVu2bOFaO5hjQpdYvXnzBm/evMGTJ0+grq5OvIR27dqFrl27QkJCglXpwHmf3r59i/j4eLS2tiI3Nxfy8vLYtm0bvn37BkdHR3Tr1o10sGWOLS9cuHCBNIPJycmBtLQ0tm7dinv37kFUVBSOjo5ciVbO+xQXF4dBgwaRWO327dtwdHSEt7c3+S23trbCw8ODpajjfIZOnz5NiJOcnBxISEhg165duH37NoSEhDB8+HAu/0FOZVFhYSHKy8tJOVlNTQ1SU1OhpqbGRYgx4+WWlhbSDGTAgAGIiYnBnTt3WPuGtrY2rFixgkuRxHze9u3bhzlz5mDixIlIS0tDa2srGhsbSbxNJxY7iu8Btjm7uro6XF1dyd/i4+Ohra0NMTExmJqawt3dna9aC2gncZSUlBAUFMRqetHS0oLjx4+DoihWnMo8j6VLl0JYWBgfP34kx162bBn69etH7u2GDRswdOhQBAcHd6gioitOaFP44uJiyMnJITIykvWd8fHxcHV15RIHMI8jKyuL4OBgvj54s2bNwpw5c8g9Zp5XbGwsRERESFxtamqK4OBgsle4cuUK1qxZg3Xr1uHy5cusaodO/OfQSUr9l/Hy5Uv4+PjAx8fnpz070tLSoKioCGdnZ/j5+YGiKOI59ebNG1hbW8PCwgLu7u4QFRX9SyUXnOcWEBBA1DD8WONz587BxMQEPXv25CKaCgoKEBISAktLS6Snp5PXb9++DS0tLRQWFpIf//Lly1neWePGjSNBOP2d9Oafc8KhJ87Hjx9DXl4eK1asgLi4OMlMAe2TdEBAACtbv2TJEixevJhs3K5cuUIWwI8fP8LU1BTDhg3jYu8nTpyIQYMGEdKDF1GQl5eHwsJC/Pnnn9DR0SFlZHTNOUVRXMbN/MBJTLm4uCAgIIAEDpwBDG1sPXDgQBKEffr0CTo6OlyS+6qqKpSXl7PUa7yQk5OD9+/f4969e1BVVSWLdFxcHCiKgqSkJGtB4VwslJSU0KNHDzg5OWHTpk3knm3fvh3dunVDaGgobG1toaWl1eHGs6WlBSkpKSgvL8ft27ehrq5Osjd0vbuNjQ2LOPlVhBRnKaOCggIOHjxIxu779++kBXNsbCw+ffqEPn36YNCgQR2ew927d/H27Vs8fPiQlY1avXo1KIqCgoICizDkNKRVUFAgWejc3FxQFMUqQ7l16xZ27tyJxYsXsyTjvM7p7du3KCkpwbt372BsbIz169cDaCe9JSQkoKGhgQMHDnCdB9D+G5OQkCDKxMePH4OiKJaxaWZmJqZPn46+ffuySOwzZ86QzSGd5SoqKuJLTNnb26Nv375cRCpdhjts2DDiS/X+/Xvo6OiQJglMU+CysjKWX8PvhkePHqFnz56YN28e6urqcP78eSgqKsLf3x9Au/eXgIAAVq9ejQ8fPuD169d/2dT8n8b/ZSLpV4F+JvPy8rBq1SpER0cTRS29+TUxMflhWc7/AmhiSkZGhpWx76iBA/1acXExGhoa4OvrS7zTamtr4erqCoqiEBYWxvM7Hz58CA0NDTIXff/+HZKSkuQzTGJvxowZP7Q6+E+DHo/a2lpCEF+8eJHMXWPGjMHSpUuhqqqKCRMmkLEsLy/HqFGjsH//fr4qHACsMpV+/frBwMAAycnJpBSLX4KUifLycpSWlqK8vBz29vakuUVjYyM0NTUhJydH1Fy8Pr9gwQJYW1tDXFwc4eHhJDnb3NwMHx8fyMrKYuvWrfDw8ICxsTHPzfjcuXMhKysLCQkJKCkpYd68eeTe0sSUr68vnJycoKOjw1d5M3bsWGKMHRQUBGNjYwgLC5O1DWiPqVxdXVlt6Jnjunz5cigpKbE64G7fvh0eHh7E0Ds9PR39+vVjxUT8Nvl0TDNs2DBMnTqVvH/kyJEwNDSEs7Mz17gybTK2bNlC1FFFRUX4/v073NzcSAfCiooKWFlZdfg7osdYWloaJ06cICr5lpYW0lFXSkoK3t7esLCwgKGhIc8SqKlTpyIuLg4tLS348OEDqqqq4ODgQNb54uJi4pfLmXDhJMesrKxgZGQEBwcHsubV1tYiNTWVS0nKi3wpLy9HZmYm6bxLxzdAO8Hl5+fHIjOZmDNnDmRlZTF79mwMGjQImpqa5HzLy8sRHh4OYWFhojzihy9fvmDAgAHEnP3ChQvQ0dGBlZUVec/Lly9RUFCAx48f/xRpcvXqVYiKimLEiBEsm5Pm5mZcuXKF52fv3LmDsWPHsoz629raMHjwYDg7OwNoV0sGBwezSFB+z+yzZ89gbGwMoJ08VFJSYu1BmOPyI1V3eno6Ke1kNvOoq6tDTEwM1NTUeIoV3r17h9DQUFYVUXJyMry9vVkliJzoLNn7z6OTlPoNwM/HiVe278GDB5CRkSFlcnSHkYULF5IF6v3794iPj0d0dDSX+uev4tmzZ5g6dSprI0vjyZMnJJN2/fp12Nvbs1RaNO7fv49hw4ax2O3KykoyAXGW4NGLxtSpU+Hp6Ulej4qKgomJCVpaWljnsW3bNixevJhktegWpbNmzSLvoU3rmKz+tm3b0KVLF1LHXFFRAVdXVygqKhL2PCsrC7169cKwYcOQkpKC69evY9q0aawNH/M+NTU1cW3A0tLS4OjoSLJ/Fy5cQFRUFFasWPHDemVepVFtbW1ISUmBvr4+q10w7RvFPBdHR0dcuXIF1dXVUFBQYGVPDx8+zLOG+0cT8bp169C/f3/yXQcPHsTgwYMxf/588gwyF/5Lly7B0tISeXl5KCgowMiRI+Hg4IDly5ezzGnHjx+PyZMns8hPfiWJ9PesXr2alQnauXMnwsPDWZ2B/gnQ5YP5+fk8DdS3bt1KyFwTExO+XhWcWL9+PQICAgj5evjwYQQHB2P16tV8r2flypWEbElKSoKoqChRE1RVVfFsrc6Z9WxqauJqIZ+TkwN9fX1iQHnt2jUMGzYMK1eu5BsIbdmyhQSAz58/h5aWFsaNG8c1PgB/5U1+fj7s7e3J74sfMXXgwAF4enqiuLiYRai2tbXh+/fvMDMzQ3Z2Nmpra6GoqIjx48eT52fLli2khPB3RmFhIaSlpTFw4EDW6zY2NtDR0UFlZSVqa2uxd+9eCAkJQU1NDQYGBjA0NPytlV+d4I20tDQICwvDzc2N+NTNmDEDpaWl+P79OzZv3gwrK6sON4y/Ezoi4qurq7F3714oKChg6NChrL8x54k7d+7g1q1brOTat2/fYGxsTAiCxsZGRERE4P3793znyfPnzxMvk48fP0JNTQ0zZszAlStX0L17d0yaNInnhuZ3wsePH2FoaIg7d+4Q3yxa0bVs2TLSzIZpch0TEwNdXV1W/MUc33379mHy5MkYO3YsGc/W1lb06dOHb6c85udLS0tZSjOgPW7U0tIi5/b+/XuEh4fjwIEDfBuObNmyBXJycti7dy82bdpEuiUzy4+GDh0Ke3t7VudJ5lpEEws5OTl48+YNYmNj4eDggNGjRxPy5OrVqxg0aBCmTZvGV2lCJ4vpWPbatWuksx2Nqqoq+Pv7w8XFhecz9+bNG0yYMIGrFHTbtm1QVFREfn4+mpubERwcjAULFpDfCjN+vHz5MtLS0nD8+HESb1VUVMDQ0JCshXV1dRgwYADOnTvHlRz9888/oaWlhblz52LmzJno1q0by5/r/fv3UFNTIwTE169fMWTIENy5c4dvrHL69Gmoq6uT9aW5uRllZWWE7KqoqEB8fDxiYmJYsQLzui5evAgdHR1WtcCbN29ICS7QTtJERESgoKCA7296/vz5kJWVRUpKCi5dugQnJydIS0uTuaK2thZpaWkQEhJieYoB3OTL+/fvMXr0aOjr6+PYsWNoaWnBs2fP4O/vDysrK54xz9mzZ6GhoUGU8PR30RUSQPs8FxwczNM3lsbWrVuhr6/PimVpDywdHR1W10AmfoY0uXLlCiGmOL1EAfZ9OXbsGCwsLGBsbEz2K/R35OXlQUhICGZmZiSu7YgQoz9XXFwMMzMzJCcnk5Ji+nMvXrxgNeP5EVpbW7Fjxw5069YN+vr6CA8Px6RJkxAcHAxZWVncv38fu3btYiXHd+3aBREREejr63M1WNm9ezd0dXUJwdVJQv330UlK/SbgZyb7/v17VmCXlZVF6qTfvn0LZWVlVqaROZH8ah8d5gREm7UPGTKEbKAvXLgAJycn9OvXj8vXit6Arl+/nkVCFRUVQUZGBoMHD+Z67/bt2+Hm5gagPYMmLCzMpfqaPXs2FBUVkZCQQLJh9+7dQ2hoKGRkZLBq1Sr88ccfxLSOnoCbmpowe/ZsjBw5kpz7zZs3ceXKFXh6ekJXV5eM5dmzZ+Hm5gZ5eXloa2vDwsKCTG7MSWzTpk0YMGAA/P39WZmd7du3Q1BQEC9fviTKGWbZAnNzfu/ePdy4cYOUa3F+BzPLmZubSxbryspKSEtLIyYmhkVMhYWFoU+fPlBTU0NkZCT5rtraWgQHB2Pjxo18zV/379+P6dOnY9q0aaS0DGjPDCkqKqK2thZNTU3o168fy7iQGUAcP34cERERrE5q3759w8SJE+Hg4IAVK1aQ9zO9PTgJqYMHD2LWrFmIjIxkye3Hjh0Lc3NzAO0ZLc4Wu/8EMdXQ0IA+ffoQ+fv79++RnZ2NAQMGIDIykpA4z549w+nTp3n6D+zcuRPTp0/H8OHDcejQIbKJiI+Ph6qqKp4/f462tjb07dsXsbGx5B6lpqYiNTWVdV1jx45FeHg48vPzISIiQggpoP33FhcXx9cMHWjvrhIUFARlZWXMnj2bqDNOnjxJPMjevHmDPn36sEqBX758yaX0mzx5MsaMGYPW1lbicUSf+6FDh1jKRV7diYD2DYCDgwNcXV2JWoFJTDHNz6urq1FdXQ15eXmuMuXIyEiEhoZCWVkZkZGR5LdfX1+Pfv36YeXKlb99EPLu3TvY2NggODiYKGmXL19O1IDBwcEIDw9Heno6Lly4gKysLDx+/JivBL4Tvy9evXoFVVVV7N69m/wmkpOTISUlRbxwKisrsWrVKri4uJDEye+Ev/p7qq6uxqZNmxAYGMhzPpg/fz50dXVhbGwMcXFxTJo0CYWFhWhra4OFhQUcHByQkJAADw8PmJubszqncR6rvLwcjx49QnNzM/r374/Ro0fj+/fvaGlpgbm5OSiKwujRo3/7cg0PDw/IyMigS5curHUZaPfEkpKSwsSJEzFv3jyMGjUK4uLifLtd0mXfEydOxMKFC0FRFFHNtLa2Ijg4GBYWFti3bx9PI/ElS5bAysoKcnJycHNzw9atW1FVVUW8PRcsWICsrCwEBgYiICCAZ1k/0K4Unjt3Lsvr8PXr15g9ezasra1Z8WRpaSlPX8YDBw5g7dq1ZF2msXv3bhgbG5NELuf3c97vffv2wcXFBX379iUxSV1dHbZv3w4tLS3o6urC29sb9vb2MDc3J+PCXN8zMjJAURSUlZW5OrDduXMHAQEBkJKSgq6uLktJxBzbuXPnQldXF0ZGRnB1dYWGhgaZ12fNmgUhISHMmTMH9vb2sLKy4vnMf/36lXSoFRERIYQUkygyNTWFs7MzTp48CQ8PD7i6unL9jpjIyMiArq4uGhoa8Pz5cyxZsgSampqQkJAg8TonmMdJT0/H6NGjCUlEn++3b98gJSWFUaNGkS7FTOUXZyItPz+fJF2B9nhFXFwcJiYmrL1CTU0NLl26xDqHtLQ0WFpawtjYmDRAoo85evRoiIqKQkFBASYmJnBxceFLXh44cICoh1JTUyEiIsKyC6G9H6urq/nOjc3NzT80Z9fX14e6ujrrb39lf3f16lVISEggKCiItT/gLFXNy8uDp6cnhISEWH6g9Lnn5+dj5syZiI+P59k9mj7ezZs3kZ6ejk+fPqGurg79+/eHsLAwQkJCWOc1d+5cODg4/OV45datWwgNDYW5uTlcXFwQHR2Nly9fIiUlBb179+YaaxcXF1AUhb1797J+7y0tLZCUlGTtGTrx30UnKfUbgdNMtqGhAfb29lBXVyc/9l27dsHKygpPnz4lnZboH+CZM2cwZswYLr+XfwL19fVYvnw5HB0dMWbMGLJ45+bmwsnJCaGhoVyeDDU1NXBxcYGYmBhRcFVWVmL37t08u4UcP34clpaWmD59Orp3785VppOWlgYFBQWuzmJAe9ng4sWLoaenh4CAAEyZMoVL7bVixQoICAiQLi90VuzGjRtwdXVlEVPl5eUoKirCq1evuBQlQHspl7y8PJYvX45Dhw6BoiiEhoaiqakJjY2N8PDwAEVR0NTUZClnOLvJGRsbEz+evn378hx7zsWIXhQSEhLQvXt3xMXFEXXM8ePHoaurC1NTU9Zn5s+fDy0tLb7dLubMmQN5eXlERUVh4sSJkJKSIpv+/Px82NraQlZWFqampjAwMOAZVFVXV8Pd3R09evRgtWsG2gOQSZMmwcnJCQsWLOhwM0O36R09ejSioqJY5o0FBQUQExODtrY29PT0YGxs/Ms3FZznRhufhoeHY9u2bejTpw+8vb3h6+sLV1dXDBgwgEjzaTAXblruvWTJEoSHh0NbWxujRo1Ca2srLl26BE9PT8jJycHIyIhrbEePHg2KonD8+HHy+rVr16ChoUEWXRp1dXUIDAwk5p28sGDBAsjJyWHnzp3IycmBrKwsMRetr69HcHAwVFVVoaioCCsrK9ZzGxISgp49e+LmzZssE2FNTU2IiIhg8uTJrO+aNGkSRowYwRqbt2/fkixjSkoKhg0bBqDds8zHxweOjo4sYioqKgq6urrE84r+3r1795ISNxqHDh2Cjo4ObGxsWGqxmJiYDp/93w20kjY4OBjjxo2DjIwMUlNT8eHDB2RkZCA2NhYyMjJQV1fv0FC2E78XysrKcOfOHVJO/vjxY2hqauLhw4eseTQpKQldunQhCoKamppfYl7/q8EvifCjbnZMf0fmMdauXctSPdDGuvT/i4uLYWlpCXt7e/j7+/Ps0lpRUYG6ujqWQqC6uhq2trakRKe5uRlRUVHIysri2eXtvwnmc0BvJi9evAiKoiAuLo7r169zKbNXrFiBIUOGwN7eHpGRkawyFyYuXLgANTU1olI4c+YMunTpwlWK5uDgQJJ3TCxbtgyysrI4fvw4qqqqYGdnBz09PRLbLV++HHp6etDQ0ICzszPPmKetrQ337t0jdgbMpAXQrp4xMDAg831HY6OnpweKosgawsSwYcNgaWnJ8xj0M9fa2ora2losWrQIWlpapOSIxvfv3/Hs2TNER0dj0aJF2L59O1nXmTEHbfUQHh4OiqKwfv16VsINaCfhjhw5gi1btpAyemaMsG3bNsjIyJCkz+bNm0FRFEnIffz4EXPmzIG7uztGjBjBkzShx+fYsWMQERGBhoYGa32kP0Mr2Q0MDFjmzpyJbXqcLl68CEtLS9ja2kJBQYHEQfn5+ejRoweXMoz5my4rK4ObmxvExMRYJVz0d54+fRqysrIwNDRkkUGcMW9cXBymTZtGElRnz54lpZ2FhYXQ0dGBvLw8FyFIj8/Jkyfh5eXFRb4A7XPGkydPcOzYMdy8eZNnmRz9WmJiIgYPHkw8Vpm+U5mZmZg1axYxaqc/x69rXEZGBmRkZNCnTx+u8bt06RJL/c95DM7nixdhdf78eXh7e5PPZmZmkt/+rFmzMGjQIAAgiXknJycWGfwjPzn6O9PS0iAmJobY2FgSu12+fBmampoYMGAADh8+jAsXLmDq1KkQExPjqpT5WfBLONPneenSJZIgBgBbW1toaGiw1HlfvnyBvr4+6VDYif8+Okmp3wzMbEtbWxuuXbsGY2NjmJubo62tDZ8+fYKjoyPExcUxatQo8j4AmDlzJvr37//LjG2ZHga88P37d6xbtw62trYsYurixYswMjLC8OHDuTbnRUVF6Nu3L6SlpUl5TmVlJQ4cOAB5eXkWMUWb8YmIiPAsRYmNjUVAQADrNU5Cgjblo8FJYDk5OUFQUBDTp08nr7W1tRFiSk9Pj8tkkRMPHz6Evr4+mexOnz6NXr16sXx0gPYStYyMDJ6BzJo1ayAlJYUbN27g+/fvWLx4MSiKYpVz8lM0MSfnnTt3EpPS1tZW1NXVYfHixTA0NISHhwdmzZqFgQMHQkJCgm95T25uLjQ0NEjwn5KSgp49exID+tbWVty7dw8rV67EqlWreMqzaXz8+BFhYWHQ0dHhyup++/YNgwcPZqlpOHHmzBmoqKiQczl9+jRXzf/Tp0+xYMECrFu3jmf25ldhx44dJOA8cOAAHB0dIS0tjT/++IOc3/z581mqP05cvHgRWlpahIjJzMyEkJAQy0stPz8f27Ztw+rVq3mWzU6aNAm9evUiXS3Ly8sxffp06OnpYcOGDaipqcG9e/fg7+/P6s7COcZPnz6FkZER306VQDsxfvXqVZw7d47ncxsQEMDqGvjp0yeMGTMGGhoaSElJAdAeiNIye6ZPS1tbG9zd3aGkpIT4+Hh07dqV9YxkZWVxEVOFhYWYM2cOzw4tSUlJEBAQQHR0NHktJiYGZmZmcHV1xdSpUxESEgJJScn/udK2Fy9ewNvbm7R858SXL1+QmprKCsI68fvi6dOncHJygp+fHwYMGICWlhbcuXMHAgICRCXLLJkyNjbmuTH/HcEribBx48Yfqqg456fhw4cTb73U1FSIi4uzumEB7WtRRUUFSzlD//vUqVNwcXGBlZUV9PX1kZ6ejurqapSVlUFaWhozZszAvXv3MH/+fKipqf2yuOlXgb4O5qY2Ly8PO3bsQE5ODoKCgiAvL4+zZ8/yXHvr6+s7HPOkpCTSqYouG6VjlsrKSjKnt7W1sRKdra2tKC8vh5OTE5nj6S6IzI5hQHvS4dWrVz/0vzl8+DDxMeJUAIaEhGDIkCF8YwT69dLSUvj4+EBRUZFrft+8eTOcnZ1ZKhFO0NdYUlKCNWvWEGUe5/dwIisriyTspk6dCj8/P/LewYMHQ0xMDCdPnuTrq/f06VOiAmJaV9Am1MePH4eIiAgZ2/r6enIsJtnKzwqiuLgYjx8/xsaNG6Gvr48ZM2ZwncP3799RXFzM+h1xlvczv+vkyZNYtGgR0tPTyfP58eNHWFtbs6oZmLHz9u3bUV5ejuvXr8Pf3x8KCgo8CevKykq8ffuW9cwwzyU5ORkqKiooKCggZfvBwcFETdrc3IzAwEDIyMjAw8OD72/g4sWL8PT0hKOjI4t84RU78rt3L168gJCQECiKYsVN9fX18PPzQ3h4ON+4/dKlSzh69Chyc3PJGGZkZEBNTQ39+/dnfQ/zGMxzWbt2LYYOHQoDAwNs2LCBb6MiTjQ2NiI4OBi9evXCoEGDICIiwmoicP78eQQGBsLb27tD831O5OfnQ1JSEnv27OGyZ8jOziZ7P1NTU7i6uv5tQorzXNra2lhx7u3btyEoKIiYmBhW8tHS0hLy8vKYOXMmdu/ejT59+rCUip3476OTlPqNwM/Y8+bNm9DT0yP17CtWrICEhARWrlyJz58/4927d4iOjoakpOQvM7alz+Xu3bvQ1NREZWUlbt68iYULF7Le9/37d6xfvx6mpqaYOHEiCaSvXLnCt7VnUVERgoKCICUl9UNiKjIykmUSCfxrMoqJiYGDgwNXoNHU1IT09HQuMi0nJwciIiKoqKhAc3MzSkpKoKysDFtbW4iIiCAzM5M14d+4cQOenp6QlZXl6kTCxPnz52FgYACgnWTgDO7ojnVMMBe+pqYmDBs2jCxqJ06cgJiYGCsIYYK+N2VlZTzNyemOdkuXLgXQHrhkZmYiNDQUgYGBmD59eocmrgcOHICTkxMAkPax9PVUVVURSTITzHF7/vw5Xrx4Qe7/p0+f0K9fP7i5ubHIF/rc+JVxAe1SeloxRgfOtAz/27dvPBe1f2KBqa6uJllf2pvi06dPLOk3APj5+RF/J15ISUkhv2NOuXdVVRUuXbrENQ/wyo5FRESgV69epHnAq1evEBUVBVlZWYiLi8PU1BSenp4ddmf5888/Sekjr06Vx48fJ6Qyp5yfeb/pzkj0Jub27dsYPHgwxMXFoa+vD1tbW6iqqvIlgrS0tNC1a1fSIp4JmphydXUl5p4tLS348uULz/nl0KFD6NatG8tPLikpCREREQgICMDs2bN/OwPjn8Xr16/h4+MDf39/FlndaSL+v4UnT55AXFwc8+fPx4cPH1i/64EDB8LQ0JAVSDc2NsLKyoprw/874meSCD9CW1sb6urqoK2tjfT0dFy/fp01NzU2NmL27NlcFgHM9SM7Oxs9evTAmjVr8ODBA0RERICiKJKUysjIQJcuXaCpqcmTxPhd8OXLFzg4OGDhwoXIzMwERVGsTaKvry/k5eWRm5tL5oEDBw78VOOGU6dOwd3dHXv27GGtQ/TfwsLCUFhYiIiICERHR7M64pWVlcHMzAw1NTXIyclh3Z/6+nrs3r2b5WEF8FeJ0NizZw8oikJMTAwKCwsBtK9DFhYWmDNnDus4nMdlnpe1tTX09fVx5coVfP78GVVVVXB1dSW2F7w+d+rUKQgLC5PfXVlZGVauXAlDQ0OWbyfnXNvQ0IAVK1ZAT08PdnZ2EBMT41pf6ETIqVOnuD6fkJAAiqJICRr9DIeEhCAuLo4ocGgytrW1FZs3b8bmzZv52jrQuH37Ni5dukSaidTV1WHVqlUwNDQkBA7QHkczuwNz3qe1a9ciICAA5ubmXPYQwL88pYKCguDk5ERihJs3b0JQUBD3799HVFQUJCUlicVGXl4egoKC4OnpiTNnzrCOxQTnvb58+TImTpxIOrm1tbWhvLwcmpqaJLasqalBaGgorl+/zop7Tpw4geTkZBw5coS8dvHiRQQEBHCRL0ww55W9e/di3rx52Lx5M/FRS0tLQ8+ePREVFYXc3FyiSDI1NeWbEIyOjoaqqirs7e2hp6cHLy8vUmKYlpYGDQ0NrlI3TsybNw9ycnJYt24dtm/fDnFxcYSFhXW4V+GEoqIihISEyPzMHO9z584hKCgIvr6+P92JdMOGDXB3d2clVJj3tLGxkXiA8vK3+rtgji89961duxZqampYuHAhaz11cnICRVEYMWIEX9uRTvz30ElK/SagJ4OSkhIu36SmpibcunWLyKCB9knNyMgIgoKCsLW1hZ6e3i8LrOhzefjwIURERIg/0syZM2FgYMD6IQPtP+aRI0dCREQEQ4cO5ZKS0mBOHB8/fuRLTCkrK7O6ZfALZPbu3QthYWFkZ2ez3lNVVYWAgADW4gO0L8p05oYOsOjgZ/To0RAREcGJEydYk+iVK1cQGBjIZeTJxOvXr+Hq6orly5ezCBygfWEODAxkdRnjvJ6mpiZYWFjg8OHDOHPmDCsIaW5uxpo1a4gqhsbbt28hJSUFeXl5xMTEYMuWLSgrKyPH3rdvHyGm/uqm9cSJExg6dCiOHj3KItiA9k3G1KlTCTEDsO/r4sWLYWRkBH19fUhLS2PdunUA/qWQc3d3Z5lA8hsTGklJSejduzcOHjzIFTinpaVh2LBhf2kR/lnwOp/CwkLSsYd+boD25/bixYvw9/dnlQ/yItnS09MRHByMkydPsu4z0B4YT5s2jUV0ZWZmYubMmSgoKGCNOdDuJSUkJIS0tDQA7QTxp0+fkJOTgydPnvwwO/38+XPIy8sjLi6Oq1PljRs34O/vj3v37pHrePv2Lfbv349v375xBaZeXl6Ql5cnxFRJSQmuXLmCuLg4ZGRkkJJkJpqbm9Hc3Aw1NTUoKyvDwMAA169f5xr77Oxs2NjYwMfHB42NjXjz5g0UFRUhISGBefPmYf369aisrCSBUFJSEoSEhFibif8rYDbF+NlurZ34ffD161c4Ozvz7SaVl5cHPz8/6Onp4cKFC7hy5QoWLFgAaWnp/4ly046SCJWVlax1kAlec+XSpUtha2sLQUFBVknZly9f4OXlxTLAZoLuOksn0OhyHmaTD6D9t3Tv3r3f0puLxufPn7Fq1SqoqKhAUFCQxDTMGMvPzw/KysrYvHkzZsyYgS5durAM2/mtrY8fPybjGx8fT16vr69HYGAgUXnMmTMHqqqqWL58OYmfWlpaYGJigqCgIFYCDWiPh9zc3Fjej5znkZGRgR07dmD79u0spduOHTtAURQsLCwwduxY9O3bF2ZmZkR1wTzGjh07EBkZib59++LChQtknSsvL4eNjQ2EhIRgZGSEoUOHwtbWlhyDs3Pz0aNHMX/+fFAUBUNDQ9IAo7S0FCtXroSxsTGLxOFEQ0MDPD09yUaXBvMehYaGQlZWlssP8smTJxg5ciRkZGRYxNSKFStgY2MDERER1rpcXl6OgIAA0sGQCeZvKCYmBhoaGjA2Noa8vDxGjx6NFy9eEE86PT09+Pn5wd/fH0pKSnw35DExMVBQUEBsbCwOHz4MAQEBjB8/noxRY2Mjdu7cCW9vb1hbW3MlwUaPHg0xMTGIiIhw/fYvX76M4OBgeHl54ezZs3zHl0ZJSQm0tLQgIiJCVGQ0QkJCoKCggISEBDg7O8POzo6lHIuKioKEhAS0tLQgLy8PIyMjsl86d+4cAgMDeZIvzOckOjoaMjIycHR0hImJCWxsbMg1HT16FCoqKlBWVoaVlRWCg4P5JgR3794NBQUFEivFxcWhZ8+eyM7OBtD+PGVkZEBISIjlxcrEnTt3oKurS/aKd+7cQdeuXUls/SNFE11FYWlpCTc3N8jIyJBkM/Oz58+fh62tLauShN/xgHZPUXqPynwdaG949au9Ljmvc/Xq1ay944YNG6CkpMRFTNF+cH+3M30n/jl0klK/EQoLCyElJQWKouDu7o6YmBhcuHCBlKDdvn0bJiYmRMVSVlaGo0eP4t69ez8sMftZ0JMI3Yac2bGipaUF06dPR+/evbFo0SKuAMHExAR9+/ZlZdQA/hPkx48fERgYyEVM7dy5E7q6uvj06ROX3PXMmTOsGvBhw4ZBTEwMiYmJePjwIf7880/4+vrCxsaG70L78uVLUBTFlXkODw/nSUz9qF7706dP8PX1Rffu3VmLyPfv3xEYGIjQ0FCegSF93ObmZsyYMQN+fn4QFRVlES/FxcUIDAzkKgVMT0+HsrIyKIrCxIkToa6uDl1dXdja2mLPnj148eIFDhw4gK5du2Ljxo2szOmPFqyHDx9CTEwMFEWR8gmAvySZxvLlyyEjI4MLFy6gsbERI0aMQK9evYia6ePHj+jfvz+MjIx+OvNy584dEmAyAzHaLykiIuKXG/ozwani+vjxI/HOoImpGzduwMfHBwMGDOhQmQS0B7p0G3Jmqdr379/h5+eHESNGkO969uwZBAUFQVEUbG1toampicjISOzatYsE2AsWLECvXr2QmZnJkwzmlU1l/pvuyMOU9NOdKvv06cNS5cnLy4OiKLi4uMDFxQU7d+4kgXRraysGDRpEiKmOsk7M62OWzFhaWkJPT48nMXXr1i2SeT9y5AjU1NQgKCgIPz8/WFlZQUVFBU5OTti5cyeuXbuGtLQ0dOnSBStWrOB7Hv+r4NcUoxO/P54+fQotLS1cuXKFL1lw+/ZtDBs2DIKCgtDW1mZtoH4n8Dr/jpII6enpGDZsGKscjZfSl0Z2djYsLS3h6upKFBafP39GQEAAHB0d+c4x9fX1MDExwaVLl1BdXU06b9LYvn07F8H/O+Py5cvo2rUr5OTkiPoZYMclgwcPho2NDYyNjVnPCnPOP3jwIFavXo1ly5aRtevQoUOQk5PDpEmTcOrUKWIwbWpqyvLzWbZsGZSVlREXF0fGLjk5GYqKiggMDCTfUVdXh4CAAHh5efH0NwLaN/dycnLw9PSElJQU/Pz8cPbsWfI87d+/HxRFwcnJCcnJyeTzzORadHQ0FBUVMW7cOIwfPx7du3fH1q1bSQleWVkZfH190atXL1ZnL84E3ezZs6GhoYGVK1di/Pjx0NfXZ7WVLy0txerVqyEjI8PTDLmtrQ1VVVVYvHgxZsyYAVNTU9YGnmlfQftOcuLFixcYMWIEJCUlSSl9UVERzM3Noampiby8PFRXV+Pdu3fw9/eHra1th2rwhIQEyMvLEzXtzJkzISwsTNbqb9++4ciRIwgJCcGoUaP4xiwnTpyAjo4OURfm5eVBQEAAAgIC6NevH9nkHzt2jGU1wBzjlStXgqIoiIqKspr30Lh06RL69esHExMTnn/nxKNHj6CtrQ1HR0dWudnjx4/Jb6B///4sX6xnz57B3t4eDx48QElJCT5+/AhnZ2eoq6uTazh9+jTs7e35ki+vX7/GuHHjSMOAixcvok+fPtDX1yeVHKWlpXj16hU+fvzI04ifxqRJk0jCLD09nRXz19bWory8HM3NzVzm7EzcunWLKO7p5DGd4KypqcGFCxc69DXlnLsHDBgAaWlplgIbaI8FX758+dMNLGjikunbRF/XrFmzkJ2d/cvi9cjISFy4cIF1bhMnTmR1aAb4E1OWlpYwMjLClStX/tE9RCf+GjpJqd8I79+/h7m5OfT09GBtbY1Ro0ZBSEgI5ubmGDFiBFJSUnDs2DFoaWmhd+/e/9gPiW5Dztl2et++fQgPD0dUVBTs7OxY5Tbz589HXFwclwErfY5XrlxBdHQ0pkyZQnwIgH+RLlJSUsSQs6qqisvfYfbs2VBRUYGqqipkZGRgb29P/FNoUkZYWBhmZmZwdHTskBxoaGjAnDlzuPxzgHZiSlJSEqmpqawF5erVq6RUinldzL9raWkhKCgIq1atwr59++Dp6ckyNWceLzU1FcrKysQ0PTc3F5KSknB2diYTZ0lJCd8AvKamBkePHiWZwObmZpw/fx7h4eGwsrJCjx49MGTIEPTs2RMURWH79u2sc87Pz2epfTiviy4VmD17Nk6ePInc3Fz07t2brySZJjJoCXVGRgYkJCTIQkmrWN68eYPo6GjW9dy9e5evGSvQnv1QUFDArFmzcPHiReTm5sLHx6dDefTfQXp6OsvEfuvWrayOjfR3FBYWwsLCAhYWFmQMnz9/zjJY5LXZoq85NzcXEhISGDVqFLKysnDq1Cl4e3uzWuy2tbWhpKQEy5cvh7q6OkJCQpCbmwt/f3+oqKhAXV0dPj4+SE9Ph4mJCbS0tJCSksIVBGVkZLA6GnGO04MHDxAWFgZpaWksX74cS5Ys4epU2draig8fPmDy5MmQkpJCcHAwNmzYAG1tbZI5nDt3Lu7fvw99fX3Y2dnxLENkfn9GRgYMDQ2xYsUKogyjO2oZGBgQFVBsbCyXyW5DQwP27t0LX19f4qF39uxZouSUl5eHk5MTFBUVQVHU/8nOKpxNMTrxv4GkpCR069aNZ7kNPT/U1dXh2bNnKC8vx4cPHzokbv5bYM4jmZmZ5N/5+fk/nUS4evUq3NzcWGV4nPPTrl27YG9vDyUlJdjY2MDKyoqnIoMTo0ePxrBhw6CiooJJkyaR99fW1iIoKAgbNmz47Tci9LPx/PlznDt3DmvWrIGBgQFX4otGaWkpa/1iXt/s2bMhKioKd3d3yMnJQUdHhyjYduzYAV9fXwgICMDZ2Rl9+/ZldSmmERsbS4ip8vJyVFdXY9GiRZCQkICfnx+GDh0KV1dXVszD2TFtw4YNUFZWJub+R48eJUnY06dPcym9lyxZwlVCfvDgQaiqqpJj3LhxAxRFQVBQECtWrCAxaFlZGWlvT5OaTBQUFEBVVZWVILt69Sq8vLygoaFB1PGfPn1CYmLiD8t7ampqsGrVKhgZGbHIjZaWFnIsfpv758+fE2KKbrjz7t070rxFSkoKDg4OsLe353r2maX1bW1tGDJkCFG+ZWRkQExMjFVayUs5zxk3tLW1ITs7m6i0cnJyIC4ujqSkJNy9exeCgoIYM2YMa1xv377NNUbV1dV49uwZxo4dC3FxcfJbZ77vxo0bmD179k8TH48ePYK5uTnGjRvHpb76+vUrixDau3cvPDw8EBwcjIaGBtZvwtLSEp6enqzz53UOycnJ0NLSgqOjIyu5S5cgGhgYsAgyGvzKTEeNGoXExETk5eWxKhFaWlqwc+dOrkoCXvfr8uXLUFZWxt69eyEmJsZS050/fx4DBgwg+yOmypTz/tDj0dTUhJCQEMjJyeHChQv4+vUr+vfvzyLzeSU4X7x4gWvXrqGkpIT4joWEhBClL9C+n1u4cCHk5eV5/g7/LjQ1NaGtrY28vDyyvxg4cCBR0XHOO6qqqlyVCBoaGrCxseFb3dOJ/zw6SanfDK9evUL//v3Rt29f5Ofn48OHD0hOToaTkxNsbW3Rs2dPmJiYgKIo0tHsVwdX/NqQ9+rVC/fu3UNNTQ3mzZsHCwsL6OnpkS5cTMk4ExkZGZCSkkKfPn1IR5JVq1YRtUdxcTH69u0LiqJ4+r3s2rULUlJSuHv3Lt68eYPnz5/D1NQURkZGJGB/8OABrly5wrNbBj0+Dx8+xK1bt9DS0oKGhgYsWbKEy6AQaJdaq6iooKamBm1tbaisrISTkxPc3NxYxoyc437hwgWMGTMGKioq8PLywsiRI8k5ME3/0tPTiVzcwcGBXENmZiakpKRgbW0NIyMjODo6sjqecS4otbW1SE5Ohry8PMLDw8nrJSUluHPnDhYtWgR/f39Wt8PW1lZ8/fqVdAfklTGmryspKQl6enqQlZWFnZ0dX0lyW1sbvnz5AgUFBXIfmB4T379/x7x587jubUtLC96+fQsDAwOMGjWKq/MRc1GJjY2Fh4cHunTpAgcHBwQFBf1wY/JXkJWVBYqisGLFCkKI5ufnQ1VVFW5ubiyCBviX/4W8vDyrfPDbt2+QkJCAu7s7X18PmkDU09ODmpoarK2tERISwvN6SkpKsHLlSoiKipLgoqmpCbt27cLkyZOhp6cHfX19UBTF1Xmtvr4ekydPRpcuXXD06FHyOi/D8z/++AP6+vrw9/fH1KlTeRqsv3nzBrNnz4aYmBjJuj558gTTp0+Hh4cH1NTUYGhoCIqi4OHhwddU9tSpU+jRowc2b97Ms1OojY0NVFRU4O7uDhEREdKBiHnu379/x+7du2FhYYERI0aQ39fHjx/x8uVLzJkzBwMHDkSvXr34lgz9r4PTSLQTvz+uX7/OKrnlhU2bNsHb25vly/E7gTkvv3v3DhRFITIykry2YsWKn0oiZGdnw8PDA/7+/lxdspjfce/ePRw6dAjx8fE4cuQIq+ECPR/U1NSwEll79+6Furo67O3tWb+TmJgY6Ojo/NLN0a8GfU1MM3egXT2zbNkyGBgYYMGCBeT9Bw8e7LCUt6ysDB4eHrh79y4Zu7Fjx8Lc3JyUA1ZXV+P169eorKzkUnlwrsM0MVVVVYX6+nqyCZ44cSKrVfycOXPIZp02pJ8xYwb27NkDoL28U1xcHCtWrIC+vj6srKxYVgy7d+9Gly5dYG9vT8i2uro67Ny5k2zkT5w4AVFRUSQnJ2PlypUQFBREQkICianKy8thb28PRUVFrnuen58PISEhljl0a2srzpw5Q/wQaTKppaUFmzZtYqmueN2zr1+/YvXq1TA1NcWkSZNQVVUFb29vDB06lPUdvMCLmCorK8PFixexZ88eXL58mavZCHMtpz0W3d3dceHCBdy8eZNFejQ1NWHjxo04f/48l2qa1zl9/foVhYWF+Pr1KxwdHYnquKysDFpaWqAoCnPnzgUAuLm5YfTo0azYhXP+GjRoEMTFxVm/9UWLFrGUkz8by92/fx+WlpaIiIjg6aPb1taG2tpaREdHQ11dHWZmZuRvNAGRmZkJdXV1UopIg3MskpKS4ObmBgkJCa4qkOvXr6Nfv36QlJTkKq/md59jY2PRtWtXCAoKEiUg0F4l4uXlhdjYWJ7H2LNnD/HSAtrVkRRFsd5PJ4dDQkLQ2tqKs2fPgqIolhKe3xg3Nzdj0KBB6Nq1K4yNjaGvr9+h9UdqaioUFRUhJSUFU1NTbNiwAY2NjXj69ClGjhyJbt26wcLCAlZWVpCXl/9H1L6urq7Q0tIiCq8+ffpwKaVoLF++HMHBwVzP+++8Fvz/iE5S6jfE8+fP4evrC29vb9aG7Nu3bzh06BDmz58PCwuLf1TSz2xDHhERAVlZWVbdd3V1NXJychAREdFh2+E7d+5ASUmJZOVKSkogLCxMVDj04lpYWIhBgwbhxYsXXJP5tGnTSJtf5qZUW1ubq0sFDc6yq4yMDMjLy2Pt2rUsE026yx0nMcXpM3H//n34+PjAz8+PlRnmnODq6+u5DNaZG/tZs2ZBW1sbf/zxB4YMGQJVVVUYGRmRhfn27dtITEzEH3/8wfIf4CfXrqurQ3JyMpSVlbmUbTR4tQ+/du0ahIWFMXz48A5LGUpLS/Hu3TuUlJSQseS3GY6IiICnpyd69OjBys6UlJTA1dWVlKtxkiJbt26Fra0tJkyYwEVcMa/727dveP36NcrLyzuUR/9d0H4W8fHxxITx3r170NTUhLOzM+u6T5w4gcjISEyZMoVrkX/06BFUVVXh7+/foeFsdXU1ioqKWGPL63o+f/6MuLg4iIiIcJWjffjwAQUFBYiLi+P52ffv32P27NkQERFheazxC0SZ4BW8vHv3DpGRkRAREUFqairrfXl5eUhNTUVwcDAhgs6fP0/Kj9va2lBRUQFPT0+ioqirq8OHDx+wc+dOloIyLi4OsbGxhExlgh6rhoYG7NmzB1ZWVggNDeWZ7eLsvtmJTvw3UVRUBFlZWQQHB7OMoJlz4qxZszBv3rzfUsnDPKf4+HhMmDABCgoKoCiKpWhcsmQJSSI4OjryTSKcPXsW/v7+8PHxYW1W29rayHfR3biYYB4jMzMTbm5uMDY2xsKFC1FRUYGWlhbMnj0bJiYm8PX1xdy5cxEWFgYJCQlSgvM7gr7mc+fOYdiwYQgICMCsWbNIPFJUVIS4uDgYGBhg2LBhiImJAUVRXBtrGmvXroWZmRl69+7N2vy3tbUhLCwMJiYmHZ4HDU5iSklJCXFxcXz9HO/evQtbW1s4ODiQ2LCxsRHXrl1DeXk5Hj9+DG1tbbLJPnnyJAQEBGBlZUUItoqKCtjY2KBr164sH9Nnz56hsLAQHz58gJmZGdavX09e79WrFyiKYqmDP3/+DHd3dy7SoKKiAtbW1oiNjWURKA0NDXBycoKWlhaMjIxQXFyMvLw8qKqqYtSoUXzjbiYxtWnTJuKVaGlpyZXUot/LOc4vXrzAyJEjWcQUJ+hnn/nZyMhIyMjIAACmT58OGRkZCAkJscbh69evcHd3J+NFg3lvX79+jbdv37K8f969ewd9fX1iSP7lyxfMnDkT9+/fR3NzMzZs2ABFRUVyHJpApM+PuYehLR2WL18OV1dXGBgY/O2k4v3792FjY4PQ0FC8e/eOZzxD/1569erF5TF55swZqKurs+YWfjFRdnY2zM3N4ezszKVOvnjxIubOncu3PO7Ro0e4desWK+lKd2V8+fIlvnz5gg8fPhDbEV5xHF0psnr1avL9ly9fRu/evaGlpYXExEQkJCTA19eXpXIvLy/Hzp07ISMjg6lTp5LjdTTmKSkpOHLkCM/EJI03b97AxsYG27dvx+PHjzF+/HjY2Nhg8eLF5Ld08uRJLF++HHv27PmlxM/Zs2cRFxdHlGB2dnZQV1fH3bt30bdvX6xbtw61tbUoKipCUVERKioqCHHJ/N11dtz7PdFJSv2mePnyJXx9feHr68uVRQT+mQ5jnKDbkPfo0YPVjppzQuN3Lq2trTh8+DDJ6hUWFkJNTQ2TJ08m8uz4+Hiy2aflxzToTeqgQYPg4uJCXqcnvUOHDkFfXx+fPn3qMICnzcO3b9/Oc5M6f/58dO/eneV/wQRTaeXp6cmTmALaCZzZs2eTGnzm34D2QE1RURHnz59nnZutrS1MTEx4EhhZWVmsRZ0XmMTUkCFDyOv0OHGODX3/bty4AUFBQb7E1KdPn7BlyxbWmDHvdVFREWuBPnDgADQ1NeHv70/uaWVlJfz9/eHm5sb13DAX7l27dsHCwoInMQW0L66bNm1ilRz+rNz7R7h37x6OHz+ODx8+4ODBg1zEFN2B0snJCY8fP8a7d+8wYMAAVidKzmt7/PgxFBQU+BJTHz9+RHx8PAkeKisrWS2hOVFWVob4+HiIioqyTD45M1m8fouFhYWYMWMGi5hifg9dJpqTk0OOwdwUcuL9+/eYMmUKREVFCTHFPCZNeNGKOebGpa2tDe7u7pg1axa+fv2KmTNnwtXVFWpqaujatSuWL19O3nvq1Cmesnjmd9HElLW1NcLCwsgzz1ly2YlO/C5IT0+HoKAgRowYwUrm1NXVISYmhuVp87siLi4OEhISOHv2LM6fP4+1a9dCWFiYtf58+/YNT5484ZlEYP4uz5w5QzpgccY6JSUl8PDwwKRJk8jnmJ+9fv06JCQkMHPmTCxYsAA9evRASEgICgsL0dzcjIMHD2LIkCHw9vbGtGnTeJLcvxuOHz+OXr16Yfbs2Zg3bx48PDxgbW1Nyk4+ffqEHTt2wMnJCU5OTiySjXNNzMzMhJqaGmRkZMjn6bX53bt3EBQU5NlNlxc4iSkVFRUsX76ctSYz7w1NONra2pIkBf3du3btgpOTEyHKkpKSMGTIEIwbN45LKRwREUEayTBx48YNmJiYkDXi6dOnWLBgAQ4fPsxaB5nd5CorK1nlO7NmzYKlpSUSExPJuVdUVCAkJAQHDhyAra0tadZy9OhR2NjYYOTIkaR0kBNMldu7d++QnZ3Ns2MtZ0dpJl68eIFRo0ZBRkaG1ZmOH169eoXBgwcTHx/aV1VHR4fEFWVlZfDz84O9vT1fr68lS5bAxMQE+vr6UFBQwN69e1FVVYXi4mKIiooiMjISx48fh5+fHxwdHcln161bBzc3N1RVVSE6Ohpr1qwh63BGRgYoimIRbDNmzICLiwvLg/PvxnK3bt1CeHg4634/fPgQz58/J8rJ4uJixMbGQkNDA5GRkfj48SOePHkCPz8/ODs7k+9mnsPJkydx4MABbN++ncRvOTk58PDwgLu7O0/rC4B7DxMdHQ09PT2IiIjA2NiYdNR79eoV2VtpamrC0tISDg4OPIn7w4cPQ05Ojqfn1o0bNzB27FgoKyvD3d0dY8eOJWNB34Pm5mbs27cPEhISWLx4MetcmeB1D3iRVw8ePEBMTAzGjRvH2rtFR0fDxsYGCxcuRHV1Nc/x+Xexb98+KCkpYdKkSSzVIu0rqqKiAoqiYG5uDklJSYiKikJNTQ1+fn7kvZ0x4e+NTlLqNwaz0xI/2fA/DX5tyDvatDJfLy4uxp07d9DY2Ahvb2+MGTMGLS0tKC0thZKSEiiKIpt75ueWL18OISEhfPjwAadPn4aSkhKrUxnQHiTwI3NoNDU1YdCgQaS8oLa2Fn/++SeWLl2KNWvWkKBo5syZkJSU5PKy4rwmfsTUp0+f4ODgAB0dHb5ZiIsXL6JXr16sDUdzczPS0tLQo0cP2Nvbk3KmlpYWPHnyBJqamhg5cmSHQQzwL2JKQ0MDQUFBrL91tNgwiSnmQltaWkoyELw+P2/ePOjp6UFGRgaTJ08m2bElS5bAzMwMBgYG6NOnD2xtbWFubs631I4fMcXcPJSWlsLGxgbW1ta/jIiicfjwYZibmyMwMJB4dWzcuJGLmHry5AksLCzQq1cvqKqqwszM7IddDZnEFNMXprS0FM7OzpCVlUVLSwtmzZqFKVOmEPLmR8SUmJgYiyRmgt/48CKmgH+p2HR1ddHc3Iz9+/cjNjaWlI78DDFFd4bklf2lr/vNmzdEwRAdHQ1LS0t069YN/fv3x759+1BdXY3IyEhSIvr69Wv07NkTo0aN4inPZ34PTUw5ODjAz8+vs6ytE781WltbsWPHDnTr1g36+voIDw/HpEmTEBwcDFlZ2d/S1JyJ79+/w9/fH8uWLSOvNTQ0IC0tDUJCQhg3bhx5nTkXcM5NPyKmSktLSXlGU1MT19rx9u1b7Ny5k+VddffuXcjJyWHAgAH/k2UZBQUFMDQ0JCVXhYWFUFBQgJSUFHR0dEjyiJ7j+ClBaRUBXY4mLS3N6koFtCs41NTUCMHSkQcgDeZ74uLiICAgQHwkaTDXxZSUFHh7e8PR0ZHEPW1tbYiLi4OJiQkKCgpQVVVFfAppNDY2slRjYWFh0NHRYfkD5uTkoGvXrjhy5Aju37+PwMBAVgk7s7wTaO/m6O7uDlFRUYwcORJHjx4lDTqsrKwwaNAgbNiwAc7OznBzc0NbWxvs7OxY1ggpKSmwtrbukJjiBeaYREVFoXv37h12DX7x4gX69OkDf3//Do+bmJgIIyMjuLi4sBRK6enpcHBwgJiYGKysrH7oxRYbGwsZGRmcPXsWtbW16N+/PyQkJAhpnpWVBTExMRgaGsLZ2ZmV9MnPz4esrCxsbGxYFhzZ2dno0aMHqZJg3osvX778MrU7059v3rx5kJKSgpaWFszMzMgcQBNTQkJCEBMTw8iRIzFkyBCuElkAmDt3LpSUlBAYGAgNDQ3Y2tri+PHjANpJNk9PT3h5eZFySX5Yv349JCUlcenSJVIBoampyfKxOn78OFJSUnDu3Dm+VRFz5swh5Z/8xoyzqx3zeduyZQsmTJhAmmjNmTOH/O2vqtRaWlowZMgQiIiIwNLSkus7o6Oj4eTkhOnTp3NVjPy7SE5ORs+ePZGSkkLmPeb5+/v7o1u3bti8eTNevXqFP//8E/fv38eLFy9+icVHJ/4z6CSlfnP8Dp2WfrYNOT1h0pMRJzllYWFBSgC/ffuGiIgIHDp0CM+ePWO999atW5g4cSIxyvv06RMmT54MBwcHrF+/Hs3NzSgsLERgYCCCgoJ+yHwPGTIE4eHhuH79OiZOnAgfHx+oq6vD1tYW/fr1Q1NTE+l6wXnevK6PSUydPHkS3759g5ubG5c5NA36358+fSK118y/V1VVwcLCAsrKyrC3t2cFmgcPHoStrS3Cw8NJFzt+qK+vx/79+2FkZESygXTpHq9JmRcxVVxcjLKyMri6usLY2Jjn9aSlpUFDQwOJiYnYuXMnxMXF4efnR0oMcnNzsXDhQsyaNQtbtmzpUAbMeWwmMfXixQvU1dXBxcWF79j+Ozh48CB69OiB5ORklkEs0N7BhiammFmf48eP48yZMz8sq6Tx+PFjKCoqwt/fHxUVFSgrK+O6nmnTpsHKygoLFiwgwUVHxNSKFStAURSSkpJY50WfJ7/PMokp2mPK2dkZBgYGaGpqQmtrKwYPHgxLS0usX7/+p4ipqKgoUBRFvNZ4vZf2nVm0aBEp4bt16xYJ9GgMGzYMEydOJPf35MmT0NDQwNixY3/YSr6hoQFbtmyBp6cnKxPeiU78rrh16xZCQ0Nhbm4OFxcXREdHEzLhd0ZjYyOMjIxY5BPQ/hukPSMnTJhAXu9ovuZFTPn6+iIjIwPe3t5kblqzZg3x4WppaUFZWRkoioKAgADLXwloLxeSlZXF4MGDf+tSPYB7vrx27RrGjh0LoL00W0tLC2PHjkVubi5pN89PpUGD9kdkzq+0UXWfPn1w8uRJXLt2DQEBAbCysuIyI3/27FmH48Z87/79+/kqb5YvX44BAwbA1NSU+GfSJMerV68gLy8PDQ0NqKmpsbr9MTFr1iz07t0bvXv3hrS0NNTV1bF69Wry9ylTpoCiKGhoaLD8NzmxZMkSyMjIIC0tDffu3YONjQ309PTw+fNnNDY2Yt26dQgKCoKNjQ0GDhxISsH79OmD2NhYlor56NGjf4uYAtrJpnHjxv2UOq2wsLDD305rayu2bNkCKysryMnJcZWvf/78GTt37kRCQgJSUlJYMQtnfBoUFITDhw8DaI8lmE1q6Bjn06dP5JyWLVvG8lfy8vJC165dERISQuwI4uPjWeWD9Hcx8e+qVpiJsCtXrhCD7dTUVAQEBEBSUpLEzSUlJYiNjYWJiQmrlI05bgcOHICioiJJDBw6dAgUReHUqVPkPSdPnoSxsTGmTJnC91oaGhowdOhQLmXSjRs3oKGhgVmzZvG8Hl5xekREBLy9vbnGqqGhAdnZ2XzHBAAWL14MSUlJHDt2DCkpKZg0aRLExcURFRXV4Xd2hMrKSkyYMAEqKipYu3Ytq/S1ubkZkydP5ioX/ndRVlYGd3d3VjdwoN2CJS8vjxCh/v7+0NbW5qkq6ySm/jfQSUr9D+B36LT0s+RYVlYW/P390a9fPxw4cIAQLM+fP0eXLl2wefNmlJaWYsGCBTAxMeHK9NHdxHR0dIjJJP35OXPmQFpaGtLS0tDT0+uwVr+goIAEQFu2bIGRkRF69uyJQYMG4dixY2hubsayZct+mIniBJOY8vLyQu/evaGurk6CZ4C/51JdXR2GDx8OFxcXVie/8vJyhIaGYv/+/bCwsMDOnTtZi3diYiKsrKx+mpiix3TRokUQExMjZNGPiCkhISGEhobCzs6OZXLIeT0XL15kZSxfvHgBCQkJ+Pr6dihr7gicxJSVlRVGjRoFExMT1tj+qrLVJ0+ewMjICLt372a9zjw+TUwtX76cpyH3zy5yjx8/hpKSEry8vODg4ECuh1nXvnjxYlhYWGD+/Pk/JKZKSkpw8OBB8tldu3ZBTU0Nq1at4kkIM1FYWIiZM2dCXFwcsrKyXGPb2NiIiRMnwtbWFmvXrv0hMfXmzRvMnTuX1X2wrq4O5eXluHTpEiGIdu3ahS5dumDZsmVEfUajqKgIc+fOhaSkJJ4+fcr6rqysLKioqPwUMdXY2MhX6diJTvyO+N0DZX7eN2vXroWFhQXOnTvHen3VqlUYOHAgJCUlSXfeH208OUu+goKCQFEUmZtaWlrQr18/4slCv//06dMQEhKCv78/q4Mn0K6Y6tq1K8LDw39L5WRH5Zm0P9SQIUMwZMgQMq96enqCoihYWFj8cB0cOXIkJCQkcOLECfJaTk4O8f+KiopCREQE2ZDTz+GcOXOgrKwMUVFRuLi44ObNmx3GDfz+n5CQAGFhYZw/fx6vX7/Gtm3b4OrqCjs7O6L6fv36NXbt2oU9e/bwTFwdPXoU4uLiuH37Nurr61FSUoKRI0fC2tqaRUzl5eWxOr8xj9HW1ob379/D2tqadNm7cuUKevToQTwumWCqO2JiYiAtLc3zXiUlJfEkpjp61o8ePQpNTU1YWFigtLSUL+HET53G6/3fv39HYmIi1NTU4O/vTzoV8jsPzvu0ePFirFy5EkpKSnjx4gUuXbrEalJTX1+PBQsWsPYehYWF6NKlC/z9/fH582d8//4d4eHhSEhIgISEBMaMGYOSkpKfGpN/B5zjkZeXR7oOAu1xRXBwMCQlJckz9/HjR8TGxsLAwABLly7lOubChQtJx7nk5GSIiYkRcq6mpobEgVeuXOFLxtJwdnYm5XpMzJgxAz4+PlwEKr/nYcWKFZCTk8OtW7e41GZBQUHIysri+Tk6Wc7szFdeXo6NGzeiV69eLDL/R89icXExSktLyZ6spqYGo0aNgoODAzZv3sy6lubm5g5VgH8HZWVlMDQ0ZBHt27ZtQ2hoKCiKgoyMDIKDgwGAlEX+aK/Uid8TnaTU/wh+h8DqR+RYfn4+hIWFMXv2bLi6usLe3h6RkZGkvI5WeOjo6EBKSgr379/HrVu3sGfPHhw4cADv3r0j5Ff37t1JlxYaNTU1KCoqQlJSEk+5Kz2BHj9+HAoKCli7di1RPxUUFBD2nJ6Ap02bhuDgYNTX16OtrQ15eXmkq9jkyZO5WHkaTOLLzMwMTk5OPAmcxMREzJ07FzExMaT0saysDN7e3rCzs8PYsWOxb98+uLq6onfv3mhoaGC1E2YuFIcOHfppYorGlStX4OHhAQMDA5LV6ijAvHnzJrk/vFpCb9++HVFRUbC0tGRlgIB20lJSUhJBQUE8fTv+amnAnj17ICEhARsbm19OSAHtmx8NDQ28ePGC53nQr23fvh0URSEmJoZFpvzM9TDx5MkTiImJQVtbm3U9zOMsWrSIEFM/KuWj0dzcjKamJkyePBk2NjZYsWIFOc+OiKmJEyfC3d2ddS70c9DY2Ijx48f/NDHFvA7aqFVfXx9CQkIQERHBkCFDUFpaipSUFKI+o8mjrKwsjBo1Cjo6OqzsPPO7Tp069dPEVCc68b8E5nP7uz3DzLnp8+fPrDL5/Px8eHt7Y8CAAWSzX1lZieDgYGzZsgVLliyBlZUVS01RX1+P1tZWMo/z29RlZWVh+vTppPyKOe/n5eXh6NGjZE46ffo0unTpgkmTJpGNMH0sunTjdwOt/uFMyDGvs6ysDObm5qQBS2NjI8aMGYMjR46wxpTzmWH+Pzw8HCIiIixiirZCoMuBWltbidIhPT0dOjo6OHHiBPLy8mBpaQkzMzNWrPUj0PdryJAhmDx5MutvmZmZMDExgZOTE0/fSM7vWLFiBSwtLVmvf/jwAQEBAVBUVGSV+zGPwbk2l5aWwtzcHHV1dcjIyOAiXQ4fPswyin/58iUGDBgADQ0N3L9/H4cPH0ZMTAyWLl1K4kPgX8RUR+bnTCQlJZGOsnTp18+OK/Oabty4gZs3bxIvrZaWFhIfhoSEEGKKl2qMeZyjR49CRUUFT548wfDhw+Hr64uePXuyyLri4mK4uLhwKZ6ePn0KZWVl+Pr64tu3b+S42dnZEBcXx5gxY1hj+qvnNubxVq1ahdGjR8Pc3JzV8RpoJ6b69u0LGRkZQh6WlJQgLi4OsrKyLBKrra0No0aNwtKlS3H//n3Wc9La2orNmzdz7QlaWlpY92L8+PHYvHkzgHavLQcHBy7D+k2bNsHOzo5vPJmTk4Ps7GziEQYADg4O0NTUxJkzZ/D27Vu8ffsW/v7+cHBw4PsM1dXVQUNDg3RIpPHlyxd4eXlxKVo5QY9xZmYmLCwsYGJiAnl5eWJmXlNTgxEjRsDe3h7btm37oZ3Fv4OysjIoKytj3LhxuHDhAkJCQmBiYoJJkybh3LlzSE1NhYqKCiHgOL3pOvG/g05SqhN/CZzkGHNxSE9PJ9lRoH2xcHBwwPjx40kZ2Y0bN3D69GkUFhZiz549UFRUhLa2Nnr27AkzMzM8efIEhYWF6Nu3L6ytrVmts3mREpwTT05ODnr16oUdO3bw9Zp68eIFoqOjISoqioKCArS1teHTp0+wtLTEwIEDMWTIEAgKCnZI/tDXzez6wTy/uXPnQkVFBcHBwRg8eDB69OhBgsMvX75g6dKlcHFxgbm5Ofr06UMyln5+flizZg3X2ALt0uK/SkzduXMHAQEB0NfXJ0QHr8mavoaXL1+S62AuMvHx8ejevTtCQ0MhKCgIExMTLlPaly9fkq6KvI4N/LXSgL9SJvdXsXz5ckhLS5P/8wqanj59ivfv32Pr1q0sY0/mOebn5+PKlSs85cKceP/+PU/TUyYWLlz404opgN12eeLEiXB2dsbq1at/mDFlNgfgzCwD7b/ziIiInyKm6PF49OgRFBQUMHHiRBw4cADPnj1DdHQ0NDQ0oKenh8LCQhw5coSozxobG/H161ekpaURopvf+Z44cYInMfW7beQ70Yn/C2DOccuXL4e1tTWMjY3h5ORE5qWLFy8iKCgISkpKRNFqZGQEoD2poKurS8jndevWITQ0FC4uLpg5cyZPPxZev2VmwqmtrQ0+Pj7Q0dFBamoqmZOysrIIMfUzc+Z/EydOnECPHj2ITx49T9PrAjNm8fLygoeHB+7evYvZs2dDV1eXb2nypk2beK5Bo0ePhqioKE6dOkXuKV3KN2rUKPK+1NRUrFy5ktVyvqmpCU5OTjA1NcX58+f5bvJ4KXnGjh0Lb29vrnV71qxZoCgKWlpaLCU8r+Pt3bsXZmZmZG2gX7927RpERUWhrq6OAwcO8DwGAEyaNAnx8fF4//49VFVVMXXqVEhISLCUIw8fPoSfnx+LAGhra8OFCxfw9u1bzJ07F7Kyshg2bBhsbW3h4uKChIQE8t6kpCTY2dmhT58+LAK0oy5upqamsLW15bqun8GsWbMgJSUFZWVlCAoKYsqUKaSkjjZmHzhw4A/9fC5fvoyJEyeS+71lyxaoq6ujT58+5D3V1dXw9/eHu7s7z3v/5MkTKCoqws/Pj6WMoZ+viIgIvvf43wFzvFatWgUJCQkMHz4czs7ORJ3HRHFxMZydneHn58dS/qxevRqvX79GQUEBIYjS09MhJCQEiqJY3pu1tbXw8fFhETxtbW0oKSmBnJwcRo4ciVGjRqFnz54kvn3+/DlsbW0REhJC7A2+fv2K3r17E1KYPg6NWbNmQVJSEqqqqlBSUiI+pwDg4+MDDQ0NiIqKwsLCAra2tjyTx8zjzpo1C/7+/lwx9+zZs+Hh4YF+/fp1+PydPXsWPXv2xNatW/Hx40dSPUCXDVZXV2P06NHQ19fHrl27+B7nVyA3NxdiYmLQ1NSEmZkZLly4QObLiooKmJubs8YL+P2VyJ3gRicp1Ym/DXoyvX37NjIzMxETE8PqkNLS0oLVq1cTxRSzxnjXrl0QFBREUlISysvLkZKSAlNTU2LSnZ+fj9DQULi6urJK3ToKNhsbGxEaGkqURvX19Xj9+jXi4+Oxb98+FBYW4sWLF/D19YWFhQVXZ68bN25AWVkZ3bp1IxnKH30n81pp7N69GyoqKqRr3tGjR0FRFCiKwqFDhwD8S43DzJbScvFXr17hzp072Lt3L3bt2sUyOWcSU/zMz5mLTGpqKhYtWkQ6UtDZ5I6IKYBNVNy9exejRo0iaq9Xr14RI3Om+T3QLo/mRyD9bGkA5yL5Tywsx44dQ48ePYjHGb/zjYiIAACehFRMTAw0NDRgZmYGERGRnyYLmQFERUUFvn79yjruggULfkoxxXwtKSkJs2fPhqSkJOTk5LB27doflvLRf2OSU8zzaGhowLhx47iIKV5+aY8ePULPnj0RExPDdf/p37atrS0aGhqwY8cOCAgIYP78+SySmz6PW7du4cCBA1i/fj3evXtHxotJTPEzP+9EJzrx67BgwQLIy8tj//79uHnzJrS1tWFubk5UB2/fvkVubi6io6OxdetW8ludNGkSAgMDUVNTg3nz5kFaWpr42xgYGBDlyl9FY2MjgoKCYGFhgZSUFBYxRfsi/urSkV+JY8eOwdbWFpWVlTh+/DhLNfr+/XsICAgQpcrx48dha2sLeXl5aGtr8y0Ta21tha2tLeTk5Hh6HNnb20NLSwvHjh0jnz19+jQoisL48ePx/ft3iIqKgqIoEjvRoIkpCwsLFrFFIzk5GeHh4cT/kcbWrVuhp6eH7OxsVuJk79698Pf3R2xsLFnX+W2KCwoKICIigtmzZ7OOffnyZQQFBWHjxo2szzLH5OHDh1BSUiJk06ZNm9CtWzdSmgW0E4KBgYHw9fXlSaxt374d6urquHPnDoB2tXq3bt1gZmbG6oC7e/dujBkz5qe7uJ0+fRru7u7w8PAgdgf8YhzmNd29exeqqqrIy8vD8+fPcfz4cUhLS2Po0KGoqKhAY2Mj9u/fDw0NDcyfP5/n8YB2pZCWlhZERESwYsUK8v0zZsyAmZkZzM3NMXDgQNjZ2bEauvCK6548eQIFBQX4+fmx4vvTp09DSkoKoaGh/5jH4+vXrxEZGUnUa3V1dRg8eDAxF2eivLyc3Bd6rFtaWpCRkQF1dXXs27cPdXV1qKurw9SpU6GgoID09HTU1NTg2bNn8PPzg6WlJc8xuHv3LqSkpCAgIMD6jQHtnerc3d2hr68PZWVlWFpasvzTmPf3w4cPsLS0xMOHD/Ho0SNs3boVAgICmDlzJnnP1atXkZmZidzcXJayncajR49w584dcvwLFy7A0NAQEyZMIPuRmpoa9OvXjxjQA/x/g5GRkeT73759Cx0dHa6Y+Nu3b5gwYcIPjd9/BcrKyng2sKioqICLiwtPU/1O/G+hk5TqxL+FtLQ09OrVC0pKSujRowdXsNna2oq1a9fCwMAAUVFRaG1txdmzZ0FRFCsrB7R3JdHQ0CCb6du3b2PgwIHw8PAgJowdoaGhAb6+vpg+fToePnyIKVOmoHfv3lBQUICtrS2Rql65coW1UDLl/jY2NjA1NWW12KWv42dQU1OD+fPnk6zBqVOnICoqig0bNmDKlCno2rUrq2sf0L6w9+/fH6qqqrh//z7S09MhISEBLy8vqKmpwcfHh9V58MCBA7Czs0NoaGiHm/OZM2dCU1MTS5cuxZAhQ6Curg59ff0OPaY4kZSUBFtbW5iZmbH8oh4/fkyIKV7m95wEx68oDfiVePPmDcTExBASEsIqR6WfhaqqKoSEhJBnlEneAO0d+mRlZUl2OjY2FgICArhx40aH38sck2XLlsHT0xMyMjKYM2cOy59lwYIFsLS0xMKFC8n94ofFixdDQkIC+/fvx5EjR+Dj4wMjIyOsWrXqh4op+vWzZ88iIiICLi4u2L59OyFsmcTU+vXreW4kCwsLIS0tjYEDB7KOywzgdu3ahV69epHfRXx8PMTFxUmgTp8H/ex7e3tDWVkZbm5u2LVrFwm8Tpw4AU1NTYSFhf1PtHfvRCf+V3H58mVYWloSRWx2djbJVCspKfEsWXrx4gVmzZoFMTExFBQU4NGjRzAxMSFrRFZWFkRERMg8wOlZ1VFCgp5PGhsb4efnx0VMZWRkQEpKiuVn87vh9u3bkJeXR0BAACiKQnJyMoD2OVReXh4TJ05kkTVfv37FgwcPWN21mGNEl/I1NDQgMDAQSkpKLGKqubkZw4YNg5ycHHr37k3WsdbWVpw/f574dFVWVsLY2Bj6+vq4f/8+a71oamqCrq4uRo4cybqWqqoqaGlpQUZGBiYmJsSOgEb//v2ho6ODo0eP4uPHj6isrETfvn2xZMkScnxmgub69es4fvw4bt++TRqPHDt2DF26dEFkZCRycnLw7Nkz+Pv7IzIykhyDM37YsGEDZs2ahXnz5pHXSktLMW3aNFKyNG7cOHh6epJmLj4+Pqz1pKmpifgtAe3Plri4OOLj4xEWFgZVVVWu+JXzen5VFzf6mqKiorgMsq9evYqePXsiLi4OQLvHVHZ29g9jqkePHkFbWxuOjo64e/cugPZxPHHiBGbOnImpU6eSpkIAm/j48uULKisrSUL18ePHkJeX5yKmjh8/ziL8fiUyMzNBURSUlZVZcXpzczMGDx4MKSkpVqkl8+80EhMTsXr1anTr1g1aWlo4fPgwWlpa8OzZM0ycOBECAgJQUlKCqakp3NzcWJ0LmR62BQUF0NDQgKKiIsLDw7mI4Y8fP+L69etYsWIFkpKSePqnrV27FmFhYZg0aRLLm3P37t1cxBQTzPscHR0NGRkZyMnJQUdHB7du3QLQHjNZWlqS0lm6FI/+fub9YR6vqakJ9vb22LNnD75//w4lJSWMHz+e/O4SEhLIvP7fJIHKysoQGBgIOzu7TmXU/wF0klKd+MtgdtkbO3Ys9u/fj8+fP2PHjh2wsLBAv379WB3LWltbsWnTJrL40vXvI0aMIGbkQHvLXmNjY9bCdvv2bXh6eiIyMvKnzo1uwyoqKoqBAweSDmW0jJUJfovlxYsXYW9vj5CQEK4SNX5jwfz306dP8fr1a7x69Qq6urpE7n3mzBmimGIqdFpbW5GZmYk3b97gypUrkJOTI0H7zZs30atXL5iammLdunXkMzt27IC7uztfwqKgoADKysosouP8+fNwdHSEgYEBCXI5Mz+ci8udO3fg4eEBERERLmPQJ0+ewNjYGA4ODh0qhP6d0oB/EsnJyRAUFMTQoUNZG6zi4mL4+/vDycmJr+pr1KhRJMt47NgxiIuLE+KwsbHxh4HYggULIC0tjUOHDuHw4cOws7ODi4sLy8hx0aJFUFZWZmW0mGhra0NpaSmMjY1Z/mvNzc0IDw+Hmpoai0jiFzgcP34cPXv2RFRUFKZOnQonJycEBwcTgq2hoQETJkyAjo4O8Utg4t27d7CxsUFwcDCXco75na6urujXrx/5P13SS4N+9ulrefnyJbp16wYrKyts3ryZBMbHjh2DsbHxD8m6TnSiEz8PzvkhLy8P69evB9BOWktLS2P79u0oLy+Huro6rKysWCVjTU1NiI+Ph4eHByG1b9++DVVVVQDtG0mmT0tdXR0SExO5Gh+8e/cOf/zxB/k/c21gqgNoYio1NZXMcb+6DfmvwNGjR1nqrRkzZqBLly5wd3cnr69btw7R0dF8jeVpMNeVFStWYOTIkWSerqurg5+fH5SVlXHnzh1iCTBs2DA8e/YMra2tLL/E2tpaNDQ0kM32169foaqqCnt7e671nOk5SKOlpQUxMTHYsWMH7t27hzVr1kBcXBxhYWHYtm0bWlpa0L9/fzg4OEBcXBwGBgbQ09PjuRmOjo6Gjo4OFBUV4eTkRHwIgXa1kb6+PpSUlKCmpsbqssc5Tl++fEFISAgoisKwYcNY76mqqsKhQ4fg7++PYcOGYdGiRWhubsbXr18RFRXFUnQB7SXuRUVFePPmDfT09Ej8dePGDYiLi0NNTY0o6ulutjT+nS5unGPz7t07DB06FBRFITQ0lNwPegzi4+Ohra3N1YzlZ4gpc3NzjBs3rsP4jRkDLVu2DN7e3tDU1MTQoUNJvEKX8vn7+/PsuvZPEFOTJ08GRVFISEggxDTQft3Dhg0DRVF8vb4WLlxIEnm7du2Cq6srVFVVcfjwYXKuDx48wMmTJ5Gfn8/TogNonyOZZaVqamoYPnz4Dz3GmPemtrYW8+fPh5iYGLy8vFjvq6urw549e9CjRw+Wyg/gtrrQ1tbG2bNnkZeXh379+kFSUpLE/wUFBUhKSsKkSZMQFxfHRYyVl5eT+5aVlUXm72XLlsHX1xfy8vKIjIxkWXsMHjwYCxYsIN5//2mUl5djxYoVCAwMZHnPdhJT/9voJKU68bdw+/ZtGBoawt/fn7SxbmlpwcGDB2FnZ4e+ffuyiClOJCYmws3NDQMGDEBlZSUuXLgAAQEB4rvEnHD//PNPvq1k3759iwcPHrBK2fLz8wmDT09Q06ZNQ2hoKDE1Z06iqamp2L59OzIzM8mkm5OTAwcHBwwaNIgYFXp7e/Ps2AK0ByG7d+9mLVqnTp2CjY0NUYPcvHkTEyZMwIEDB9Dc3Iz58+ezsnMtLS2Ii4sjBNzbt2+hqamJQYMGISwsDBoaGizFVEedxq5fv44ePXqw/Heam5sJAWFjY8Mlq+Y0m6WDwqdPn6J3797w8vJikSZAu0x+6NChfIOOf6c04J9GS0sLyUQpKyvDz88PPj4+sLOz69Bgvba2FlZWVsjIyMCtW7dYG62mpibExsZymVsycebMGejq6pIN3dWrVyEgIAArKys4Ojqyuqns3Lmzw0W2trYWZmZmhPBjnquZmRl0dXWxYMECVtDGxMOHD6GlpUW6ENbU1EBcXBza2trw8/Mj5/j9+3dMmzaNp3QaaCeQ/Pz84OvryyKmmM+Uu7s7y0uBs1x048aNmDZtGoB2JZumpiZGjBiBvn37QlVVFdu3bycbB86NbCc60YlfA6YPTFFREZqbm+Hn54fo6GgA7XOOu7s7unfvjr59+7I+29DQgK9fv2LlypVITU3FvXv34Ovri61bt0JERAQ7duwg771x4waGDx/OWrtbW1uxfv16yMvLs0qQ+CmmgoKCoKqqStal361so7y8HIKCgujduzc+f/6M5uZmooiQkJDA2LFj8enTp7+8kaI7EWdkZLAUzI2NjfD394eIiAiCgoJgZWUFY2NjtLS0EIUUALKZoxXstA/P169foaamBnt7e572ALw8PEVERAip8f37d2IX4OHhgdWrV2Pbtm1ITU1FYmIiq3SKxqpVq6CgoICrV68CaFd4CwkJwdfXlyjBPn78iGfPnuHGjRssgoDX/X706BHCw8MhICBAkorMmI/Tf4fzXGiFCf16WloajI2NSTx0/vx59OvXDwkJCWhtbYWbmxtMTEzQ1NREzm3+/Pl/q4sbJ5YuXYodO3bg1q1bGDduHLp164aLFy+SawLa/aCsra0JCflXcP/+fVhaWiIiIuKHJfELFy6ElJQUMjIykJWVBQ8PD0hISODjx48A2okpZWVl2NjYEKXbr0BHceGoUaPQq1cvpKamskjF5uZmLFmyhOfYFhUVQVtbm1hp0AgODoa8vDwSExN57l04z+PChQvQ0tLC/PnzyfvPnTsHdXV1jB49mpR9uru7sxSEvK6nqKgIcXFxoCgKa9euZf2tvr4eCQkJcHV15fm879mzBwkJCUTVR2PgwIGQkJDg8tiiQc+jZWVl8PDwwB9//IG9e/eCoihShnjq1ClS0klXFNB7FzU1NZaZ/X8aDx48QFBQEGmKQZ9bJ/630UlKdeKnQU+I9+7dw9GjR+Hg4ABhYWFWN5jm5mYcOnQIzs7OcHd359o8MifkgwcPwsPDA/b29sRfivM9vD7L7LJnbGwMJSUl2NvbY/jw4VyfefHiBebPnw9RUVFC0DAn9pkzZ0JGRgaampowNDREaGgoUWScPn2aBBzGxsbQ1NTkaSjY0NAAV1dX2NjYICkpiSyEqampoCgK165dw+fPn9GnTx9iLlpVVYWBAwdyBX5FRUV4+PAh6urq4ODggPDwcADtxJyEhATU1NRI15mOsqpfv36Fubk5Vq1axZqoa2pqYGlpiR49eiAsLIxrbIH27I+pqSkiIiJI8PTw4UN4eXnBx8eHi5hiHoPXufyd0oD/JB48eICpU6fCx8cHY8eOxZYtW3garG/evJmUhixZsgQaGhro3r07K7ipqKiAp6cnMasH2GNbUVGBly9fErl9dnY2JCUlsW/fPty6dQvS0tKwt7fnKlfl1VUIaN+EeHp6wtfXl/VeABg6dCg0NTUxZcoUnoq+hoYGPHz4EJGRkWhqasL79++hqamJSZMm4dixY5CWloa/v/8P1YI0mMQUs6SztbUVHz9+hL+/PzGmZW4Srl69iqdPn+L58+d49uwZamtr4ezsjDFjxgBoz1hLSEhAX1+fBPa/2+azE534v4Dk5GQ4ODgQY16gvfRJV1eXqEG+f/+OoUOHspJFzLn/0KFDEBERwePHj9HY2Ah7e3tQFEWUpfQx/P390b9/f57d0lasWAFDQ0NChAG8FVPNzc0YMWIE3rx584tH4tfh8ePHUFVVJZ3K6BiC2amMSQT+aG47ceIEVFVViXkx7U3JLB1fvHgxIiMjMWXKFBLP0GM2f/58SEhIYNOmTYiKikJgYCDU1NTIul5RUQEtLS1oamr+lFF1ZGQkS8luaGiIfv36kWNTFIWUlBTyd+Z9fPfuHdzd3ZGRkQGgPWEjLCyMsWPHwtTUFAEBATw9wjjXw5qaGtJpmT5uWFgYJCQkCNnFScxxnktJSQn69u3LMqsG2uNMXV1dHD58GF++fEGfPn0wc+ZMtLW1IT09HcrKyiTpQyv//04XN/rvNE6ePAlxcXGiWnn58iUGDRqEHj16ICcnB2VlZfj27Rt69+6NgICAv70m0rYVoaGhfJNOHz58gL29PS5cuACg/T6JiooSRT/9TD948ADBwcG/LMHIPM6xY8ewbNkybNq0iVVpMGzYMIiIiHARUzQ4iYrPnz9DS0uLlM4yyTwDAwOYmpoiMTGR57GYaGhowMyZM+Ho6IgFCxawiCldXV3Y29vD1NQU2tra5DfIvJ4nT57gypUrKCkpQVNTE5qamrB06VKIiIiwqiLo7+IV7zc0NMDc3JxvJ72BAwdCVlYWWVlZHd6TBQsWQEtLC127duV6Njdu3Ei8sAYPHoygoCBIS0v/VMfJfxrfvn3jW8bbif9NdJJSnfhLyMrKgrq6OnJycnDu3Dno6+vD2tqaRdY0Nzdj165d8Pb2JlkUJpiTY2JiImxsbGBpaUkCyx8ZMwPti6KIiAi2bNmC4uJibN68GRRFITg4mLw3Pz8fHh4eMDY25jI1B9qVSIGBgXj8+DHKyspw6NAhWFpawtfXlywiN27cwK5du1iSV17E1Ldv39C3b184ODggMTERzc3NaG5uJrJrbW1tkk2jr4M+3rlz5whhRr927do1GBsbE8+Hhw8fonfv3pg5cybLA4kzMGN2H4qMjISdnR0rICwvL0f//v1x9uxZLpIPaDcEjYiIgLKyMoSFhTFx4kQi633w4AG8vb3h7+/P6kzC61z+3dKA3wHMc6qsrISJiQlR8ty8eRNeXl4wNzcnZaklJSXw9/eHvb09z+uZO3cuYmJi8PXrV1RWVqK+vh4+Pj5YtmwZuQdubm7Q0dHhUpUxx/bBgwd48OABCQqeP38OSUlJjB49GvX19ST4Hjx4MDIzM3ne58zMTKxcuRJfv35FYWEh2traMGjQIIwcOZI8+25ubpCXl0dYWBjq6up+Kujlp5iKjo6GmZkZ13xw4cIFUBSFnJwc8r35+fkwMjIiz8mDBw/g4+OD0aNHs579TnSiE78WFy9ehJeXFwIDA5GTk0Ned3R0hImJCbZv3w5XV1dYW1tzGQcD7fFBQkICSxH15csX6OrqwtbWFqtWrcKWLVuInw+vUi6gPXsfHx/PRUzR72tsbMScOXP+8Y5Pvwp0eZOvry/PTmXjxo3jS6xxzruHDh2CiYkJgPa5Py4uDlpaWpCUlGTFP0zQ4/zmzRuYmpqyysgeP36MiRMnQl9fn/gLff36FaGhoT+1Lu/ZswdOTk6oqKiAhYUFnJyciN/Qx48fceTIkQ4VDKdOncLHjx+Rn58PRUVF8uxMmjQJFEXB0tKSRThxjklcXBycnZ2hqqqKfv36ITc3F62trfjw4QOGDBkCaWnpH3rf/PHHH1i5ciXevHmDsLAwiIuLk/X1zZs36Nu3L1RUVKCkpARzc3MS11y7dg16enpIT0/HnDlzyPhnZGSgR48eP9XFjRcOHTqEDRs2sAzVgfYk66BBg9ClSxcoKytj0qRJsLW15Ul6/BXcunUL4eHhPM3a6e9VUFBAaWkpTp48ySLa6uvrsWPHDi5C61cq32fPng1paWn4+vpCW1sbenp6LH+tESNGQFxcHIcOHeLb2ZgJGxsblsqTHr9+/fpBS0sLhoaGxBuKM9nK/HdjYyNmz54NOzs7LFiwgCThb968iTVr1uCPP/7guW+IiYmBnp4elJWVYWFhgTFjxqCoqAjfvn3DsmXLICYmRpLPTPAa02/fvhE/OXqfwzxHLy8vLusSzuM9ePAAUlJSUFFRQXx8PFcJ5unTp7F06VKEhYVh2bJlrE6TvwM6k5T/d9BJSnXih6B/8KWlpRg+fDjxSGptbUVubi7MzMxgb2/PJZ9ldpbjBCcx5e7ujtDQUELCMHHjxg2WHPjz58/o27cvkbmWlZVBRUUFAQEBUFNTIx38gPYgmylvp3HgwAHY29ujf//+JFPS2NiIY8eOwcLCAn5+fixzRxqcWT4mKisr0adPHzg4OCA5OZlk5c6cOYMTJ07w7JZRVVWFsLAwdOvWjeWvdfXqVaioqJBszqJFizB8+HDWODAn4tjYWPTu3RtiYmIYP348Tpw4gcbGRgQHB8PGxgbDhw/Htm3b4OrqChcXF57BR1xcHMTExJCRkYFr165h/PjxMDc3R0REBAkKHz58CHNzc8yYMYN17cxz+VWlAf9J/GhRa21txaJFi+Di4kKyoocOHYKnpyfExMRgYWEBCwsLvmV/eXl5rI6MQHtGWltbm2SmKioqMHToUCQnJ/PtKrRgwQLo6enB0NAQ4uLimDFjBkpLS3H+/HlISEiQZ9fGxga6urqsDCzT80xYWBgHDhwg31NTUwMzMzPSLru+vh4jR47E6tWr/7J3E5OYun//PlatWgVhYWEuYvjDhw9ITU0lgTd9fhcvXoSmpibS0tLQ2NiIpUuXYuTIkZ0le53oxC8Ev01jXl4efH194efnRxRT7969g6urK2xtbREYGEjmOOYxPn36hC5duoCiKCxevBgAO3YYMGAAbGxs4OHhgYiIiA67egH8ian6+npMnjwZQkJCP9Xt9L8J5tzdUacyGRkZDBo0iLWxZ64VTJw+fZqYL6uqqmLkyJHYuHEjzp49yyrv8vPzI55gNF68eIFevXqxSsSB9g5i5ubmXN5IwM+tyzY2NqAoCm5ublzeRjR+5F+5YMECDB8+nMRH69evh5+fH2JiYview5IlSyAlJYWEhATs378ftra2sLOzw8GDBwG0dwoeMWIEKIpiPSvM5zYjIwMKCgpEHVVaWoqQkBCIiYkRku7du3e4dOkS0tLSWCq9kpISjBkzBlpaWhAQECDx6+fPnxEVFfWXurjRqK2thYaGBumOyInnz59jwoQJEBYWJmVWAO9k6V8Bswsvc2zev3+P8vJyeHp6YunSpRATEyOEFNAeE4aEhPA0Fv8VyM7Ohry8PK5fvw6gfZ5Zv3491NXVsXDhQvK+4OBg9O7du8NjMf2fxMXFucZ3+PDhuHfvHszNzVn+lzQOHjyItWvXssaIJqb09fXxxx9/EF87foq89evXQ05OjvxOw8PDISkpSZJ4ZWVlpJSP+XtkHu/Dhw949+4dic3q6urg5OQELS0tUobJ2aGzI3z48AG3b9/GokWLYGFhgYULF/L0ButEJ/5pdJJSnfgp5OXlwd/fH3Z2drh58yZ5vbm5GefPn4e5uTlcXFx+KHllgjlR0ht8Dw8PooZoa2vDnTt3QFEU4uLiWCTXzp078fDhQ5SVlcHY2BgTJ05EY2Mj8TNwcXHh+72NjY1YsWIF9PX1oa+vz/W31NRUWFtbw8bGhm/wsHfvXlhaWrKMxIF/tSbV0dFBcnJyh/4FWVlZWLlyJZ49e4YBAwZASkqKLCjv3r1DcHAwydpISEiwJOVMLF68GDIyMkhJScG1a9dgYWEBIyMjVFRUoLq6GitXrkTv3r1ha2uLfv36cW0o2tra8O3bNzg4OLDq2dva2hAfHw8NDQ1ERkYSb6yXL1/ybef8q0sD/hvgFwB//foVMjIyWLJkCXnt9evXSEpKwvr165Gamsqz7C8hIQFLly5lZUfb2trw+fNnBAYGon///tiwYQN8fX3h6OjIN1u5evVqSEtLk+Bs7ty5oCiKFUzPmzcPU6ZMwaxZs8g5MK/nxo0bSElJwezZs8l5AO1qht69eyM8PBwXLlzAwoULoa+v/7fbq798+RJBQUGQlZWFgIDA/2PvqqOqyr7wuiYoISFItyDdXdKtgFiU2FigAiJiKzZ2d2ICdndjjI7YiqIiiCDSzff74617fve+98AJmHFm3rfWrJGb55573zn7fHvvbxPjnkZubi4oioKoqCgWL17M2kenQNIeUUlJyZ8iVFwAAf6NyMzM5Kmgev36dXh5ecHFxYWlj1dYWMhXm4cmEu7fvw91dXU4ODiQVGfmPFNRUUGcQNeuXfth2h0/YmrixIno0qULT5Wrnwl0H9GRq7Rd1FylsqNHj0JVVZUsMp2cnDBkyBDW2E33Y3V1NQ4cOIAxY8YgLS2NnPPmzRtYWFjg0aNHKC8vx/79+3kca/n5+bC3t8f8+fN5ROHNzMx4qrv91ufcvXs39PX1yTj/RyIXRo0aBRMTE+L0CQoKYkWLMPuiqakJHz58gIGBAYuYKS8vx4ABA2BmZka0Th8/fozZs2fzndf379+PuXPnktRSut35+fkIDg5Gt27d+H5ndDQywInS6dSpEywsLHDo0CFyzP379zFmzJgWq7g111efP3+Gk5MTVFVV+Wo9PX36FOHh4ZCSkiJpm60RmcS8xtSpU6GgoIBVq1YBAEaOHAmKohAfH0+OqaiogI+PD7y8vNpME3TNmjWsanEAJ+J/xowZsLOzY0Vf/9Y21NTUYN++fZCQkICVlRVCQ0NhaWkJLS0tABzbytnZmXVObW0t/P39YWFhgfXr1/N8T46OjlBRUcGECRP4ang2NTWhpqYGffr0IY79kydPQlRUlBSzobMLvn79iu3bt5NnZn4j06dPh62tLWRkZODr64u5c+cCAMrKyuDo6AhNTU2Wg5tf39DXe/v2Ld6+fctyPCYkJMDExAQzZ84kY9TKlSt/egeAAP8OCEgpAX4T3rx5Ax0dHVAUxRLbBjiT68WLF6GiogIPDw8AzXsJuP9mHrdhwwaMGzeOJ1x21apVaNeuHebPn89TsWvTpk3w8PAgaWs7d+6Era0tHBwc8P79e5570CgpKcG6desgKyuLIUOGsPbV1tZi586dGDJkCN/UJ4CT+mdoaAh3d3ceIcF79+5BVFQUurq6rBQI5nUePnwIcXFxEtqel5cHf39/SElJkVS+Z8+eYc+ePUhNTSUGFjfevn3LIseuXbsGISEhlrAiDaYwOm2sMvOxHR0deSKgAMDb2xvi4uIYPXo0Iaboc7jb0tqpAX8lmKWFAeDIkSN4/Pgx690vWbIEjo6Ozb4PgNcj7OfnB4qi4OPjw7NIyMzMhK+vLwwMDODt7c03AgEASa+jUxsOHToECQkJ8lukjSDu75TZloaGBlhZWYGiKHh4ePAQphs3boSFhQXk5eWhpqb2pxd9L168QEBAAF/PXV1dHVavXk1SV7jbW1hYiO3bt2P9+vV/q5imAAL820D/DhsbG/H8+XNoamqif//+ROCZxo0bNyAlJQUXFxfWYpt5DYBTrn7NmjVkfsnKyoKUlBSCg4PJNu5x6c6dOxASEkJycjJPwQ1u0MSUoaEh1NTU/jGE1MmTJzFo0CBYWFhgzJgxJDrpyZMnkJeX5yGmaJJo+fLlkJeXJ3MAHR1NX5ebpKAryPn7+7OioGksXrwY0dHR5O+YmBgoKCjg4MGDpGphWVkZrKysCAHxe/Hp0yfIycmxdMN+Lw4cOABLS0vo6urC3NwcOjo6rPRObnvh69evUFNTw5EjRwD836apqamBgoICpk2bxnMP7ggXKSkpUBSFYcOG8RxbUFCAAQMGgKIoVroSd/+uWbMG6enpGDhwIJycnIhuIsAhEB8/ftxsFTfuSMPPnz+TqPQvX77AwMAAxsbGfLWeXrx4gcjISFAUxXIUtwbmzJkDaWlpZGVlsezG0NBQyMrKYty4cYiNjYWzszP09fWbtVt+L5jn0/9OT0+HpqYmq2gPwCnm0759e1YFUACsd7Vp06ZmI/eamprw8uVLREVFITw8nGhrAhwtprCwMBb5CHB+i+Hh4bCxscHatWtZNtSECRNgYGCA2NjYZvVea2tr4eHhgfv37+P8+fMQEREhNl1tbS02btxIdLtoML/Z2bNnk6p6T548wcCBA0FRFOmb0tJSODk5QUREpFl9MBqHDx+GiooK5OXl4eTkxCrilJCQAAsLC1KUgXkPAQRoSwhIKQF+M96/f080A7gHzoaGBly5cgVv375lTSxr167FkCFD0K9fP6SkpPBNieOeyOjSxfn5+bh37x4KCwuxd+9eUBSFlJQUVgrb5MmToampSf5OSEjAlClTiLHFvPbNmzdx6tQp3Lx5kxg5a9asgaGhIWthDPAu5mkcOXKEpCHl5ubC1NQULi4urIipCxcuIDIyEnFxcXyJl19++QVbt24l0So0aGJKUlLyN08AHz9+hKGhIWpra3HkyBGektv79u3jmZyYEyW94K+vr0dERAQsLCx4dHuSk5Ph6uqK3r17E6P1r0wN+CuwevVqGBsbo6mpCQ0NDcjPz4eYmBhJ8Xz37h3q6+vx8uVLyMvLE1H+ltpPG0MNDQ0YNWoUhIWFcfLkSZ7jvn//ju/fv7PC57mNmdLSUqiqquL06dO4ceMG6z3T4eNMoVsaTFFzuk0BAQGQlpbmWYQCnAin7Ozs352y1xy4ia9r166RBWVNTQ3WrFmD9u3bIyUlhRwjqKAigAB/HQ4ePAgbGxsMGjSIZ4HXu3dvKCsrt6iBExYWBgkJCWzbto1EM9+9exeSkpLo169fs1ViFy5cCGVlZcycOZNvij0ThYWFmDZtWrP6kD8bjh49CiEhIcybNw/r168nOkB0td3s7GwoKyvDxsaGkBD0WL1ixQo4OTmhtLQUU6ZMwZIlS8j4nZ6eDoqiiP1VVVWFrVu3ws3NDWZmZqirq+MR8160aBFERUVZES6DBw+GkpIS+vfvT6JCmBpffwSrVq2ClJQUK0qjOZKC37xZV1eHgwcPYurUqUhMTGRF+jIdE9u2bcOdO3dQWVmJnj17skTW6fkmMDCQ6D/yAzNt3dTUFMrKyrh16xbPvJuXl0fK3nM/z+bNm7F582biEHr27BmCg4Ph7OzMIqaauzfzXrNmzYKjoyPk5OTQt29fktJfUFAAIyMjmJiY8MhFAJzvaOTIka2q8VNcXAw3NzdSbOXTp0+4dOkSRowYgd27dyMsLAyDBg1C3759kZSU1GqVz5h9m5aWhgsXLqCiogLZ2dlQU1NDXFwcq6jS69evYWhoyIrCvnXrFszNzbF7927ExMSAoqjfVQTh+/fvmDRpEqSkpMhvtbi4GFVVVWRN8e3bNwwaNAi2trZYvXo1qqur0dTUhLCwMBw+fJgvIUU7yAGQDAgxMTFs376dbP/8+TN69+7N0uNj4uvXr3BzcyNO39OnT0NUVJRUTqbHiNLSUkRHR/P9jdFt+vTpE1RUVLBp0yakpaVh7NixUFZWJlWcAQ6ZPWDAADg5OQmipAT4yyAgpQTgAT1wvXjxAufPn8e9e/dIiOyrV69gYGAADw8PnsgSbiQkJEBGRgYpKSmYNm0aFBUV4efnxzdcmem9bWpqwtOnT2FnZwd3d3cEBgYC4Bhr3MTU5cuXoaWlBUdHRwwcOBAiIiJkMmEiMTERKioqMDMzg6ysLEJCQvDLL7+gsrISq1atgrGxMd/8feZEefv2bZiZmSEoKIhEyrx//x5mZmZwc3PDunXr8OLFC/j7+xNNDYAdpl1XVwd5eXlQFIWgoCCe++Xl5SEwMBAUReH58+fNtoW+5tu3b6GoqIjExERISEgQTSCAUyXRx8eHJTjNxMmTJ9GlSxeSWldcXAwFBQW4ubnh+fPnqKqqQn19PYKDg7F7924MHDgQFhYW+P79+1+SGvBXoqKigvQpbVQXFBRg//79cHJygpycHIYNG4Zff/0V8+bNg7a2No/4KhOpqakICAgg3yItPC4hIcFTzY4795/5ngsKCki7EhMTYWNjwxMJR+s9rF69mm9bzp8/j8mTJxPDqKSkBLa2ttDS0iLf2F8hFNnU1ARLS0v06NGDpBzW19eTSEiml10gXCmAAK0L5riyZ88e9OvXj/x96NAhWFhYYPDgwYSsLi0txbBhw7B///5mU4ppjB07FjIyMti6dSshprKystC9e3f07t2bNR8wF6+LFi2CmpoaZs+e/cOIqa9fv7IidX9W0BXR6NSzwsJCyMvLY+zYsazjHj16hF69evE4ge7cuQMZGRmi00SP0SdPnoSwsDBJ9QE4JMyWLVswd+5c1NfX48GDB4SgGDt2LM6ePYuKigqsW7cOkpKSrEjo1NRUREVFwc3NDdHR0TxpZb8Xb968QUREBF/S5ciRI9i7dy8yMzPJtuYi5ploaGjA48eP0aFDB+zevRtTpkxBt27diP2VkZGBzp07k2q29LXMzMwwa9asH14f4ESJaWhowNzcvEXCk+lgiY+Ph7y8PDZv3sx6f9nZ2QgODkbv3r2xe/fuZq/FxMyZMyEpKYlTp07hzp076Nu3Lzp16kTeY0FBAUxMTCAnJ8fXWfRntaS48e3bN8jLy2PatGm4evUqBgwYAEtLS5ibm0NBQYE4w5jvtzWdiwkJCZCTk8OmTZtIJOG+ffsgKiqKUaNGYf/+/bh//z48PDxgZWXFU9144MCBUFBQgJiYGLEzmmsfswrw27dvMX/+fGhra5PzFixYADc3N2hrayM2NpbovBUXF2PIkCEwNTVFr169YG1tjV69evGVtdi7dy8cHBxw5swZAJwsCxMTE+jq6gLgEMvfvn2Dt7c37O3tW5SPUFdXx6NHj3DixAmWY7KmpgarV6/m0aHjd61bt24hOTmZRdq+f/+epGsyiamamhpCxgkgwF8BASklAAv0AH348GEoKChAVVUVKioq0NbWJkKGL1++hIGBAXx8fFilWZm4c+cOtLW1SfRGRkYGREREeKrl8KtqkZ2djW7duiEpKQm5ubmsSXflypWEmKqsrERVVRX279+PoKAgDBw4kK+A9tq1a9GjRw/SlpkzZ0JYWJh4G8vKyrBmzRr06NEDCxcu5Nu2RYsWISoqChoaGujUqRNCQkII4fDhwwf07dsXqqqqUFBQgJWVFavKHvf1ioqKYGhoCAUFBdy+fZtnAf7hwwcMGjSo2ZDx9evXY968ecRDl5KSAoqiWJNMVVUVfH194e3t3axB9ujRIwwZMgRqamqkJHNOTg7U1NSgq6sLExMTGBsbQ0NDAwBH90tfX5+UvgX+mtSAtgS3V/ncuXOgKIoIzNPYvHkzIiMj0b59e7JYYJZN58bFixchJiaGiIgIFrnYv39/SElJNSsKymzL3LlzMWLECLJITE9Ph6GhIXr37k2M069fv8Lb2xt2dnbNGjPbt28HRVGYMmUKiUj4/v07rK2t0bNnT77FBdoKNTU1cHBwgKamJtGJoompzp07s8hcAQQQoHXAHFeuXLmCoUOHomPHjoiNjSXbDx8+DDs7O9jZ2WHMmDFwcXGBtbU1X0Lq7du3PIvh6OhoSElJYcuWLYSYorWp+OmZXLx4EZs2bYKsrCzExMSQnJzMioT4p6KwsBCampp48OAB8vLyoKCggBEjRpD9hw8fJqQK7diZO3cuS7/P1dUV7du3R3BwMPLz84m+I5PoYEZkNDU1IScnB5KSkoiLi8Pw4cPRvn17srguKSnBmjVreIipxsZGnsrJfwb8xLInTZoESUlJaGpqQkFBgSWXwD1n8XNG5OfnY+7cuRAWFoa4uDiZ+xobG1FRUYGVK1eiY8eO8PLyQmRkJJycnNCrVy++0U3r16/H+PHjERAQgBs3bhCSs7S0FOrq6kSTqyXQ9iR3pDFtj2VnZyMkJAT6+vqEiGgO+fn5cHJywunTpwH8v6o0Hf1Cfx+fP39GRETEXxZZvmXLFkhISEBMTAwJCQlEoiI0NBQRERFtdt8NGzZAVlYW9+7d4xlfDh06BGdnZ0hISEBfX5+lz8WMLl+5ciVERERgZGSEnTt3Nlvhkx9evXpFpECSkpIgJSWFHTt2YN26dbC3t4eFhQXR3ystLcX+/fuRkJCA5ORkvhqeAIdMdnZ2Rp8+fciaIyMjAz169ICGhgYsLS1hY2MDU1PTFonh0tJSeHp6YsSIEZCQkGCJzb98+RIBAQE4evRoi89XVlaG4cOHQ0xMDF5eXqx9NDGloqLCWgcJIMBfCQEp9R8Hc6CmB9W7d+9CVFQUGzZswKdPn3DlyhWEhYVBSEgI165dA8AJnVVSUkJQUBBfJv3YsWPEE5Ceng5RUVEyiFZUVCAjI4PvJFFcXAx7e3ue0Gtu4Wha/JwprN6cyPrQoUMxffp0AByDkFk9pLKyEnV1dSgrK2MJVTOxePFiiIqK4vTp08jOzsacOXNgbm6Ofv36EcKhpKQET548wfXr11li181FfBQVFUFZWRm2trZ8U/W4RT1pxMXFQV5eHmvXriXh3J8+fcKwYcNAURRiYmIwZswYuLq6snL9mzNmnj59imHDhkFJSYl4MSsqKrBmzRokJydjwYIFpO8jIiLg5eVFhGqBvy41oK3A/Hbz8vJQVVWF2NhYdO7cmUdHpaGhAVevXoWvry+8vLyaFXun39f169chJSWF0NBQFjE1aNAgUBTFEu/m/k7oKMO0tDQiGAxwDGI63cDe3h5mZmYkbQNoflGxe/du8p6YxJSDgwOkpaVb1Mj6o6CfiVu/rKamBjY2NjzE1KJFiyApKfmPiIYQQIB/IiZNmgRLS0tERUXB0NAQsrKyLD2dS5cuYdKkSXByckJYWBgZV5gLxIyMDFAUhZMnT/KMN0OHDoWEhAS2b9/Oo//IHCdPnToFiqKwfPlyrFu3DuPGjUPXrl0xffr0fxwxRY9rv/zyCz58+EAEkTdv3gxVVVWMGDGCzBUfP35EVFQUMjMzyXkfPnxAu3bt4O3tjS9fvqC6uhpRUVFYuXIlJCQkMHToUNYc0FIU6eHDh9GtWzd07tyZpNHTx9PElJSUFGu+/i3X/b19QaOgoADOzs548uQJ3r9/jwMHDkBCQgL9+/cnxzRnmzC/l02bNoGiKHTp0oVU1mOef/v2bQwYMAChoaGIiYnhSxBMmTIFsrKyiI6ORkhICGRlZZGamkqyAEpLS6GlpQUVFZUWdQwjIiKIffry5Uvs3r0bjo6OsLKyIqlkjx49wrRp03iejdtW+PTpE5SVlfHy5UscP36cJ/plzZo1PI7Wv4qYys3NZdkFjY2NcHV15avV1VoYPnw4Ro0aBYA/wVlWVoacnBw8e/aMR58L4GR3PHv2DA8ePEBYWBhsbW2xYcOG391n9PqFjjw6d+4chISEiKOW1vDi/t6bi1q7cOECXF1d4evrSzIXvn79irlz52LRokXYuXMn3yI53Ni4cSMoikJYWBh5/tLSUvj4+MDFxeU3PWdWVhaGDRsGISEhHDhwgLUvNzcXEyZMgK6uLr59+yaIWBfgL4eAlBIA79+/Zwleb9myBb1792ZNoPn5+Rg8eDBMTEyIgfTu3Tu8ffuW78B1/fp1BAQEYMeOHSwxP4Bj+A4fPpxv9bWnT59CQ0MDV69e5as1Rd9r1apVaN++PZKSklgaU0w0NDSgoaEBnp6eyMjIQFZWFmvSr6+vx/Lly1kh5fR5AGfCqa6uhru7O6ZOnco6ZuPGjdDQ0ED//v35GjBMgcTr168jNTUVsbGxePbsGfEiFxYWQlFREXZ2dnwrrHCnx23evBmysrI8Ibr19fWorq7Ghg0b4Orqiv79+2Pq1Kl8c/23bNlCiEUa2dnZGDZsGBQVFXkMWYAz0cfGxkJMTIwIt//VqQFtgbNnzyIxMREAx8tvYmKCmpoalJaWYvLkyWjfvj0OHz5Mjqe/R6b+E/N5zp8/T7xs9P5r166hW7duGDhwICsiafr06c32xdGjR6GgoMDy2H758gXPnj1DU1MTXr9+jTVr1mDmzJnYtWsXX2Pm/fv3rIg2gFMEoGvXrpg0aRIxxEtKSuDh4dFmlRCvXbsGa2trcj8mMWVpaQk9PT1CTDU0NPAsZAUQQIDWwcmTJyEpKUkihisrKzF//nzo6+uzUtfr6upYYxNzXKHHJF9fX8jJyeHUqVOs/a9fv0aXLl0gJCTEKnhBg9bsCwoK4om4SElJgbCw8G8SP/9ZQI9nGRkZkJeXR3JyMhobGzF27FhQFIXAwECWHZOYmAhdXV0eDa2nT59CUVERnp6eKCkpIeecPHkS3bp1w9ChQ1l2BrfNRR9/+fJlKCgoQEZGBvHx8TxaQ3SBF4qiWKn+rQHuSq3Lly9H7969ERYWRhxZtbW1yMzMhISEBAYMGMDTfn6oqqpCQUEB7t+/jzlz5hCHKdAykcb8Lrdv3w4VFRUy12RlZYGiKCgoKCAlJYUQoSUlJQgJCWmxMl5cXBxMTEwwe/Zs2Nvbw9/fH9HR0fDz84OysjLKy8tZx9PXYvbPxo0bkZOTg9LSUri7uyM2NhbdunVjRb88e/YMffr0IVFUfxfKy8tx/fp1+Pn58VTBay3Q0hZmZmYYN24c2cZ0bN2/f5+nb5nfzfHjxyEnJ0fs66KiIgwcOBC2trYk8gzg6Hc1t2agcf/+fWLPnjhxAlJSUti4cSPOnz+PHj16wNTUlEeGgYnjx4/j3r17rG3nz5+Hq6srPD09eWxwGi3pQD1//hy1tbWYP38+OnTogKCgIAQGBsLR0REGBgZ8xebpc79//86Sm3j79i0iIyPRq1cvHufrx48fWQUYBBDgr4SAlPqPo6amBtbW1lBVVSUDWGpqKiQkJHiqvpw4cQJKSkoszSbmpL19+3YSLp6XlwdlZWXiDaVRXV0NLy8vDBw4kO+Ev3fvXnTo0IGlMcWNyspKFBQUYMuWLejWrRuJrGjOsJk+fTrExcXRqVMnIlANcAwQFxeXH4aq9u3bl6dCH8DxCgsLC2PgwIHNiimmp6dDQkICXl5esLW1JVFOtBFUWFgINTU16Onpsfo1LCyMiGLTfTFmzBiSBvDs2TNs3rwZZmZmMDAwIIYLdyla5iT34cMHEkHFLWj78OFD6OrqQlFRkVSzATieqfXr10NXVxfi4uJ/a2pAayMxMRGGhoawsbGBlJQUK5qprKwMcXFxaNeuHSGmuKtCMtMnXrx4QVIo6Qmd3n/58mV06NABo0eP5kkN8PPz49EaOXDgAGxtbVFWVoZnz55hzpw5UFVVhaamJry9vXnIJoBNpL579w4URWH+/Pk8x9KpfMnJyeSbbUtv2Lt37yArKwtHR0ey0KR/p3fu3AFFUdDQ0BAIaQogQBtj8+bNUFNTYy3siouLERcXh65duyImJoZsp3+jR48eRXBwMAAgNjYWNjY2ZBz39/dH9+7dcerUKTLOP3r0CImJiUhJSWlxgRUUFETmMqbzZfjw4ZCRkUFCQsI/hpg6ceIEhIWFsXnzZlZ5+sjISHTv3h0pKSlYuHAhRo4cCVFR0WbTw7Kzs0lVPiaBcerUKXTr1g0jRozgcR7wG7vr6+uxf/9+KCgoICYmhm8U7KFDh1p1Ph47diypvEw781JTUyEvLw8TExPWsbW1tTh69CikpaXh7u7e4nU3btwITU1NsqD+9OkTpk2bxkpxA4B58+bxLdxB32/Lli1EczEjIwPi4uLYtWsXEhMTISQkhMWLF/MUhGHaLkxNtGvXrmH48OFQU1PDokWLyPvct28fPD09eWwwgFPJsmvXrnj69CliY2PRvXt3Euk+f/58UBTFIobLysrg4+MDNze3v9WR19TUhMuXL8PPzw+enp6t4lxsiYCcOXMmlJWVeapr0lXyWioARKfEMaPfi4uLMWjQIFhbW2PUqFHw8fGBpKTkb2p/SUkJqqur4erqijlz5pDtdnZ2UFVVZa0JmLbgkydPoK6ujrCwMJ7f+tmzZ9GtWzd4e3vzFATiB6acirq6OumX/fv3Y+LEiRg2bBiWLVvG1wFNn3vs2DHY2tpCR0cHFhYW2LhxIyoqKvDs2TMMGzYMvXr1Ytn8Agjwd0JASv3H0dTUhOvXr0NfX59UH3v79i10dXWRmprK8ii8fPkS6urqZPJnTi6//PILrKysYGlpSciVhw8fQlRUFMHBwdi2bRsOHjxISBF68OQ2qm7evAkhISFWhAo3VqxYQYwZusIZsy137tzBjRs3yIL806dP8Pf3h6qqKj58+ICGhgZ8/vwZXl5esLS0bDYNi0Z8fDzU1NR4Fs5Lly6Fh4cH3NzcMGvWLJ7zb968CXl5eSJKXVtbi3bt2kFFRQVLly4lUTUFBQXQ09NjVVeZOHEiT+rEvHnz0KNHD0ybNg3m5ubo27cvpk2bhtDQUEhLS6O0tJSvkcqcgC9fvoyQkBBWCDKNoKAg9OzZk0eAvaamBsXFxX97akBrgdkWFxcXUBSFESNG8BiTZWVliI+PR6dOnbBr1y6+12Bea//+/USnhbvUt7a2NiiKYhk3AEeMnjsi7uDBg5CXl0ffvn0hLy+PiIgIrFmzBnv37oW6ujpu3rz5w+eaNWsWOnXqhKVLl/IQU7q6uqAoCjNmzPhLiML3799DXV0dtra2rIXmnTt3MHjwYLi6uraYLiGAAAL8PnAXTwA4nnptbW0eL/2LFy/QvXt3aGpqstLm6+vrsW/fPsjIyMDQ0BDi4uI8kTf+/v6Ql5fH8uXLcfHiRfj5+WHo0KFkf3OLv7i4OPTo0YOMTfQYOH/+fMjJycHIyKjFQhI/C6qrqxESEoKkpCQAHIfZy5cvsWTJEhw9ehR9+vSBl5cXTExMEB4ezlpU85sTs7OzIScnBy8vL9Yccvr0aUhJSaFfv3485D7AicLhHkO3b98OBQUFTJ48mUTpenh44NKlS+SY1hr/P336ROwUZiT4xo0bISQkxCI8AY5Ns3//fnh6erZIUrx48QLa2tqwsLDgIaY6d+6M8ePHw83NDT179mzRjnv9+jU+f/6M3NxcGBkZkcrBeXl5EBMTg4iICNHrYkboAByJgj59+sDb2xtXr14l96FtT/ocLy8vhISENFvIZ8CAAZCQkICoqCgPuRIbGwshISGEhYUhLCwMTk5OLPmF36KH1FaoqanBw4cP+abL/V7QEXPNXePmzZtwc3ODl5cXSYUsKCiAv78/7Ozs+Iro0ygtLUXPnj2JfUSPKd++fUN8fDwCAgIQFBT0u/r006dPUFBQwL59+0hbBgwYgCNHjvC1AWns2LEDlpaWiIyMJM5bGnZ2dlBSUmJlX7TUlgMHDkBYWPiHkY38xtrTp0+jS5cuWLBgAXJycjBo0CB069aNaIQ9evQII0eOhKysLE/GiAAC/B0QkFL/MfAb/BobG3H79m0y+QPAtGnTYGBggMWLF6OgoADl5eWYMmUKNDU18eXLF9ZAPHfuXPTr1w9GRkbo2LEjrK2tCYFz69YtWFtbQ1NTE3Z2dhg0aFCL3pZPnz5BRkYGAQEBrDKqzPtNnjwZ8fHxPJErAMfYVVBQQKdOneDh4UHIrUuXLsHFxQXCwsLQ19eHqakpLC0t+WpmnD17FufPnyeChgCnepyuri5u3bqFoqIi1NTUIDAwEDt27MCkSZN4wrYbGhqwc+dOUkr77du3UFVVxfjx4xETE4POnTsjNTWVhPE3V91ow4YN2LJlC+rq6vDy5UvEx8dDR0cHqampJOXvwoULcHZ2ZhlJ58+f5yFSaFy5cgVBQUEwNTUlIcYVFRUIDw9HRkYGz2T7M6QGtBa4KxhOnjwZI0eOhIWFBZKSkoiHmj6urKwMI0aMgL29Pd9rfP36FSUlJcQ7d+DAAVAUhdjYWHKt0tJSxMfH4+LFi80u0lJTU2Fra0v+3rx5M+Lj47Fv3z6SLvvx40cYGRnxRLnR74lbz4D2wC5dupQsFKqqqhATE4MVK1a0usA53Y579+5h3bp1WLt2LTF+3r9/Dy0tLVhbW+Phw4coLi7GrFmzMGrUqJ8qpVMAAf7p4Jf2DnCiFvX09DBgwADW2P3ixQv069cPM2fOhJmZGU/aSUBAACiKgre3N9nG1BUcOXIkNDU1oaSkBDs7O9Y4xKxs9eTJE5I6WFVVBXNzcxgZGeH79+/k+ISEBOzdu/cfoytHP8f48eNRXFyMcePGwdHREfLy8lBRUcGyZctQXl6OyspKlvOB+Y6Kiorw/ft3MkY/efIEPXr04CGmMjIyCInDPH/mzJmwtLSEqKgoBg4cSIqWABxiSlVVFa6urrCwsICSklKrV2tjYteuXRAWFia227dv30h6/6RJk1jHMokJ7mcC/v/tvHnzBnp6ejA1NSXEVGFhIdauXQtbW1sMHjyYr6biypUreYqr3Lx5E7q6uiTi5PHjxxg3bhxWrVrFl9RatmwZEeE3NTWFlpYWli9fTpy1paWlOHXqFNzc3GBoaMi3wA2NuXPngqIoSEpKkjRC5nEbNmzAyJEjERoaipSUFL7RL383/gw5Fh8fD0VFRWInN/dcGRkZ6Nu3L7p06YJevXqhV69eLBFwZhvmzJmDSZMmYdGiRdizZw9kZWVx8OBBnmvW19ejrq6OpVPFL+qdG8XFxURDdNeuXXB3d2dJmzCfYdeuXayU5F27dsHMzAxDhgwhEVPfvn3D8OHDsW/fPr42/82bN3Hx4kVWxezk5OQfFgnid63q6mr069ePkF/FxcVQU1NjFSYCOAEF48ePbzMJBwEE+D0QkFL/IdADVn5+Pk+UTF1dHe7evQs1NTU4OjoC4KS96evrQ0hICNbW1ujevTtLnBngRC117doVFy9exLt377Bp0yY4OTnBysqKCDSWlZWhsLCQZXy2NNEeOXIEnTt3Rnh4OJ4+fUq2V1ZWkuoQtFHNnEzu3r0LY2Nj3LhxA7dv34aXlxfs7e2xZ88eAJxBes+ePdi4cSMyMzOJEcI00iZPngxJSUkoKSlBSUmJlBqmxZnpNCo9PT1Sle706dPQ1tbm8ey+fPkST58+RVVVFVxdXYmgbGNjI2RlZSElJYXVq1ez9Ke4J33aC7h//37SXtp4BTjEipeXFwICAsg1Xrx4gXbt2qF79+5wcHDApk2beEL4L168iH79+qFbt24YMmQILC0teaLGfpbUgNYCs2+3bNmCy5cvk7+nTZsGExMTJCUlsRYCOTk5LAOG2ScLFiyAg4MDTE1NYW1tTb739PR0dO7cGf3798fs2bPh5eUFGxubZg0igPM+JCUl4ePjQ7bRi5iGhgaUlpbC19cXjo6OfDUDzp8/j4iICAQFBWHIkCEk6mvRokXo0KEDpk6diiNHjmD69OnQ0dFhpSO0Jg4fPozu3bvD3d0d/v7+EBYWxtKlSwFwKggZGRlBUlISWlpakJKS4hlPBBBAgNZBamoqIiMjERYWRhwYd+/ehbS0NPr27Yv169fjxo0bcHd3R2RkJPLy8tC5c2eWXk9tbS3Wrl2LRYsWQUVFhaUDxBxD3r9/j+zsbNZijR6b0tPToa+vj549e6Jnz55E0PvOnTuwtLSEjIwMQkJC4O3tjU6dOrHS2P8J2LlzJ4SFhSEmJobAwEAixD1hwgT07t2bZy7kdui5u7tDXV0dgwcPRkZGBoD/p/J5e3vz1Xeh+3nGjBmQlZVFWloaHj9+DCMjI1hbW2Pr1q3k2MzMTCQnJ2Py5MltTnS8fv0a9vb2UFVVRW5uLgDOYnjdunWQlpZGXFzcD6+xf/9+8m+6r16/fg09PT2Ym5uz7Kza2lq+gtjx8fFQUFDAggULWCLxtD7Q7t278eDBA/j7+yM0NJTsZzpInj17huHDh+PChQtkW3R0NAwMDLBs2TKUlpbixYsXGDNmDMLDw3n6ltuWKyoqwpMnTzBw4EBIS0sT4qElp8y/xWHT1NSEs2fPwtLSEubm5nyJKebvIi8vDydOnMCKFStYti+36PmkSZMwaNAgaGpqwtXVFRRFgaIoREZGIioqCvv372edz30f5t/N9fX+/fvh7e0NTU1NVgojsy3Xrl3DkCFD0LlzZ1YE1K5du2BjYwNHR0fExcXBzc0NdnZ2fO19OiNDRkYGioqKcHBw+E3kPNPpsHHjRpZTwd3dHdeuXUNRURHk5ORYKaLp6elkXmiuSJQAAvzVEJBS/zF8+PABUlJSoCgKzs7OmDp1Ki5evEiIjqysLBgYGMDOzg4Ah8DaunUr0tPTeSKX6urq0L9/fyJMSOPo0aMwNjaGjY0NS6eHeW5LaGxsxIYNG9ChQwfo6OggKioK0dHRCAgIgIyMDFnIck/6z549w5gxY8jfnz9/RnBwMOzs7LB9+3a+92JOLO/evYOhoSF++eUXPHjwAKmpqWjfvj2Sk5PJMfv27cOqVauwdu1aci4dSVNeXo7Lly9jy5YtrHu8evUKBgYGrKiRQYMGYcKECc2mLTG9PUFBQdDV1cWePXsI2VBWVobMzEy4uLjAyMiI5aHLzc2Fn58frly5grVr12LQoEGQkJDAqlWrWETMu3fvMH/+fPj6+mL06NEsT9TPlBrQGmB+cwkJCVBWVsbs2bNZBmtSUhLMzMwwefJkZGdnw8XFBa6urnyvkZycjO7duyMtLQ137tyBjo4O1NTUSLnqc+fOwcPDA9bW1vD392/Wg7ps2TLs2LEDAEcQX05OjhWRUFFRgfnz58PDw4NVZY/5fjIyMtC1a1dMnjwZq1evhqamJrS1tUmk1rp169CrVy9CqHLrNbQWsrOz0aNHD6xbtw4A8Ouvv6J9+/YYP34867hdu3Zh3759As+cAAK0IphjwowZMyAtLY3Q0FBYWVmxUq7v378PPz8/qKqqkrTaqqoqNDU1wcLCghAjTFRXV2Pfvn1QVFTEwIEDWfsuX77McuwwF3eXLl1C165dsWnTJpSUlODkyZOgKIpUfSotLcXMmTMxbNgwDB06lG/Bj38Cnj59inPnzgH4/3sYO3YsIiIiml3wJScnQ0pKCunp6Thx4gR69+4NCQkJokuVnZ0NRUVFWFhY8Oh7AsDVq1dhYGBABJdv3LgBISEhGBoawtjYmFWlrjnh+j+D5qJm3r59CycnJygpKbGIqQ0bNoCiqBajPj5+/Eii3GkwqxvSejzMeZt5DMCJOJKWlmbp+TCff/To0RAXF4eSkhIsLCz4Ro4dOHAA8vLy0NDQ4IlMHjNmDAwNDbFy5UoibcBNjDH7Jicnh+W8a2hoQGBgIKSlpVnO4ZkzZ5L++jeisbERV65cgYWFBUxNTX9ITHHjRxF+FRUViIiIgKWlJSZMmAB3d3eSEcEvnRng2PMBAQFkW3PkVXl5OfLz8/kSoJMnT4a1tTUGDBgALS0tyMjIsGyeU6dOYcyYMbCxsWFlijDbQUcT3r59G8+fP8f169dhYGAAY2NjEpXaXJYLwLG1evbsicDAQKJFCwBeXl7o06cPNDQ0WAWHSktLERgYiDVr1vxU0hoCCCAgpf5jeP/+PYyNjaGtrQ1zc3NERkZCSEgIxsbGCA8Px4EDB3Dw4EFoaGjA3d39hwPWkCFD4OHhwTNhTJkyBRRFwd7e/g97Pu/evYt+/frB2NgYDg4OmDJlCt/InJSUFPTu3Rumpqbo27cva19+fj6Cg4Ph7OzcojG0dOlShIeHsyaT8vJyrFmzBu3bt+dbBvfJkycYM2YMJCQk8PjxYxw5cgTS0tIYNWoUa8F98+ZN4s38/PkzZs2aBTc3N1YKBI2mpiYUFhbyCMQHBQVBX18fe/bsQXV1NV6/fo1p06Zh6NChfL2fsbGxsLCwQENDA+rr63HixAl4enpCWloaw4cPR1ZWFiG4mO2or6//aVMDWgNLly6FtLQ00SsA2JN9SkoKTExMoKSkBGtra9TW1vIYA58/f4a1tTURlz927Bi6detGyBj6eFook58hA3C+r7CwMAwePBj19fVoaGjAtWvXICcnB19fX3Lcrl27kJSURKIPmNcpKiqChYUFli1bBoCT/qqkpETKKtN4/fo13r17x1Mh6Y+Auz/o9pw6dQouLi4AOISnoqIiK1S8JZFSAQQQoHXw5csXTJ06lSx4y8rKMHr0aFZFvLKyMh5nw5QpU6CoqIjc3FwcPnwYS5YsQWpqKiFJysrKkJaWBmVlZQQGBuLjx4/w8PBA37590dTUxLei1dy5c4me0Lt37/imj9D4tyyOnj9/jqSkJIiLizc75uXm5sLa2hoXL14EAJw5cwZiYmLYtGkTgP8vwH/55ReyaOYedz98+IANGzagsbER58+fh5SUFLZv347y8nJCuKxcubJNnpHZlocPH+LRo0esqPacnBweYurr16/IyMhgLfz5LbSvXbsGRUVFeHl5sbYXFxfD0tISFEWxRKa5rzF27FiSKvj8+XNs2bIFxsbGcHFxwbFjxwBwbLI7d+7wjcChMWjQIHTq1AlLlizh0ZscP348pKWl+UZ1MTFlyhTo6uqia9euGDNmDCG4mpqaEBQUBHFxcSxZsgROTk7Q09P710RGcYMZGXT58mWYm5s3S0zxA/MdHzhwAMuWLUNiYiJyc3NZfTZlyhQEBASQv5n2V1NTE+s6dBXwDh06IDo6+ofEFL+2HD9+HBISErh16xaamprw7ds3TJ8+Hb169UJsbCw5rra2lhXVx20nR0dHsxzqAIeg1dTUxODBg1vsm+fPn0NCQgKJiYmkgBLzGbW0tKCtrc3aPm3aNGhoaDRboEkAAf4uCEip/yBev36NwMBA9OnTB3fu3EFubi7S0tJgZ2cHS0tLdOnSBQYGBqAoipA8zeX7r1ixAjo6Ojh58iTLI7hr1y4EBgYiMDAQw4YN41uR5LeA3yTN7WEQFRXFtGnTYGFhARkZGcyfP591fH5+PpydnTFmzBi+k0x5eTkmT54MERERVpQKvW/NmjXo1KkTq7JcZWUlDhw4AHt7ezx+/BhXr16FiIhIsxFZQ4cOhYiICDQ1NSEtLd1stArdvuTkZHh6erLEz4OCgmBgYEDCkZnC5twljIuKiuDt7U080gCgpqYGb29v4j1SUVFhkXzcffOzpQb8WVRWViIkJIRU4Xnz5g0OHz4MNzc3REVFkQn68ePHuH79OulTbt2vp0+fQkpKClVVVTh9+jRERERIKeeKigosXbqUJz2uOa9yWloaxMXFSfQfTUzJy8uziCn6fG6hUNpw+f79O/Lz86GgoMAipFoqGPBnkJOTgylTprC2nThxAvb29rh//z6UlZUxcuRI0oc3btzAqFGj/jEVtQQQ4J+IgwcPgqIo9OrViyWwW11djdGjR0NYWBinTp1inXP//n34+/tDTk4ODx8+REJCAlRVVWFvbw83NzfIy8uTCKby8nJkZmZCRUUFKioqRJdxy5Yt0NLS4klNDggIQGxsLEpKSqCoqIiRI0eS/Vu2bCEkzL8F9+/fx6BBg9CrVy9WpA73+P/y5UvIycmhoKAAx44dY80hVVVV2LBhA6saXFZWFonCjYuLI3Pwt2/fUFdXhz59+mD69OnkPp6enlBUVERsbGyrk33M602fPh2amprQ1NSEuLg4632+e/cOzs7OUFVV5alsx+38On/+PNLT00mUx/Xr13mIqerqagwdOhSPHz/maxcmJSXh/fv3iI+Ph4iICNauXQsLCwv4+fkhKSkJrq6uMDEx4Sks0pIjLSgoCHp6eti3bx+PE3HZsmU87WD+nZaWBhUVFRw8eBCbNm2ClpYWAgMDCREJcPTY7Ozs0Ldv359C1Ly1we9ZaBvHxMTkdxFTACfFTVlZGf7+/vDy8kLnzp1x5MgRcu6VK1fQs2dPFBUVtUh+Tpo0Cebm5hg5ciTMzc3Ro0cPhIeHk3N+Kzm4fv16aGlpsb6NgoICjBkzBl27duUrZs7v9+jv74/evXuTv+n7p6amwtzcnCV9wgRdaIG7gnNdXR3y8/Nx584dLFy4EPr6+nBxcUFMTAwROxdIJwjwM0JASv1H8eLFC3h6esLd3R1ZWVlke0lJCYnMMDExYVXdADjGVE5ODivM2M3NDTo6Oti/fz/y8vJQWlqKPn36YNGiRVi4cCHk5eV5GPzfipbECM+dO4f58+eTtITCwkKMHTsWNjY2WLhwIevY4uJiHjFA5vXev3+PmTNngqIoEvFCo6KiAgsXLoSDgwOP14VOe5w3bx7xaJSUlODs2bMYPHgw+vXrR8L609PTkZ6ezjLQmjNAzp07ByUlJXIujZCQEEhLS7O285vkamtrMWDAAJJDbmhoCDs7O9TX16O+vh4ZGRmIiYlpNnT6704NaCv4+vrCzMwMmZmZcHNzg7OzM0aMGIEePXrwVB0EOKLdFEWxKpPU1dXBz88P48aNg4iICKs09YsXL+Dh4cHSoQBajgIIDg6Gn58fEUunK2JSFMUSh83Ly4OqqirRrmpqakJNTQ3s7e2xZs0aqKioYNSoUcS4/fDhAwICAnDmzJk/0FPNo6mpCZs3b4a4uDirqlJWVhaMjIwgISHBqsAFcCL3AgIC+EZTCCCAAK2D3NxchIeHo0OHDiSVmklojx07FhRFsdKG6uvrsWTJEjx//hxr1qyBgoIC0SXZsWMHEWemq+42NDSgpKQE165dQ2NjI9LS0nDz5k1C6jMXaLt27YKvry9kZGTIXERHe44ePRoTJ078V+mZVFVV4dq1a8SRAbDneFoG4evXr3BxccGsWbMgLi5OCCmAUxErODgYV69eRWNjIz5//gyKojBq1CiMHDkSYmJirAismpoaWFlZYdasWQA47yciIgKnT59ucSH8ZzF79mzIysri0qVL+P79O4YPHw6KorBgwQJyDC2uHxgYyDqX2Z7ExEQoKCjA2NgYQkJCGDZsGF6+fInr169DVVUVxsbGmDFjBhwdHWFjY0OeiUkmpaWlgaIoXL9+HSUlJQgPD4euri4WL15Miu5cvnwZNjY2rGhhbpHpzMxMZGdn49u3b2R7QEAADAwM+BJTAKe/FyxYwIo6uXLlChISElhSDnfv3oWZmRn69OnDIqaYhYN+dvvp94DZt8eOHcPGjRuxdetW0k83b96EmZnZbyam0tLSICcnR8j2S5cugaIoVvT+L7/8gvbt2/PNqKBx5swZSEtLk6IL9PhnaGiIyMhIvhFT3KDf19mzZ9GzZ0+eFE861VRNTY1FGF2+fJnYY9HR0USz9sCBA9DV1SUVIGns3LkTRkZGrCJGTNTX18PBwYE4Wunni42NhYiICPT09GBiYkLWI/7+/oiJieErqyKAAD8DBKTUfxivXr2Cp6cnPD09CfnABL9ULhMTE8jJyaF3796sCdff3x8GBgaQlpZGr169oKWlBYAzEWtoaLD0qFoDN2/ehLKyMqSkpHD16lWyPS8vD2PHjoW1tTUWL17M95loVFRUsAzijx8/IikpCSIiIkTslQYzDJhbz+fjx49YvHgxunTpgqtXr8LPz4/kctvb20NfX59VmY8G0zC7du0aT+nYyMhIGBsb80TdJCUl/aYJ8/Xr15CVlUXXrl3h4ODQbInthoaGnyo1oDXQHNl38+ZNODg4QEpKCjNnziSGyaZNm+Dt7c0T0Zefn4/hw4ezNFmqqqowYsQICAsLY/To0eTYyspK+Pj4wMvLi3V/5r+XL1+Os2fPsn4P+/btg7GxMU+qx6NHj1jv+f3793BycoKUlBRJiS0vL0dkZCRERERYYesAJ5TdxMTkDxPCLaG4uBhr166FmpoaS1Nu3rx5oCgKixcvxrNnz5CTk4O4uDhISkoK0vcEEKAV0dwYl5ubi759+7J+c/ScUFVVhSVLlvBd/BUXF2Ps2LFkYXT8+HGIiopi0aJF8PPz49HpAThRO+rq6sRJlZWVBTk5OTK+3b17F6amptDR0SHVbMvLyzFt2jTIy8vzVHD9t4E5x0+dOhUKCgpERmDkyJGgKArx8fHkmIqKCr5zyIMHD9C5c2cICwuzHB5NTU0oKyuDn58fPDw8kJCQAFdXVxgZGTVb0bc1kJ2dDU9PTxJ1l5mZCQkJCQwYMADt2rXDwoULydz1+fPnZu2VRYsWQU5OjhCeq1evBkVR6N+/P968eYN3797B19cXrq6uCAwM5BtNdPToUcyaNYtoM9LgLgjj6emJoKAgvkVLEhMToaioCC0tLcjLy2PSpEmsb71v374wNjbGli1beCKtXr58iQEDBrAiq7t06QKKojB37lzWsTQxFRQUxNL+4W7PvwkJCQmQl5eHv78/9PX1YWFhgX379gHgkDQWFhawsLBgvS+A97tdtmwZsbf2798PUVFRQuZ+//4dlZWV+PLlC4YOHdqifbxr1y4oKiqyiMfS0lJMmzYNXbp0wejRo3l+O81JFuTm5kJbWxuRkZEsZ/Pjx48RHByMxYsXQ19fH7GxsSgsLISVlRX8/f0REhKCLl26EML0/fv3CAkJgaenJzZu3AiA87vx8vJifbPcKC0thY6ODkaMGIEXL14gJSUF2traCA4OxooVK7Blyxbo6OiwdHH/rd+ZAP8OCEip/zhevXoFLy8veHp64ubNm80eN3PmTEhLS+P06dO4f/8+Bg4ciI4dO7J0mq5cuYLt27dj3759ZFIYM2YMrKysmg0//aPIy8vDnDlzIC0tzaooAXAG8wkTJkBdXZ1U3gN4yQEPDw+4ubmxNC4+fvyIadOmsfQdmGAO6FlZWaAoCps3b8b379+JRzgsLIwYjs+ePYOOjg6PqDOzLTt37oSxsTHU1NQwb948Qjj8+uuvMDc3x9GjRwHwVshoaeJtbGxEdXU1oqKiYG5uzqriwT0pZWVloaCgAMDfnxrQGmD27dmzZ7Fjxw6cOnWKFaXDnUbm5uZGqiNyo6CgAGPHjkW7du2IJkVRURFcXFxgamqKQYMGISkpCQ4ODjAwMGAZzsz+uXz5Mnr37g0NDQ3Y29tjw4YN5Fhra2seAWEazPf89u1bBAQEQExMjKTUPHv2DKampnB2dsbChQtx8OBBjBo1CmJiYjyLyD+C5hY1RUVFWL16NY9OzKRJk6Cjo4OuXbvCzMyMJ5VIAAEE+HNg/ibv3r2Lu3fvsiKe8/Ly4O/vDykpKTJOcP+O+RVfuH79Ot69e4fs7Gyoq6tj7dq1AIA9e/aQylb0/PT27Vuoq6uTohwfP37E06dPYWVlBXV1dUJMnTlzBrq6ujAxMYG5uTm8vLzQo0eP/1T6CG2rZGVlsWyh0NBQyMrKYty4cYiNjYWzszP09fVZc0hdXR1u3rwJUVFRtG/fHtHR0Tzz1/Pnz9G3b184Ozv/Jalg+fn5WLt2LWpqanD16lXIy8tjzZo1AICBAwfy1eHktlfy8vIQGRlJdJmOHDkCCQkJTJ8+HeLi4ggKCiLPyYyKYhKqDx48gI6ODrp06UJsPSZpVFlZid27d8PDwwOGhoZ8v/kFCxZAXl6eOGZjYmIgLi6OIUOGsL5Re3t7hIeH8+0P+nrHjx9HUVER7t27B2VlZXh4ePDMfVlZWVBUVERiYiLfa/3Twfzmdu/eDQUFBUI6rl+/Hp07dybFFBobG3H9+nUoKysjKiqKnMd8P/T7nDRpEgIDA3Hu3DmIioqyMhpSU1NZUeUAWFWtme26cuUKtLW1eaLZ3717Bzk5OWhpabHSjJnPs3LlSowYMQKWlpbYuXMnSktLkZWVhW7dumHgwIHYunUr7t69Cw8PD4SFheHr169YtmwZFBQUMGXKFDx58gQqKipo164dKzoS4NhxERERxNmur68PY2PjH/6WL168iA4dOkBFRQWioqLYsGED0Qqsq6sjbeHXtwII8LNBQEoJgFevXsHPzw/W1tassH4at27dgpWVFYlIOnPmDERFReHt7Y0uXbrwpLsBnIiUUaNGQVJS8k8vjLkHY3qSKikpQUpKCjQ1NXn0bT5+/IjU1FS+xE1iYiJ69OiBBQsWYP369ZCWlkafPn1YOj3Tp0/nSdti4smTJ9i0aRNPmiB3RFhCQgIsLS1ZXhkmkpKSEB4ejtOnT2P79u2Ql5eHk5MToqOj8fXrVzg4OLBKFv9epKeno1OnTuQdcE/SP2tqwB8Fsy1TpkxBjx49YGlpie7duyMsLIxVfbCsrAwnT56Ep6cni0z6+PEjz/v6/PkzoqOj0a5dO2JQFRYWYv78+fDx8UG/fv2QkJDA0tVifreTJ0+GiooKysrKcOnSJcyZMwddunSBl5cXli1bhhMnTsDAwICUiabPraqq4tG8eP36Nfz8/CAqKkpS+R49eoSoqChoa2vD2NgYvr6+ZF9r9OerV6+wePFiZGRkoLa2lhCkJSUlWL16NVRUVFjk8K+//ooLFy7gl19+aRVxdQEEEIAD7iqg6urq0NLSIlFN9BiUl5dHKtZyL4yZYxO/qKndu3ejd+/ehEA5ffo0Ro4ciQULFpDj37x5AwMDA2zbtg2bN2+GlZUVcnNziZaQoqIimQ/v37+PPXv2YMyYMdi6det/qvJmcXEx3NzcCGny6dMnXLp0CSNGjMDu3bsRFhaGQYMGoW/fvqSgRWNjI9/3cvPmTbRv3x7Dhw/nGwHLdIS0dZU9OrIlOjoaQ4YMIXZZbGwsbG1tYW9v36JtUF1djfT0dJSUlODevXtQVVUl0dfLli0jFaJpoX1+bSkrKyOp67179+ZJvXr79i1mzpyJsLAw0h9M0iovLw99+vTB3r17AXDSzMTFxREeHg5FRUWEhYWxfjstkXz5+flQVlZGZGQkiouLcevWLSgpKWHIkCE8c/GzZ8/+daLmJ06cIHYB/WzJyclElP7gwYMQExMjZEx5eTmJsPzll1/IOZcuXSK/lVGjRhGH140bN2Bubo4OHToQApS+jr+/P4+uUnPRTZ8/f4aJiQkCAgJY4vxv3rxBv379MGfOHJiYmPA46adMmQJZWVnMnTsXM2bMgJiYGHm2a9euwdvbGwoKCtDU1IS1tTXpi8LCQqxevRpv3rzB06dPYW9vD1NTUwQFBfHo+xUXF+P169fYtGkTjh8/3qIQPxMfPnzA/fv3ebIhGhsbERISQiKlfiZbXQAB+EFASgkAgONp69evH3Jzc3kG8/z8fMycORO1tbU4f/48ZGVlsXHjRhQUFMDW1hYURWHJkiWsc06ePAkXF5c/vTDm9lIMHz4c5ubm2L59Oz58+IDKykrMnz8furq6zXqemJP/0aNHoaurS9K2jh49iq5du6JLly6wt7cng//79++xceNGvpNBbm4uLCwsICoqipSUFADgCem+cOECYmJi0K1bN5ZRw5wU6Jx6ZiW4d+/eYePGjdDT04O5uTlcXFxAURRLg4Bf+HlLxlJgYCD8/f1RVlbGd//PlhrQGliyZAkUFRXJe16wYAE6deqEgIAAorVy584djBo1CoGBgeQ9HzhwAJKSktDX18fKlStZQvE1NTUkYorWMWhOyJOJoqIijBo1ikcf7NmzZ5g6dSr09fUhJCQEiqJYaaMvXryAs7Mz+vfvjwsXLrC+k4KCAvj5+UFERISEgFdWVqKiogLl5eV8tS/+KEpKSqCmpkYiJWxtbeHl5YXMzEyiTbBmzRoYGBjwVP0TQAAB2gZz586FrKwsrl27hurqakycOBEURSExMZG1ALOzs2MV8OAuFEJrHyYlJZHtK1asQMeOHZGfn4/v378jICCAlaZLX3/mzJno3r072rVrx6oWm5OTw0NM/Vfx7ds3yMvLY9q0abh69SoGDBgAS0tLmJubQ0FBgSzSmZEZ9Bxy7tw57NmzB69evSISAGfPnkWHDh0watQosqgPDAxkRYW31uKTeZ0TJ05g7969ePToEXGUVFZWws7OjhAHdXV16Nu3L86fP/+b2kJfZ8GCBfD19SUk6OrVqxEeHs5KY2R+t7RtAnBsry1btkBbWxthYWE8czKzIAzTnrtz5w6Kiopw4cIFfPv2jUQw0fo8kydPhqSkJAIDA1nkRUs2z4MHD2BmZoahQ4fi27dvuHHjBiGm+KWv/1uIqdWrV0NdXR0rV65k2cITJ05ESkoKbt26xRLzb2xsxNatW7Fq1SpWkZ6SkhK4u7vDyckJ/v7+LCfp9+/fMW7cOOjp6WHevHn48uUL7ty5Ax8fH5iYmJB3y/29LVmyBAMHDkRISAjRfnr69Cnk5eXh5eWF5cuX4+LFi3Bzc8PgwYNRVFSErl27YsWKFeQa165dg4aGBtHao7VGmRpQFRUV+Pz5M7Kzswk5TPcF9zfz8OFDODg4wM/Pj1Rxbg5/9Bupra1FcnIy5OXlW9TYEkCAnwkCUkoAAn658rT4Mv3/sLAwTJo0iUwAdHqYl5cXmpqaWBMCtxbSnwHtpZg3bx5mz55Nwqtra2vx5csXpKSkQF9fv9ly0zQOHTpEqvOdPHkSkpKSWLt2LS5duoSOHTuyQt9pcBNTpaWlWLp0KdTV1eHs7Ey2MzUFZs6cCXt7+2ZJuU2bNmHYsGEkHJxpiNJYvHgxgoODYWlpSfYxJ7eioiJWSlpzxt+UKVMQHBzMd//PmBrwR8BsS3FxMYYMGUI0z9LT09GtWzdMmDABmpqacHd3J2TVhw8fyLkVFRUYNWoUunbtiu7du8PR0RFKSkowNDRESEgILl26hMuXLyMxMRHt27cnRGFLRve2bdvQqVMnGBoakrQXZnvr6upQXV2NpKQkDBo0iHxrdXV1GDFiBCGCDA0NIS8vj759+2Lu3Ln49OkTHjx4gKioKIiJiRGjo63eyfz582FgYIBhw4YhMTERY8aMgaqqKiQlJTF48GCMHj0akydPhrS0NKtKpQACCND6ePHiBfz9/YnOXWZmJrp164ahQ4eiQ4cOmDp1KvHUFxUV8R0XEhISiOd/6dKlEBISIoLU+fn5cHR0RLt27aCtrQ09PT3U1dUhNjYW69atI9e7ePEiKIqChIQE9u3bx5qPaGJKXV2dVUX2v4gtW7ZAQkICYmJiSEhIIKRNaGgoIiIiyHHMuWTy5Mno0aMHJCUloaenhzlz5pA0/LNnz0JYWBi9e/eGiYkJtLW1W6wi92cRHx+P7t27Q1paGsbGxkhOTiY24dKlS0FRFAYOHAhjY2MYGho2SxBwg94fFRUFe3t7lJaWorq6Gn5+fiStD2DbYEuWLEFISAg0NTUxb948PH78GI2NjdiwYQNMTU0RERHB13HHtK8mTZoEFRUV5OXlETt16tSp6NevH7GDZ8+eDRsbG4wZM+Z3zasPHz6EsbExi5hSVVVFnz59WGLo/yaUlZVh6NChsLa2xvLly8nYs3fvXmLD0Gm+ACe6yd3dHQkJCTzXKi4uhra2NiiK4slEKCwsxJgxY6Cnp4fOnTvD1NQUbm5u5Nvn1kedNWsWunfvjqFDh8LJyQnt27cn39Xz588RHBwMLS0taGhowNHRkWiK2tjYsNp78eJF2NnZAeAIrouIiJAMkbKyMty4cYMlr0G3g/nNcacTPnjwAA4ODggMDMTx48cBAK6urq2i1bp7925MmDABsrKy/6k0aQH++RCQUgLwxYEDByAhIYEjR46Qwba8vBx6enqIi4sDwFnE9+vXD4cPH+ZrBLQWbty48UMvRXFxMRITEzF48OAftiE3NxdlZWWwtrYmQpSfP38mE+GIESNYxzOvRxtHlZWV2LBhA9TU1EgIL/P4wsLCZitmABytLbp0N13qmQZzImN6+ZhG58KFC2FtbQ0jIyN4eXnxiERyg5lq15II+F+dGtDaOHv2LAoKCnD79m18/foVjx49gqqqKvF6rVmzBqKionB0dCTfE/D//vn8+TPGjx+PgIAATJs2DV++fMG6desQEBAANTU1yMvLw9nZGe3btwdFUT/USsrOzoaHhwc6depENF+Y75fft0r37ZMnTxAZGYk+ffogOTkZd+/exfDhw6GnpwdlZWVSLaZr167o1KlTmyz8uA08Ozs7xMfHo66uDkVFRbh06RKGDh0KOzs7iIqKgqIodOrUqVlRfQEEEODPo7i4GBs3bkRlZSWuX78ORUVFktJCV0EbO3Ysz6KIxr1796CtrU3ShTMzMyEiIkI0pABOZML27duxa9cucu7KlStZi5wbN27gxIkTiI2NhYyMDLZs2cLSTMrJyYGxsTH09fV/2jnjr0Jubi4rYqGxsRGurq5Ee4k5F9y4cQP29va4c+cOCgoKMHnyZFhZWSEuLo4QUzdu3EB8fDymTp3KShtvDTDtudzcXPTu3RuPHj1Cfn4+pk6dCmtra0yYMIEQUytXrkRISAjGjRvHIgh+K27fvo2OHTtCX18fWlpaMDAw4PssU6dOhaysLFauXImdO3dCQkICAQEBKCsrQ2VlJdavXw8zMzP4+fk1awcWFxdjxIgRrOhzABg3bhw8PDyIUy4oKAgHDhzgqy30I9DE1LBhw1BSUoJLly4hMDDwp3LktRbo9UFjYyOGDBkCJycnrFixgmyPi4tD586dcfLkSXz69AnPnz+Hp6cnTE1N+b7jkpIS+Pj4wNHREe7u7qwIQICT9klHuL1584b0Kfe18vLyMHPmTFJgobKyEpMnT0bHjh2RlpZGthUXF7NsJ7oaJDPCMz09HVpaWkhPT4e4uDhrnDx27BhCQ0NJxc24uDh4enqS9jAjwQBOxCF9/q1bt+Dm5gY9PT1SIOrPkst0hH1gYCDLESqAAP8ECEgpAZqFl5cXtLW1kZGRQdKBkpKSoKioiPHjx8Pe3h5mZmY8g25r49KlS7C1tQXA30tBG9YlJSU85Bh3yDeNFy9eQFVVlRjY+fn5CA8Px7179/iSBufPn0dsbCzc3NywYcMGMhmuXbuWeMVocBsezRkis2bNgqSkJObOnYvCwkLWPu6+ZP6dlJSEHj16YOPGjbh06RIUFBTg4ODAdwLirgLH/PvvTg1oDTCfJykpCd26dUNubi75XpcsWQJ3d3diPK9duxZubm6YMGECz3uhn+vjx48YM2YMzM3NWWKUz549w4ULFxAeHg5zc3NoaGiwDKHm3vPr169haWkJTU1N8p6b+y65+/bRo0cICwuDjY0NiYoAOIKqy5cvh7m5OWRlZUFRVJuFaDPbOmfOHBgaGiIhIYFofdB9QBc6EJQbFkCA1kFLzh7aqz9x4kSEhoaSv6dOnUpSYOjf7urVqzFz5kxy7vHjx6GrqwuAU0GWWXH2+/fvrDLrNJjz4qlTp7Bs2TLW/tGjR0NGRgZbt25lOUnev3//n0/hY6K8vBzXr1+Hn58fX/Ll4MGDiIyMZGnkNDU1ITk5GZaWloiPjyfEVHMC4H8GzPH+27dvyMnJQWBgIJlDa2pqMHv2bFhZWWHixIlkOzNl/I+05cGDB5g2bRpLF415nYcPH0JbW5to/dy7dw8dOnTAzp07yTE1NTVYunQphgwZwjftb8uWLRAWFoaxsTFevHjBuv/WrVuhoaEBa2tr6OvrQ0dH5zdHfPHDw4cPSaU9ZvXlfxMxxeyXQ4cOYfLkyZCQkICioiLWrl2L+vp65OXlYdSoUejUqRMUFRVhbGwMJyenH5KX+fn58PHxQe/evXmIKe5zFi5cyMr0SE9PB0VR0NDQYBWAqKurQ1xcHDp16sSKhAI4ulZ+fn6Ql5fHw4cPceTIEaL51NDQADc3N1AUhdTUVHIOHdU3cOBANDU1oa6uDqmpqTA3N0doaCh5Rvo7Onz4MNq3b8/6ZrOzs7Fr1y5WVdQ/+1v+8uVLqxeXEkCAvwICUkqAFidJf39/qKurIyMjA01NTXjz5g2mTZsGJycnhIWF/SGv2O9ty9GjR6GhoYFDhw7xeClOnDiBQYMGsTwd58+fZ5Xi5XfNkpISKCgoICQkBFevXoWbmxtcXV15hDIBzgQnKiqK0aNHY8aMGVBQUICnpye+fPmC8vJyrF69GmZmZujfv3+Lz3Pz5k1cuXKFJW44ZcoUKCsrY9myZb8puuT8+fMwMjIiovOnTp2CmJgYFBQUoK2t3aJn5GdNDWgN5OXlYerUqTh79iyA/z/rrFmzYG1tjSdPnqCpqQl9+vRhVVVsjpjKy8vDmDFjYGlpiQULFvAcw0xV5RY1P3LkCBYvXoyVK1eSNMGcnByYm5tDW1ubvOffapz++uuvGDx4MGxsbLBt2zbWvrKyMnz58oUlBvtn0Nx7ZrZ17ty5MDExQXx8fKvdVwABBOAFM2K1uahKV1dXUgyjtrYWffr0IVVCAWDDhg2gKAqHDh0i2x49egRvb2+sXr2aRUgBHP2UwYMH4/nz53yjhAFOtSuKorB69WrWdpqY2r59OyuVTwAOmpqacPnyZfj5+cHT05PHfmpoaEC/fv0gIiICe3t7nnOnT58OGxsbjBw5slmNyNbC9OnT0bNnTxgaGsLQ0JC1r6amBnPmzIGtrS0iIyNbVcOQBrecBK3XBHAi+ZkaReXl5USbp7q6mny3TDuurq4O2dnZcHNzQ+fOnfHgwQMA7O96x44dmDVrFiv67M/Ytnfv3kVkZOS/iojih+nTp0NSUhKbNm3Ctm3bYGdnBwMDA9b4cOfOHZw7dw53795tNrqJGzk5OfD19YW7uzu2b9+OhoYG9O7dm6Ufe/fuXZbEBcCJeB85ciTat29PUuOYkgkJCQmgKIroiwKc39fq1avx4sULJCQkQF1dHevXr0dBQQGamppw4sQJWFhYwNzcHGfOnMH27dvh6ekJPT098hyNjY2oqanB5s2b4eDgwJJk+OWXX9CxY0eeqnvc+LfojAkgwB+BgJQSgGDDhg24fv06j/Hr6+sLeXl5HD16lAyYbe2h27FjB7Zu3Uq2e3p6gqIoLF26lBxDeykGDBhAzq2pqcHIkSNhYGCAxYsX8702/XwZGRlQUVGBpqYmHBwc+GolffjwAYaGhiwxUlFRUSQkJJDrVFZWYsmSJbC3t+dJxaORmJgIHR0d9OrVC2pqavDw8CDes4SEBKiqqmL58uU/rFR2584d4qE+c+YMpKSksGHDBuTl5UFOTg6Ojo5E+JqJnyk1oDXAfEeHDh0CRVFQUVEhQpY0zp07B01NTejp6UFTUxO6uro/9H5yE1NWVlasb6ml6Kj4+HjIy8sjJCQELi4u0NTUJFF9L168gKWlJXR1dX93RbonT54gNDQUdnZ22LFjB9nemgbM0qVL0adPH5ZXlwl+xNTUqVMFxJQAArQBsrKyoKamxiKT+I1ZO3fuBEVR8PX1haGhISv6Zv369ejQoQNP5FNRUREMDQ1BURQp1gFwoq+8vb2J5x/gLPDo1JSjR4+SirQrVqxAu3btsHLlStaYOHbsWLRv3x67d+/+qaJqfxbU1NTg4cOHJHKZe16tqqrCuHHjoK6ujsWLF5MIOIDz/mNiYjBixIhW71vm+H7w4EFiW0RFRUFOTg4hISEsu6+mpgZxcXEYMWJEm5IutBPn5s2bkJOTw9q1a9GtWzeWc/LSpUvo06cPsrOzybaLFy+SCJuRI0di/PjxaGxsxNOnT2FpaQltbW0isdCcM6Y1bJ4/kv73T0FTUxM+ffqEXr168Yh+Dxw4EOrq6li7di1Lb4nGb+2PnJwcBAUFoVevXlBXV4e+vj4PYclMjaMJ0oKCAoSGhkJERITYhfRxtbW1WLNmDd/3u2jRIsjIyBCHIhPXr1+Hr68vZGRkYGtry4qGYn5DZ86cwfjx4yEkJIThw4ejvr4etbW1JJVQAAEE4A8BKfUfBndqgLKyMjQ1NXHnzh0eg0dPTw+mpqbYu3cvyyvWFkZnXFwcVFRUkJKSQvL7T506BVtbW5iYmODkyZPYsmULXy8FwCETYmJiYGVlxRJK5J4Eq6urUVpainfv3jXrufn48SNMTU1RUVGBV69eQUFBgaU5dfv2bTQ2NqKiogLfvn3j+zzLly+HlJQUCSOmvcyXL18mx8THx6Nz584k1x0AS/MoNTWVeL/z8vJQW1vL0qP4/v07bGxsQFEU34gtGn93akBrgNmu0tJS5OfnIyoqCu3atcPRo0cBsNt74cIFrFmzhhUe/SMyh0lMjR07Fra2tpgxY0aL5xw6dAiKioq4ffs2AGDz5s0QEhJivdM3b95ARUUFAwcO5GuUtRSRRhNTTk5OrGiv1sKFCxcgLCyMyMjIZosUMNuckpICVVVVzJw5U+DdE0CAVsb9+/cxaNAgGBoaIiMjg2znl9qdlpaGiIgITJw4kYxxR44cAUVRrKgpgOMgef/+Pe7evYsuXbqgf//+WL16NQ4cOABXV1cWqVVcXAwfHx8MGjQI69evB0VRLAHqZcuW8SWmJk2ahJcvX7Z2l/yrwEynf/DgAR48eEBshJqaGowYMQJWVlZITU3liURqSw3PtLQ0bNiwAbt27QLAWcBv2rQJ5ubmGDhwIGuOqqura1PSJS0tDdLS0uT5IyIiQFEUpk+fTo6pqamBn58f0WyiK/NxV3FjFp159uwZTE1NoaurS+y2trRx/s3kbFlZGXR1dYnjllmspWfPntDT08PcuXP/VLT958+fcfz4cWzZsqVZJ+mbN29AURSGDh1KSLDCwkIMHDiQLzFFg+mkLC0thaurKyE83717hxMnTiAkJAQxMTHkG8/NzUVtbS1fjdWJEyfCxMQEUVFRMDExgYyMDMLCwlol8k4AAf7tEJBS/1EwB2Y6RaCpqQlmZmbQ0dEhZAvAMTaCg4MhKiqKAQMGtGm7tm3bhu7du+Pu3bus7Q0NDbhw4QICAwOJl6Kl9MH8/HyMGzeuWWIqPz8foaGhrLxyppA3rZPw+PFjKCkp4dKlS9DQ0GB5BR8/fowBAwawyCN+GD58OImWOXLkCMTFxUm6BDMEf82aNeQ53r59CzExMURHRyM+Ph7CwsKs1Lz8/HxoaWkRr3VlZSXCw8Px/PnzZo3Dnyk14I/i/PnzJKd/1KhRCAgIQH19PT59+oSQkBCIi4sT8fHmjMyWjCN++k55eXkIDQ39oXc6JSUF/fr1A8DRDhAVFSXvuby8nJQ2/vjxI6tS47lz5wBwUhLmzJnD4wVkIjs7G/7+/vDy8moTzYAbN25ATEwMoaGhvyliaunSpcjJyWn1dggggACclI+oqCj06tWLRUxxV2FlRogAnEiF0aNHQ0NDg1XNqU+fPjA0NCTOHjqVTElJCU5OThg8eDDPnLp161bo6Oigffv2WL58OWsfwHGYtGvXjieVT4Dmwa0RSS/eu3XrhnHjxqG8vBzV1dUYNmwYrKyssGLFClbEFPc1WguvX7+GkpISKIpiOT6qqqoIMRUaGtpspEpbtMfU1BTbt28HwIlU8fb2hpKSEjZt2oQlS5bAw8ODVIcE/v/baKmKG8AhpszMzGBgYEAccQK0DH7vubKyEjY2NsT2Af4/PgQHB0NFRQUTJkxo1W+Eu8oe/e5PnToFUVFRjBgxgkVMDRo0CN26dcO1a9davC4t8dC3b1+kpaXB19cXTk5OCA4ORo8ePRAUFASgeU3QCxcuoHv37kT3rLGxEcuWLYORkRGLmBKMkwIIwB8CUuo/COaAumfPHoSEhBBipampCUZGRtDR0cHNmzeJh2rYsGEtEh6t0RaAE/o/cuRI1j7uAfzDhw8sD11zAzyTmGLqAn3+/Bl2dnbQ0tLie25WVhbU1dVJtAjtnQsJCWEdN3XqVFhYWDSbske3zcjICOvWrcOlS5dYOggNDQ2YOXMmXxHHiooKHDx4EMLCwhAVFSWaWUxCxcTEBDY2NtixYwecnZ1hYWHB0sTi967+rtSA1kBdXR3CwsJgbm4OV1dXSEpKshZjnz9/RmBgICQkJPDo0SMAvGQls09oXYCbN2+S9BSAPzH19etXVgVDftdctGgR4uPjce7cOdZ7piMZ5s+fzyJ6ysrKEBoaCltbWyQnJ4OiKGJ8t4Rnz57xrY7YWrh27doPiSmBt08AAdoOzDGGSUxxp+EVFBTA0dERAQEBPNf4/PkzYmJiYG1tjRUrVqBfv34wNjbGmzdvAPx/3KqqqkJxcTErOrK+vp78xl++fAkVFRWoqakhKiqKFMBgjgErVqwARVE/1EsRgI0lS5ZAWlqapApNnToVFEURp1x1dTWGDx8ONTU1VoRaa4F7LquurkZmZib09fVhZWXFs2/Lli1QVlZmiea3FpqzV4KCguDv70+23b17F+PHj4eKigpcXV0xcuRIvov9lqq40c/9/PlzKCoqEk02AZoH8/18/PgR3759I4VbsrKy0KVLF1J9kdbcHDRoEDIzM5u1nVqjLdu3b8e2bdtIxButicpNTHl6esLNze2H1167di08PDzQpUsXJCcnE4Jp9uzZGDRoUIvnpqWlQU5OjqUPW1ZWhuTkZAgLC2PkyJE/vT6rAAL8nRCQUv8xMAfzO3fuYMCAAejevTsiIyNJhElTUxMsLCygra0NHx8f2NraQldXl68I+J8Bc4K6e/cuysvLMXjwYPTp04fn2Orqapw/f77Fa/ADk5havHgxioqK0Lt3b+jq6jYbZfXp0yfo6uqSEtu3bt2Ct7c3tLS0cO7cORw+fBgTJ06EqKgoIT+A5sPXFyxYACcnJwgLC7O8j1+/foWPjw+rmgcTp06dgrCwMCQkJFjpdvRES2sjmJiYsERTuavs/UypAa0BMzMzUBSFKVOm8Oz7/PkzgoKCIC0t3WIEW0JCAkRFRdGzZ09ISEjAzc2Nb8UpgN0P3H3LBF3xhaIoVnWViooKuLu7IyYmhuec+/fvw9LSEhRFIS4uju89/wpw3++3EFMCCCBA64Aeu5v73fMjpgoKCuDg4ICePXs2u9Ch5z81NTVISkqSCKmW5nBmEYePHz8iJycH2dnZ2L59O+zt7REaGsqXmNqwYYOgBPnvBJ0WCXDSvyUkJEhUNe0wqqioQEpKSqs7ArjnMTr6qa6uDqdOnYK6ujrPIr6qqgrHjh1rU6cEd8GXp0+fomvXrjxFPriF9FtyTjZXxa2mpgYfPnwQOFl+AOa4NHPmTJiZmUFNTQ0mJiZE8y4jIwPCwsKwtbVFnz59YG1tDW1tbdK3reXQZrYlPj4ePXr0wPbt28nYBgCnT5+GkJAQRo4cSezlkpKSFtvA/H6amppYxZMAwMXFBWPGjOHbDvrft2/fho6ODim4QyM3NxcKCgoQExNDcnLy73haAQT4b0FASv1HERsbCx0dHYwZMwaBgYHo2rUrIiMjcf/+fQCcQTYpKQkjR47EqFGjeHSb/iyYA3pcXByMjY3x7t07JCYmQl1dHdnZ2axjCgoKEBAQgIsXL/7ue+Xn52P8+PGwsrKCuLg4evXqxVeckEZ1dTUiIiLg4+NDtl2+fBmhoaEQFxeHgYEB3N3dWYLizH558eIFnj59Sq5979496OjowNbWllR8+fjxI3x8fGBtbU0mbW5thpKSErx58wb79++HrKwsS8uKed+SkhJW1NjPmhrwZ1FbW4svX74gLCwMffr0gZ2dHZYvX06MDmZqpqOjIzw9Pcm5zOe5efMmDA0Ncf36ddTX1+Py5cskAuvkyZMttoH5no8fP449e/YgMzOTvOuUlBS0b98eu3btwuPHj/Ho0SN4eHjAxMSEr8B6cXEx7O3tYWRkBA8PD9b9/wphVLotpaWlPOSTgJgSQIC2R2xsLDZt2kScAz8ipvT09LBt2zZ4enqy5rLmFuUFBQWYMGECzMzMsGTJErK9ufGFvn9mZiYRMKbnqHXr1sHe3h4REREkupSpdyhA8+B+r2VlZVBTU8OJEydw48YNVnRtbW0t4uLiSJVdGm1R5XjVqlWIjIyEg4MD1q9fTwjHU6dOoWfPnvDw8OB7jdZoS3BwMCncQrfF0tISiYmJrCp60dHRCAsLQ2lpKV+S40f2Cr8qbk5OTkhKSmrV5/m3Y86cOZCUlMShQ4ewZcsWjB8/Hu3atSMyBa9fv0ZMTAyGDx+OCRMmtPq6gYnVq1dDTk6Ox/lIS0+cPHkSIiIiPAL9/NpCfz+7d+9G3759yfby8nLcvHkT7u7uLK097mvQ2798+QIzMzP4+fmxovhfvXqFwMBA7N+//18peC+AAK0FASn1H8Tly5chLS3NqlS2ZcsW6OvrIzw8nG/1NqBt8qDz8/MREBBASrPW19cTUfUbN24gPz8fHz9+hLe3N+zt7f+w4ZCfn4+IiAh4e3uzjHhuDSkaL168gJCQEI+gdE5ODioqKprVXEpMTIScnBy6d+8OeXl5LF++HFVVVbh06RL09fWhra0NbW1tWFhYwMLCgi85lp+fj7KyMrJIKS4uxvbt2yErK4vRo0eT4yZNmkTKIAO8E+XfnRrQGmhJHysiIgJWVlYsYgrg9FdpaSnfc5cuXYpx48Zh6NChrO3379+Hn58foqKiWNpiTDC3xcbGQkpKCsrKylBVVYWGhgaeP3+OmpoaTJkyBWJiYpCRkYGpqSlcXV1ZUXnc1/7+/Ttu3ryJgIAA9O7dm4cY4yYMWwt0O06ePAlHR0eSDvr48WPiNb969SrExMQQGRn50+qMCSDAPxlOTk4wMDDA3r17yW+9JWJq2LBhoCgKOjo6PySkaDSnsdjcfU6ePAlhYWGsXLkSr1+/Zu1bv349nJycYGVlhaFDh4KiKJaItAC8YM5FBQUF5H0lJSXB2toaQkJCrEigoqIiuLi4sPTAWgPc7zshIQFSUlIYO3YshgwZAklJSYSGhhLn2cmTJ6GrqwsTE5NWbQeNxMREdOjQgdhZr169wpQpU2BiYgI1NTUsXLgQOTk5uHHjBkRFRfHw4cM/fK/fUsVNgOZRWloKBwcHQkDRWLJkCSiKIg7j5oTE/wz69OnDE4UZERGB8ePHA+CQYfv27YOzszNcXFyIzZuRkQEXF5cW0wfpbUeOHIGYmBgrc+H48eMICwtDQEAA38yKJUuWoH///ggODiZpfi9evICCggI8PDywZMkSnDt3Dm5ubkSIn/saAgggwP8hIKX+g7h8+TLk5OR4xFE3bNiAdu3aYciQIay0tLbCypUroaGhATs7O5amz7dv32BpaQkNDQ1ISUnB1NQU5ubmPEKWvxffvn1DY2MjFixYQFLzAE45YV9fX5bHDuBU0QgODkZhYeEP9YQATrlsWVlZHD16FL/88gvi4+Ohra2NhIQEABz9guPHj2Px4sWsEHimYbRgwQJYW1vDxMQEbm5uRK+qpKQEO3bsgLS0NBwdHeHq6goVFZUWJ/y/MzWgtbFu3ToMHToUs2fPJgZHeXk5IiMjYWtri4ULF+Lr169wcnJCREQEOY/7W4mOjgZFUdDT08OXL19Y+zZs2IAuXbrw1QhjvvesrCxYW1vj/v37KCgoQHZ2Njw8PKCoqIiPHz8CAB49eoQ7d+7g119/ZWmj0de5d+8eDh48iBMnTpDrnj9/HgEBAXBzcyPE1KxZszB//llvsRsAAGdmSURBVPw2ez/Hjh2DqKgokpOTcfXqVTg5OUFfXx/Hjx8n3+X169dBURRGjhz5U0bQCSDAPxHMsalfv34wMjLC7t27f0hMZWVlISUl5XeL5tIRw7a2tqRqKz9UVVXB29ubzFs0mI6Tw4cPY/To0fDz8yMFHATgD+Z7njt3LoYNG0YcgpmZmTAyMkLv3r3J3FFYWAhvb2/Y2dm1ybhPt+fevXtQVVUli2mAo8djZmaGqKgoVFdXo7q6GocOHcKgQYPaTE80JSUF7dq1Y2kwVldXIzExEW5ubpCUlMTatWuhrq4OT0/PZivD/hb8lipuAvDHly9fICkpiS1btgDgvKfGxkbU1NTAx8cH48aNQ0NDQ6v3Z01NDaKjo3kIxOjoaLi4uGD27NmkymJkZCQCAwOhr6/fYnpnVVUVq1r2+/fvWRUEmXj8+DH5VpltmD17Nrp3747hw4ejd+/eaNeuHUkPff36NQYMGAAdHR307NkTTk5Of3r9IoAA/wUISKl/OfgZtleuXIG0tDSJTmJG62hqakJfXx8TJkzgWbS3Np48eQIdHR0ICQmREFzmgH3+/Hns3r0bJ06cIMZZa0x4EydOBEVRpOTxkydPMGDAABgYGEBXVxfbtm3D58+fkZWVBUlJyWZLyTKxbds2rFu3jpUeAXBKZisoKODw4cN8z2ManUlJSZCVlcXOnTtx4sQJGBoaQkNDg5TWLi8vx8WLFzFw4EBER0e3GH3zd6YGtAaY38G0adMgJSUFf39/mJmZQU9PD8ePHwfA6ZORI0dCT08PSkpKMDU1JYZDc5P/jBkzQFEUVqxYwapgd+3aNejq6rZYTW7//v3w8fFBYGAgj6iqvb09HBwc+PYjsy2HDh2CuLg41NTUoKSkBG9vb7LvwoUL6NevH9TV1eHu7o4OHTr8sLrjH8W7d+9gaWlJKmoVFhZCTU0NPXr0gJSUFI4fP06i9W7duoUXL160STsEEOC/COaY8OHDB1haWsLS0hJ79+4lkZ8/IoF/73yYn5+P8PDwFotZlJWVQUtLCzt27ADAOy8wo4qZEaoCtIyEhATIyMggLS0N+fn5ZPuGDRtgZmYGJSUl2NrawszMDGZmZs1qXv4RTJs2DatWrWJtu3fvHhQUFIgDkv4eTp06hY4dOxL7gPmNtdaCmvlM79+/R3h4OISFhbFt2zaeytCbN2+GkZER2rdvDw8Pj1av4iYAL5p7z4MHD4anpychUOl3MWDAgL9ELH7FihXEHr9w4QIGDRoEdXV1LFq0iGjibtq0iZURwWwnAMyfPx+urq6Qk5PDiBEjiGOQ277h7gPm358+fcLs2bNx/fp1ABySa8qUKejQoQN2794NgJOJ8P37d+Tk5PywKJMAAgjAgYCU+heDOYhWVFSwBuagoCAoKCjg1atXZNvnz58RERGBOXPmQEJCgpBWrd0WJp4/fw5lZWU4OzsTEqyllK3WwsyZM9G+fXts3boVAIeQ+/LlC4YPHw4bGxuoqKjg8OHDMDMzg7Ozc4veua9fv0JdXR0URRFBcubk069fP9jY2LTYngsXLsDU1JRMcseOHYO4uDjU1dUhKytLiClu1NfX/zSpAW2BZ8+eYeLEiSTl8MGDB4iKioKysjLRMamqqsLVq1eRkZHBN/rs/v37uHHjBqsccGxsLDp06IBZs2bh7t27ePXqFTw8PGBtbc3qT/rfjY2NqKiowPDhw6GoqAgDAwNyDN3fu3fvRs+ePflWxqN/e1VVVejbty927dqFT58+4dixY9DQ0GBVOsrKysKaNWswbty4NhUOfv36NRYvXozy8nJ8/vwZWlpaJD3UxsYG+vr6OHz4sCDFQQAB2hAxMTHw8/ODra0tpKWlIS8vj7179/5QY+qPori4+IfVsMzMzDB8+HDyNz2uPnnyBJs2beJJdxegZRw7doxFAAGcyJPs7Gw0Njbi7du3WL9+PWbPno09e/a0qhOupKQEzs7OcHR0ZNkBd+7cgYiICM6dOweATTDq6OiwosnbCvHx8TAyMkJISAhUVVXRoUMHbNy4kccG/PDhA06dOsWjvylA64PZ9+/evSPVOgFOtW5ra2tMmjQJBQUFADjki7OzM+Lj49u0XfX19bC2tkb37t1JemllZSWKi4vJMU1NTfDx8cHAgQP5fiPTp0+HlJQUli9fjlWrVsHKygr29vZkHfBbcPToUVAUBTU1NWKXApw1xJQpU9CxY0ekpaXxnCeIkBJAgB9DQEr9B7Bw4ULY29sjODiYEBGVlZVwcXEhA/SWLVvg7u5ORC21tbUxadKkVrk/d8W/Y8eO4fnz54SEevr0KeTl5eHh4cGqvNLahgcdbgxw8uPHjRuHrl27Yt++fazjnj59ioULF0JVVRXCwsJQVVVlhfrye7bHjx+jd+/eUFNTI1VA6PbPmTMH7u7uPELmzGOuXbuGOXPmAOBUDunevTvWrl2Lly9fQl5eHtra2nj69GmzzwP8HKkBrYn09HQoKCjAwMCAVVnlyZMnGDp0KFRUVPgK7DIN+cTEROjr60NFRQUWFhYs8fOEhARQFAVhYWGEh4ezdAO4DQg6pa+wsBCJiYno3r074uPjeSL7VFRUeDRYaFy9ehVubm4YMGAAeR+NjY24du0a1NXVeUpw/xWGN21wjhs3DkFBQUTQfMiQIaAoCpqamgKRcwEEaCPs2rUL3bp1w6NHj/D161dUVVXBw8MDGhoa2LdvX5sRUwBY2nnV1dUsx9X8+fNhZGSEjRs3ss6Jj4+HpaVls/OhAPxx6NAh2Nra4vv373j27Blmz54NVVVVaGpqwsPDgxWxS6M15mf6fX758gX9+vWDi4sLSyczKioKEhISeP78Odn27ds36Ojo8NhFrY0jR45AREQEd+/eRU1NDb58+YKkpCS0b98emzZtavab/9ntln8LpkyZAjU1NUhJSSE0NJSQUKmpqUReo3///rCwsICenl6rRwHxe/+VlZXw9vaGnJwcKcoEcOz5kydPwtPTEwYGBnyrmb59+xYGBgY4evQo2ZaTk4OoqCg4OjqSSKsf4fPnz4iOjkb79u2RmZkJ4P/2Yn19PZKSkkBRFC5cuPC7n1kAAf7rEJBS/0IwF8orVqyApKQkZsyYgT59+qBXr16IjY0l+6Ojo2FqaopevXrB29ubGMGWlpY8goZ/FgkJCVBQUICioiKkpaXh5+dHxBGfPn0KRUVFeHl5kcmvrXD48GEYGRmhf//+6NKlCzp37owdO3bwTIIvXrzAli1bmo1Smj9/PmbMmEEiSbKzs2FoaAh9fX28fPkS3759Q3V1NRwcHBASEkLOq6urQ1VVFT59+sSayOm/PT09MXXqVACcSdjZ2RnCwsKsNC9u/J2pAW2F06dPo0+fPujSpQtu377N2vfkyRMMHz4cnTp1IhpT3Fi6dCmkpKRw+/Zt1NTUYNasWaAoihUBOHv2bFAUhW3bthHyhdu4Onz4MKSkpIhX7OvXr5g0aRJMTU0xevRo5OXl4ddff4WnpyccHBz4esQaGxuxf/9+qKioQEZGhuWVpompnj17QkdH54911g9Af9tv3rzB27dvWeWOGxsb4efnh7i4OHLcxIkT8csvv/CN+hJAAAFaB4sWLYK1tTWqq6tZ44aTkxOUlZWxd+/eNotKon/rJ06cQL9+/aClpYVx48YhPT0d1dXVCAsLg4WFBQYNGoQlS5YgNDQUYmJif4ne5D8Z/Mb/9PR0yMnJoU+fPpCTk0NERATWrVuHtLQ0qKur48aNG23SFub8fuvWLTg5OcHa2ho7d+4EwEnn9Pf3h7CwMBYtWoTly5fDy8sLhoaGbW4bbN68Gebm5jzzbWxsLDp37ow9e/YIIqL+Jhw9ehQaGho4cOAA0tLSIC8vDzs7O7x9+xYAcOPGDcybNw9Dhw7FrFmzWl2fi/kbKisrQ1FREfm7vr4e7u7uUFBQIBFTT548QUREBMLCwoh2J3dqcUFBAZSVlXHgwAHWPT5+/IgePXrwzRxoLrrp+/fvGDx4MLp06UI02ehvta6uDuvXrxek6gkgwB+AgJT6F+PatWtYsGABqdBWVFSElStXQlVVFRMmTCDH0dXeaCQnJ0NBQYEVtvt7kZGRwbrm5s2bIS0tjcuXL+P79+/IyMhAcHAwbGxsSMras2fP0KFDBxZp1tp4+PAhunTpgs2bN+Pr16949eoVJk6ciA4dOmD79u08x7dkFC1evBgURWHx4sUsYsrY2BgSEhIwNTVFZGQky3Nz5swZREdHQ0lJCd26dYOvry82b95MrvnhwweoqqoSb87379/Rv39/3L17t9kJ8u9MDWgtNPds169fJ+V4mdUiAc67bE6kvb6+HmFhYSRd4ejRoxATEyNeYua3GRMTg06dOmHbtm18K8xdu3YN/v7+6NWrF7KysgBw+jcuLg5du3aFtLQ0+vXrh9DQUCJSzO95ysvLcejQIUhJSbHKDtPHX7x4EcbGxizCqDVx6NAhKCsrQ15eHg4ODkSwFOCk82ppaWHTpk0YPXo0xMXF8f79+zZphwAC/NdBj1lz585lEdE0AXXjxg106tQJqqqqOHPmTJu149ixYxAWFsb8+fOxb98+hISEQFRUFK9fv0ZhYSFWrlwJZ2dnWFpaol+/fgJR8x+AOe6/efMG9+7dI6n/mZmZiI+PR1paGnG8ffr0CUZGRjxOl9bGpEmT0KdPH1haWkJUVBQ9e/YkxFRFRQWSkpJgaGgIGxsb9O/f/y9xWu3duxedO3cmUcj0vS5fvgyKokBRFIlEEaBtwW2v3Lx5k1WFLj8/HwoKCrC1tWVJfjDt49ayJ5nXnDNnDnr37g1paWkMGzaMCIk3NDTA3d0dioqKhJjKy8sj5zJtqE2bNiErKwuFhYXQ1NQkRR4aGhrIc/v6+rLWRAC7T7Zv344pU6YQ0h7gRJeGhoaia9euPMQUjZ/JxhZAgH8CBKTUvxRXrlyBvLw8ZGVlWXnPxcXFWLlyJdTV1XkG4RcvXmDYsGGQkZH5U6V3LSws4OzsjMbGRvLf8OHDERUVxTru2rVrcHV1ZbXj/fv3bWoInTp1Crq6uizPS1NTE2JiYtC5c2ccOHCALxHVHGmydu1aUBSFhQsXEmLqyZMncHNzg6ioKKtU9qZNm6CgoIBJkyZh0aJF2LhxI/T19SEnJ0ciowDA0dEROjo62LlzJxwdHWFra8vSNuLG35Ua0FpgPtO5c+eQmZmJ/fv3k203b95Enz59YGJiwvqWmeB+nvr6epibm2Pnzp04c+YMRERESOXB+vp6LFmyhJX3P2XKFJb4PTfu3LmDvn37QktLixBTX79+RXx8PMzNzVlRRtXV1azIpKysLDx8+JAQVocOHYKioiIreo7uB/qY1gLdjk+fPkFNTQ2bN2/GwYMHMWHCBCgrK2Pp0qUAON49Z2dn6OnpwcTE5DeHsgsggAA/RnPzx8ePHyEuLo5Ro0axtl+8eBFDhgxBXFxcm43VJSUl8PT0JFVnS0tLISsri5iYGJ45sKamhiUcLAAvmH2WlJQEfX19SEtLw8rKCpMmTWJVA2toaEBpaSl8fX2bja5tLezcuRMSEhJ48OABioqKkJeXB3d3d1hZWbHmu6KiItTV1bW6KHNzz1ZQUAAnJycEBQWxqi9nZ2dj/Pjx2Llzp2BR/xeA+d2uXr0a0dHR0NfXR1xcHOu4goICKCoqwsnJ6S+xD2bMmAFJSUmsX78eqamp8PX1hampKSnMUltbCx8fH7Rr144lUn7//n1QFIWrV68iPj4eMjIyJMJr586daNeuHcsRXFNTA1NTUyxYsIBvO+Lj4yErK4tJkyYhJCQE6urqRNqksLAQ4eHhEBMTw+XLl9umIwQQ4D8EASn1L8Xbt28xZcoUiIuL85R//vbtG1avXo0uXbpgxYoVZHtRURGOHz/erCbOb8Hx48fRq1cvlJaWknsBwJgxY+Dt7c0jmjxv3jzIysryRKj8GUOcOclyG9fHjx8HRVFE04c2eh49eoROnTqBoijs3bu32Wtz6zoBnImcJqbokOEnT57AwMAARkZGqKysxIYNG9CpUyekpaWxjPtXr14hMjISsrKyhCB48OABPDw8YGRkBF9fX5bO0c+UGtDamDx5MuTk5KCtrQ1RUVFYW1uTKLrr168jMDAQ5ubmZBuN5tLlJk+eDHd3d4iJibFK/X7+/Bm+vr5Yv3496zubPn06ERbftWsXz+/g9u3bCAwMRM+ePYlRVlBQgNjYWFhZWWHevHloamoi31x6ejqUlZVhYmICeXl5BAcH49KlS2hqasKBAwegrKyMgQMH/vmO+wFu3bqFpKQkjB8/nrTtw4cPJCKS/u4ATt/wixYTQAAB/hiY49OOHTsQExODUaNGkTSSffv2QVxcHBEREXjy5Al+/fVXeHt7Y+LEieS81poPmaisrISJiQnu3LmD3NxcKCgoYMSIEWT/sWPHBKl6fwCLFy+GjIwMzp8/DwAICQmBjIwMiYaqqanB3Llz4eHh8Zek0s+YMQN2dnYsDbFPnz7B0tKSVWWRidZKm+PWXMzIyGDpQO7ZswfOzs5wdnbG1atXcePGDXh7eyMgIIAcIyCm2g7M97xw4UJ07twZoaGh6NatG7S0tHiiNL98+YL27dtjzJgxbdqu3NxcmJmZkagkgOPgo52AV65cAcCJ8ps0aRLrt1NWVob4+HgICQmxIr7pYxYuXAiKohASEoKhQ4fCxcWlWU2s06dPswTNDx48CCEhIRaZ+/37d3h7e8PV1bX1O0IAAf5jEJBS/wI054nKzc1FYmIiNDQ0sHDhQta+oqIiHDp0qNUNoatXr4KiKNy5cwcTJ06Evr4+AA5xIykpSRblNDIzM2FpacmqoPFnQPdFYWEhAPCkqlVUVMDZ2Rl9+/Zl6eV8/PgRERERmDFjBqviGTMv/eLFi6AoioQQM7F48WJ07NgRa9asIdEu2dnZMDU1hbS0NCiKIsYY3Ra6bW/evIGzszPMzc1Zelr5+fksr+XPmhrQGti2bRu6d++Ohw8foqCgAPn5+bC0tISpqSmJNrt48SIcHR1ZEXfMPnn8+DFu3LhBjJBffvkF0tLSsLGxIQRTfn4+fHx8YGtr22wa4+vXr2FmZgYbGxvk5OSw9l26dAnKysrQ19cn6YRFRUWIj4+HtrY2Fi1aBIBDonXr1o1UMNq9ezfatWuHtWvXAuBU4Tt8+DC6du2KIUOGtE4n8kF5eTlGjhwJcXFxuLu7s/bRxJSKigoR2RdAAAHaBvHx8VBSUsKwYcMQHx8PiqKwYsUKlJWV4eTJk1BTU4OcnBwUFRVhbm7eKpFJ9PhYVFSEp0+fsiJ3v3z5AgcHB6xatQoaGhoYPnw4Of7Dhw+IiIhARkaGQNfnB2AWMCkrK4OnpyeJxKCjdOm0cdopd/jwYUybNq3VtXj4tWvBggUwNzcndgn9XV24cAFdu3aFgYEB34IhrXV/gFNwREFBASYmJhAWFkZERARxDGZkZCAgIADt2rWDlpYWLC0t+QpVC9B2uHfvHqKionD16lUAnLHBxMQEbm5uPILd3759a/NI+y9fvkBeXp5VLRLgONt79uzJV/+J2aY1a9aAoih06NCBtJ/5LZ06dQphYWEICgrC+PHjeWxyGlu3boWjoyMAToS7qKgocXCWl5cTB2lpaamgup4AArQCBKTUPxzMgfDSpUs4ePAgTp8+TQyQt2/fYurUqawFMzdaa4KhB/24uDh06tQJEhISxPAAgODgYMjIyCAzMxNv3rxBcXExXF1d4ePj06rGR05ODiiKwogRIzBt2jRWRT+AQ4A4OTnBz88Pr169wvv375GUlARzc3OWqOzZs2exePFiVspYXFwchIWFeaKpnj9/DjExMVAUhd27d5PtWVlZ6NGjB5SVlbF69Wqynbu08dmzZ9GuXTtcu3aN53mYHk7g50oN+LOgn2vq1KnEO0obCNXV1dDV1YWfnx85/uHDh3zLmSclJUFTUxM9e/aEoqIixowZg2/fvuHixYuQlpaGmZkZevXqRcTemd5pZv/Qi4Zjx47Bw8MDDg4OLGKqqakJLi4ukJeXR//+/cl7/Pr1K5KTk0mY+Jw5c9C/f38AnJRUdXV1VopOaWkp6uvrkZGRwdJoaC0w++b+/fsYOXIkhISEeL7bjx8/YuLEidDV1UVxcbFgESCAAG2Ac+fOQVlZmRRlOH36NCmwQKOyshK3bt3CnTt3WkX3jx7Xnjx5AgsLC6iqqkJFRYUVDbVgwQJQFAUfHx/WuVOnTkWvXr2Qm5v7h+//XwBz7qAXpk5OTnjx4gVOnz4NERERsoitqanBpk2beFLQ23qBn52djQ4dOmDWrFms7SdPnkRAQACSkpLa1EZYtGgR5OTkyHPTkeV9+/Zlpe09efIEr1+/ZlUyE6DtsX//fpiamkJXV5cVHf7+/XtCTNEFiZhore+W37dXUFAAS0tLTJkyBbW1tSy7xN/fH8OHD2/xmsXFxfj111+RkJCADh068DiE+YHfvp07dyI0NBSnTp1i/ZYBDpkaHx/PWl/8zLa2AAL8EyAgpf7BYA7UU6ZMgaamJtTU1GBnZwdPT09STezt27dISkqCrq4ukpOT27xdiYmJxEvBLU49ePBgKCoqonv37jA0NISxsXGre8Vu3boFiqIwceJEjBgxAvLy8li4cCHOnj1Ljjl48CCcnJxAURR69uwJaWlplo7Wtm3boKCggOjoaKIhRCMhIQEdO3ZkLfBzcnKQlJSEgwcP8kxu79+/R0xMDKysrFgRa0yy6eXLlxASEvqhqO3PlhrwR/Do0SNkZmay0gqjoqJgZ2dH/qZJ1VOnTkFWVpZHdJ85+aempkJWVpaEdNORQbQX69dff8XevXsxZ84cHD58mLXgY15n6dKlmD17NhFePXr0KNzc3ODo6IhPnz4B4Cw8wsPDcfz4cfLuGhoa0NTUhMbGRmJkJyYmYu7cuaioqICCggJGjRpFjj969ChfUf3WAFPXivkd0npxOjo6LC0tgBNRR0cWCiCAAH8e3IuTnTt3wtfXFwAnSkZERAQbN24EwNF2unfvHs81/syYTd//0aNH6Nq1KyZPnozLly9j7Nix6NSpE4ngrKurw4QJEwhpMWPGDIwcORKioqICXbkfgGmvjBgxAr6+vqioqICjoyPMzc0hLi7OKibx4cMH9O7dmwiM/5XYvn07OnbsiLi4OGRlZeHNmzfw8fFBYmIiOaYtFtR5eXmIjIwkGpFHjhyBhIQEpk+fjm7duiEwMJAVmd6WbRGAP16/fg1fX1+IioryRCDl5ubCwsICRkZGuH//fqvfm/mec3Nz8fXrV7Jt/fr1oCgKa9euJRkBFRUVsLCwwLx5837TNSsqKhAdHY0OHTrg1KlTZPvs2bOJQDrQ/Nrj+fPnRNaDabNVVVXB09MTw4YNEzjyBBCgFSEgpf4FWLx4MXr06EG8sHPmzAFFUbCwsCAi12/fvsXYsWMxaNCgVh9EmYvz6upqrFu3DleuXCGTAbeX5dq1a0hPT0d6enqbVYKLi4vD3LlzAXBCeWNiYiArK4vRo0eTaoQAR0T72rVrLI9dWloaunTpggMHDhBtLG5MnjwZ7dq1w6xZs3Do0CH4+/uzInq4nyc/Px/jxo3jIabo4w4fPgw7OztWO4CfNzXgj2LPnj0wNjZGQEAAS9z92rVrEBERISKWNI4ePQo9PT18+fKF51o0ERQcHEx0kTIzMyEuLk48WtzkDA3uBV98fDx69OiBzZs3E1IK4GiQ9e7dGwoKCkhMTISNjQ3s7e35Cs/v378fXbt2xadPn7BixQqIiIhARkYGEydOZEXGDRkyBGPGjEF1dfXv6bofgv5WTp48CR8fH9jb28PPz48Yk69evcLw4cOho6ND9GwEEECA1gVzfn3+/DkATuqHi4sLtm/fzkoBATge90GDBrFSt1sDr1+/hpCQEMsRlZOTg06dOmHy5MmsY1NSUuDg4ABra2sMGTIE2dnZrdqWfzPy8vJgb2+PS5cuAeBE82ppacHa2hoAZ14uLS2Fj48PHB0d/zYH0eHDhyEjIwNFRUUoKirCxMSkzdPkqqurkZ6eTohXVVVVQnwsW7YMFEXB1dWVFVEvwF+PDx8+wN/fH46Ojti3bx9rX05ODqKiotqUKExOToa6ujoMDAwQHBxMZDNSUlLQvn179O3bF+Hh4aQYS3M2LVPT886dO6iqqkJ1dTViYmJAURSSk5Ph6OgIfX393/w7PHToEISFhZGQkIDLly/j0qVLcHd3h6GhIWmHgJgSQIDWgYCU+ocjNzcXfn5+yMjIAAASZjpp0iTo6urCxsaGREwxS6a2hZAlfR8a79+/x7Bhw/gSU0y0hZG2atUqmJqasjShrK2tISUlBWNjY9jY2GDPnj0sXSmAo0Xl7OxMPMk0ysvLcffuXUL8AZywdEVFRejo6MDR0fGHGiDNEVNlZWXw8fHh8br8E1IDfg927twJYWFhpKWlsdINAY5Y5IwZM6CmpoYFCxbg+/fveP/+Pfz8/ODp6UkIKCaamppQVVUFe3t7ZGVl4caNGxAREcGGDRsAcBYDK1eu5BFG59cuGRkZlt5KeXk5IXRfvXqF6OhoODk5ITw8nCU8zzw+JiaGlFFuaGhAWFgYhIWFSZRXRUUFEhMT0aNHD1a1mNbE8ePH0bFjR0yePBlz5syBvb09FBUVSVRfdnY2oqOjISsriyNHjrRJGwQQ4L8I7iIeW7duhYWFBerq6vDw4UNYWlqic+fOrCpPVVVV8PX1bXWPe2NjI6ZOnYru3buziP6UlBRQFIWAgACkpqZi9+7dLHK8sbGR5zkEaB6pqalwdXXFgAEDSHRvVVUVduzYAVFRUZiYmMDZ2Rl2dnasqPC/a17Oy8tDVlYWLl++3GYOQW7Qz7xgwQL4+vqSeXX16tUIDw+Hl5eXIDLqJ0BOTg58fX3Ru3dvHmKKRluk7NFkaVpaGlJSUmBqaopevXoR2/3QoUMYP348goODMXny5BadrXT70tPT0blzZ5IdUV1djUWLFsHW1haDBg3ia8M1h4aGBuzbtw8KCgpQUFCAmZkZ/P39//bfsgAC/BshIKX+BUhPT8fHjx9x9+5dKCkpkUX51KlTQVEUVFRUWIRRW7D68+fPh62tLby8vMjCHOCQZsOGDUPHjh2JJ/GvMkBMTU0xc+ZMAEBkZCSUlJRw48YNPHr0CF5eXjA0NORLSunq6hKSDwDWrVuHfv36gaIoyMvLw97envTh+/fvkZeX95t1EJjE1JIlSwAAffr0gZGREcvr8k9KDfgtyM7Ohp6eHqsUL8D+FnNzc7Fo0SKIiopCVlYWmpqaZFEHcIi3qqoqfPr0iWUIhIeHQ0lJCV26dGFVRfn69SucnZ2JuHhzWLRoEdF/evnyJVatWgUtLS3Y2NiwogyYvyHme75//z40NTXh4ODACgnPysqCi4sLunTpAisrKzg5OUFeXp6VJvpnwF0coKKiAi4uLqwINACIiIiAgoICnjx5AoAjahobG8uTEimAAAL8MYSEhGDVqlWEmAA4BNDgwYPJ3ytWrICcnBxiYmJw9uxZnD17Fh4eHm3mcc/LyyNp4+vWrcOiRYsgISGB5ORkHD58mFQzVVBQgIuLC44ePdpq9/4voLq6GgsXLoSsrCyMjY1Z+xoaGki1sOnTp2PDhg1/GQn0e/BXLKjpbzoqKgr29vYoLS1FdXU1/Pz8SFofIEjZ+xmQk5MDPz8/uLm5sezLtkJaWhq2bNlCUuOamppw7949GBoaQkdHhxDm3EQ582/u7yYzMxNdu3Yl6dFMlJSUsIoH/R4UFhbi1atXyM3N/cPXEEAAAVqGgJT6h4E5AHMbsCkpKSyP3caNGxESEsJKH2qLdqSmpkJKSgozZsxAREQEunXrhpiYGLI/NzcXI0aMAEVRrEV7W4F+1rVr1yI8PBze3t7o0aMHj25HUVERz7mFhYVQVFTE8OHDcfHiRQQHB8PAwADR0dE4d+4cDh06BHV1dcyePRsAmo1sagn5+fkYP348bG1tISMjg549ezbrdfmnpAb8CGfPnoWamhpevnzJd+HF3Pbp0ydkZmayvLmnTp1CdHQ0lJSU0K1bN/j6+pK0xefPn8PGxgZ6enqE0CsuLoaXlxeryl5+fj7fts2ZMwfdunXDpEmToKenh379+mHmzJmYNGkSDAwMSDU/Zlvp9t67dw/79++Hs7MzhISESNoLvb+2thZbt27F/PnzsW3bNrx79+5P9OL/MWPGDCQnJ7Oi86qqqmBiYkIE9ZlRgpaWlhg4cCD5WxANIYAArYfhw4dDSEgI27ZtIynfiYmJrOIGAGeOdnNzQ/v27WFra9vmHnfaCaKtrc0TsUwvqFavXo2oqCg8ffq01e//bwK/+T0/Px+rV69Ghw4dEB8fT7Y3t1j9WefnvwK3b99Gx44doa+vDy0tLRgYGAgW9T8hcnJyYG1tjfHjx7fpfV6/fg0lJSVQFEVsOeD/xJSxsTH09PR4ZA6Yv6H169dj2LBhGDhwIBYuXIjGxkasXbv2h5qdrUH+C0hUAQRofQhIqX8IXr16RQbn5gbUMWPGQFNTEwDHKAoMDERKSgrZ3xaht7dv38aGDRuIiGBZWRm2bduGTp06sYipnJwcLFiw4C81Ql6/fg0ZGRmIioqyyIAf9cOFCxcgLi4OdXV1GBkZ4eLFi4TA+vbtG4yNjUkE1h9Ffn4+IiIi4O3tTRYl3H3zT0sNaAkpKSmQlpYmf/P7hp89e4bLly/zbN+8eTMUFBQwadIkLFq0CBs3boS+vj569OiB6dOnA+CEeOvr60NWVha2trawsLCAqakp6RMrKyukpqY2SyLGxcXB19cX69atw8uXLwEAd+/ehYmJSbNE0smTJ6GsrIxTp07h0qVLMDIyQq9evYgoZ1t+69u2bSORT0yjzcHBgQgqA/8npiZMmECqGwoggACtA+Z4QledpaNBx40bx7dKVG1tLV69esWqdtmWY0VBQQEmTJgAQ0NDor1Ht4OGgBxoGcy54sWLF3jy5AmZW6qrq5GamgoxMTFMmzaNHFdfX9/qcgn/dDx48ADTpk3DokWLfkrNSwE4+Pz5c6uTLty/gerqamRmZkJfX584WZnHPnjwAHJycggNDeV7vYSEBMjIyGDu3LlITExs8VgBBBDgnwEBKfUPwL1792BkZITFixeTRSY/I+fKlSswNDSEiooKTExM0KtXr1ZNC4iMjERlZSX5++bNm6AoCuLi4iSSB+AQJ9u2bYOQkBAmTpzIc52/wgihJ9S1a9fCxsaGJ9rlRygsLEROTg7P9m/fvsHBwYFvaPDvxbdv35pN+/s3pAYwcfDgQQgLC7MqIHIjMTERI0aMYH2rGzZsQKdOnZCWlsaKCnr16hUiIyPRvXt3rFu3DgAnwmrJkiVYtGgRdu7cSfpk2rRp0NTUZC3CGhoaSN9/+PABjY2NrL6rqamBj48PvL29+UYnFhQUIDIyEitWrCDbr169ClNTU5iZmZFUP+Y922JhcvHiRUyePJkQaWfOnIGqqipiY2NZx4WGhiI0NJS1UBJAAAH+HLi17mJjY9GhQwekp6djxIgRiIyMxKtXr3Dr1i3cu3cPb9++xYkTJ1BWVkbO+Ss87j8qtCHAbwO9+O3evTvk5eWxfPlyfPnyBXV1dVi+fDmpLCfAb4Pg+/u50VpjE/M6NTU1+PbtG/n7zJkzUFNTg5ubG+ucpqYmvHjxgq+T9datW+jZsyfReE1PT2dVNG3t9gsggAB/DQSk1D8AVVVVCA0NhZ2dHZYvX94sMVVbW4vLly9j3rx5mDVrFpnwWyNy5u3btwgODmYRAx8/fsSCBQsgKirKEzlER/RQFEUW7q2N35I6d+XKFWhra7M0ov4oCgsL4evrCysrq1aNRmpsbPzXpwa8ffsW4uLiCA4ORm5uLtlOv8PS0lIEBwdj1apVZF9GRgYoisKxY8cAgOd7fvPmDZydnWFmZsajDUajoaEBkZGRCAwMBACMHTsWJ0+eJPuPHDkCHR0dUv68oqICS5YsgZeXF4yMjPgKYt68eRPu7u6wsLBgiag3Njbi8uXLsLCwgKWlJWvh+WfBvD+T6Nq1axdERESQmJiIjx8/oq6uDqtWrYKysjLc3Nwwc+ZMDBkyBF27dhVU1BJAgFYE8zfJ1GebMGECOnTogB49ekBERARmZmYQExND9+7doampCSsrq7+FGKaJKTs7O8yYMeMvv/8/Ecx3fPToUcjKyuLo0aP45ZdfEB8fDx0dHUyZMgUlJSUoKyvDqlWrQFFUqzitBBDg3wDmb2j+/Pnw9/eHjIwMEhIScObMGQCcqHMdHR14eHjwvQa3bZuRkQF9fX0AHEKKWdG0oqJCoI8ngAD/UAhIqZ8c9GBcXV2NYcOGwdLSEqmpqSRlhzngl5WV4cSJE3zPb01s3LiR6Gbk5+dj/vz5EBYWxqJFi1jHVVZW4tSpU63uDaMN+pKSElRXV/PknHPDy8sLpqamf9hr8vXrV1I5him83RbpkP/m1IC0tDR07twZgwcPZol95+XlwdvbG3Z2duRbqampwejRo6GhoUE0kv7X3p3H5ZT2fwD/nFY1ZSkShaKylRRKRSS7ECLNWCIy2ZmYkn0LYRhbyb5LqrGNLWRECuVBtjGEFlGktEh9f3/0u8/TLZ6Z4a5U3/fr9bye6Wz3dW7nnPs63+u6vhfRf79zyTmfOnWK5OTk/ucMe6dOnSJBEMjOzo7k5eXp5s2bRES0b9++EtOzExEtWrSIxowZ89nhBU+ePCETExMSBEGc3lqioKCAIiIiyMDAgDp37lwiaf3XePz4sTiMNDQ0lJYsWUJERJs3b6b69evTTz/9RCkpKZSfn0/nz5+nHj16UJcuXWjgwIFSMwsyxr5O8Wf24sWLydnZmc6cOSMumz9/PgmCQIsWLaLk5GR6+fIlpaamSvWOLa/AlKurK3Xt2vWTORXZp23bto02btwoTk4isWrVKtLV1RVnMn3x4gUFBQVxDyBW5X38fPPx8aE6derQrl27KDg4mFq0aEGWlpaUkpJCeXl5dOzYMWrevDmZmZlJ7fepevvFixepf//+tHPnTqkZl4mKeo+7u7t/cqQDY+zbxkGpCkDyUP44MFV8pp8XL15Q27ZtqU+fPqVa2ZUkAm/evLnYEyQ5OZmWLl1K1atXpxUrVnxyP1lV0iTnduzYMercuTO1a9eOTExM6Pjx42IuHwlJACMmJuarZhqLjY0lBwcHmjJlSqnmQajsQwM+fPhAgYGBpKioSLq6utSzZ0/q3r07WVpafjLYl5SUJM4eVXzISUFBgXgd3L9/n6pVqya2uElIhvRJdOjQgQRBoPHjx4vLBg0aJDVTZHGS4xcPPN6+fZsePHhAREWBNAsLC7K0tCwxJLGgoIAuXbok00pRdnY29ezZk/T09Gjr1q0kCALt2bNHXB8QEED169cnT09Pevr0qdR5FO/dyBiTnZkzZ1Lt2rXpt99+o+fPn0utmzp1KqmoqND27dulfquJyndYSUpKCqWkpJTb51c0L1++pCZNmpAgCDRhwgQikv79d3JyIisrqxL7cWCKVXWS51x8fDy1atWKLly4QEREly5dImVl5RIJyQ8fPkxDhw4V9yv+nNyxYwedPHmScnNzKSkpiXR1dUuMxMjJyaGePXuSi4tLhWmsZYz9FwelKohP9Zj65ZdfKC8vjzIzM6ljx47UrFkz8QVUVg/kTx3n1q1bZGpqSq1atZIKTPn6+pZJ4OTYsWNiz6zo6GgaNmwYKSoqlphdT5aKTyVbGj2kqtLQgNjYWJo0aRJ1796d3NzcaP369Z/NifV3uVCCg4PJxsZGKhBz/Phx0tbWprFjxxJR0eyPXbt2pZ9//pnk5eX/59CV4td78f9OSEggCwsLGjZsGD18+FBcZm5uTl26dJHqJVEaCgoK6D//+Q8ZGBiQkpISrV+/noikk5xLAlNeXl507969Ui0PY1Xd77//Tnp6emKvz/z8fEpLS5MKUk+dOpUEQSjRg5l9uz6u8xQWFtLNmzepc+fOpK+vLwYfJdstXLiQunXrxvlrGKOiHlHF0zAQFeUANTExIaKiSWnU1NTEHurv3r2jQ4cOUVpamlTduvj9NGPGDNLW1qY1a9ZQamoqERXNRK2mpkbOzs60Y8cOCg4OJnt7ezI2NpZpLl3GWNnhoNQ3ZNCgQeTj4/PZB+nHgan27dvTwoULycrKipo3b/7ZWdy+VPEfhbS0NKkZOe7du0ctWrQoEZjy9vambt26ldqPQV5eHg0YMEAMLDx79owMDQ3J3d39s2WXpdI4Lx4aUORzwb7PBabevn1LvXv3Jjc3N6l/l4yMDFq3bh2ZmZlJXReSfEvy8vJSgdN/eq2sXLmSOnToQGPHjhV7TEkCU926dZPKVSUrxRMpJyYmUuPGjalBgwbUqlUrevnyJRH9d4Y9oqKZCpWVlWnOnDmV9jph7FsQEhJCRkZGlJubS/fu3aN58+ZR48aNSVNTU6rnzLp16/herCD+rs5jbGxMxsbGdP/+fUpPT6ecnBzq2LEjDR48uLyKzNg34/Xr19S5c2eytbWlbdu2ictv3rxJDRo0ID8/P6pZs6bYqEZUlKOzf//+n21UDgwMJC0tLYqNjRWfo5J7MjIykiwtLUlfX5+sra1p6NCh3/Qs1Iyx/42DUt8QPz8/kpeXp+XLl/+jwJS7uzvJy8tLJWSW9TA5oqL8GPb29lSrVi0aNWqUOOX1nTt3qFWrVmRqaioGpopPc10aAZyMjAwyNjamy5cvU0ZGBtWvX18q8LB582bxZb0iqKpDA/7ttVE8MCUJ3vXv359MTU2lWsUkx83MzKRff/2VjI2NpaZlf/36Na1fv57k5eVp/vz5ny2P5O+Ph779+uuv1L59exo7dqxUjyl9fX3q16+f1OyUsiQZCvj06VO6fv06WVlZkbGxsdhqWDwwdejQIXE2PsbY1/vUpBrnzp0jc3NzsrCwoHr16tGoUaNo48aNFBUVRaqqqhQWFiZ1jIr+zK7s/mmdx8zMjGrVqkXm5uY0cuRIMjExkXkPdcYqGsm1/+LFC3JycqIuXbqI9w0R0fjx40kQBKme6tnZ2eTg4EB9+/b9bOPgpEmTxDqc5P2n+LbZ2dlivj5JGfhZy1jFxEGpb4y/vz/JycnRsmXL/jYwlZ2dTatWrSrVPEdz584lTU1N+u233ygiIoLs7e1JV1eXHj16RERFeXZMTU1JW1tb6oVc1pWz4omaXVxcyNXVlRo0aEAeHh5ihVDSc6Z4YuxvDQ8N+HLJyck0adIksra2Ji0tLTIyMpJqFZN8Z5LvKiMjg3799Vdq1aoVjR49WjyOJDClpKRE06dP/+znXb58mXx8fMTAj4Qk2OXu7k6PHz8moqIee6WVWPPp06ckCAItWLCAiIrO748//iBra2syMTERExavXLmSfH19S6UMjFVVxZ+979+/p8zMTPHvI0eO0Jw5c+jw4cPic+LZs2fUtm1bunLlSpmXlX29v6vz3Lp1i+zt7UldXV2qXsIvwqwqK94z6fLly9SpUydq37497dy5k4iKeno7OjqSiooKLVmyhGbNmkVdu3alli1bfnKWY8kxbWxsyNnZWVwm2SYvL49iY2OlGuSIODDMWEXGQalv0KZNm/5xYEqiNCpECQkJ1L59ezFHRnh4OKmqqtLWrVuJ6L8/Djdv3qThw4eXWnfZ5ORkaty4sdjld+PGjdSoUSOysrKS+hHz9vYmIyMjMVDwreGhAV8vOTmZRowYQb169ZLqHfhxZUaS9P7du3e0YcMGMjExkQpMvXnzhpYtW0YdO3b87D3m5eVFhoaGNH/+/BK976ZMmUK1atWioUOHflUS/X9qzZo1pKysTEuXLiWioorXH3/8QR06dCANDQ0aNmwYCYIgzizIGPt6xZ8rK1eupN69e1Pr1q3J29u7RPLy/Px8Sk1NJQcHB7KxseHhIxXQP63z3L59m0xMTMjU1FRsjOOGI8aIpk+fTv379ycLCwtSV1cnIyMjMTCVnp5OPj4+ZGlpSX369KGpU6eK7y55eXmfPN6qVauoadOmdPbsWanlDx8+JCcnJ4qNjS3V82GMlR0OSpWz4kPdir8cb9y48W8DU6UtMTGRmjZtSikpKRQaGiqVnDAnJ4e2b98uDmGSKI2KeHp6OvXr1488PDyIqCjQ4OHhQaamptS3b1+aPXs2DR06lGrVqvXN/kDx0ADZKT6t+scBqZUrV5KLiws1bdqU/Pz8KD4+nvLz82ndunVkampKbm5u4raZmZmfvP8SEhLE/54zZw6Zm5vTnDlzpHpM7dixg1q0aEEDBgygpKQkmZ3bx8+B4iTPhOKBqQcPHtCMGTPI1dWVbt++LbNyMMb+y9vbm+rVq0cLFy6kPXv2kKKiIrm7u4u/f3l5eRQQEEDdunWjtm3bcl6TCurf1Hlu375N5ubm1LBhQ6nec4xVVTt37qRatWrR9evX6dWrV5SYmEjdunUjS0tL2r17t7hdRkaG1H7F0yRERUXRmTNnxJQgN27coA4dOtCgQYPEvJ1Pnjyhfv36cfCfsUqGg1LlqPjLdEZGhjgMR2LDhg1lFpi6desWXbhwgc6fPy8u++uvv6hly5Y0c+ZMqlWrFm3YsEFcFxsbS/369aNz587J5PMlL+OfO8/Tp0+TvLw8nThxgoiKesIEBATQoEGDyN7enjw8PCg+Pl4mZSlNPDRAdj5umfby8qK6devS6tWrKSAggGrWrEkDBw6krKwsevv2rZj8fODAgVL7Fb/uwsLCqGXLlrR161axsuPt7U3m5ubk4+NDiYmJREQ0a9Ys+uWXX0rcs18qJSVF6to/efIk7dmzp8R2ksDUihUrpJZ/nPuKMfZ1JPfjb7/9RoaGhnT58mUiKprOXFFRkRQVFcnR0VF8dgcFBdGKFStKdTg9kx1Z1Hni4uLIxsZGvAYYq8rmzp1LNjY2VFBQID4/nz9/ThYWFmRoaEg7duwosU/xes+MGTNIQ0OD6tSpQ9ra2uIkPxcuXKB+/fpR7dq1qUGDBtSiRQtq06bNZ4f9McYqJg5KlZPiD1FfX1+ysbEhIyMjGjJkCD19+lRcv2HDBpKXl6cVK1aUWmBq+/btZGRkRPXq1SNdXV1ydXUV1y1evJgEQaDJkyeLy7KysqhPnz7Uo0cPmf0YSLrAS87x494n+fn5NHLkSHJ1daU3b96U2L8i9CLioQGlJyYmhoyMjMQ8LjExMSQvLy92GycqusZ8fX1pxIgRn/w+jx8/TioqKrR27doSPQDnzJlD7du3J319ferduzepqqrS3bt3ZVJ2f39/atSoEV27dk28jmfOnEmCIND+/fvF7QoLC+nDhw9iwlDOH8WY7N28eVMMNufn59OxY8fE4MSJEyeoZs2atHfvXrp27RopKyvT6NGjS+ST49b7b9vX1Hk+/rf93LAjxqoKSb3F19eX2rZtKw5tlgSNzp49S9999x2ZmJjQkSNHSuwn2aZVq1YUHh5OCQkJNHz4cNLS0hIDWS9evKCoqChat24dHTlyRLwPOfjPWOXBQaly5uPjQ/Xq1aN169ZRZGQkaWpqkoODA8XExIgP7E2bNpEgCFLdX2XF39+flJSUaPfu3RQXF0cTJkwgRUVF2rdvHxH9d6icnJwceXh40JgxY8jOzo6MjY1l1kqxZs0asrS0pIKCAiooKKAHDx6QIAjk5uYmFVTYtWsXaWtrizmjKtqPEQ8NkJ2Pk1tGR0dTu3btiIjo4MGDpKamRhs3biSiomF6p0+fJqKi6/njhOhERZMG9OrVi2bOnCl13OI9kE6cOEGzZ8+mKVOmyCwgJSlTkyZNqG3btnT9+nWxPD4+PiQnJ0d79+6V2n758uVkYGBAmpqaMuupxRgjCg0NJRUVFZowYYI4XDctLY2ePn1K6enpZG1tLQaDU1NTxZlTP35usG/Xt1DnYawyun37NikoKEjNbExU1ODXr18/mjVr1ifvnYCAAFq4cKHUzHxERG5ubqSlpUU7d+4Uh/MVx8F/xioXDkqVo1OnTlHLli3p4sWLRFTURVVVVZU0NDTIzMyMrl27Jj7AQ0JCZB6ECQ0NJUEQpFouoqOjSRAE8vPzk9p23bp1NGjQIBoyZAjNnTtXpkMULl++TA8ePJA63r59+2jYsGHUsGFD6ty5M504cYLy8/Np8ODBJYZffYt4aEDpOXXqFK1YsYKuXr0qLrt06RLp6OhQYGAg1ahRQ+q7PXv2LA0YMEAqkPRxz7q3b99KdS8vXtkpLCwUE6cTyfaFRHK9Z2dnU7NmzcjU1JRu3LhBREWBSi8vrxKBKW9vb9qxY8cnK2mMsS+Tm5tLbm5uJAgC9erViyZPnkwpKSni+r/++ouaN29OJ0+eJCKiV69e0fTp0+nGjRsVroGkqvpW6jyMVVbbt28nRUVF8vT0pOjoaPrzzz+pd+/e5OXlJW7zcR3K2tqaBEEgJyenEvfXmDFjSEdHhzZt2lRicgnGWOXCQaky9PGD+Pr16+KMcqdOnSJNTU3auXMnpaWlkaamJvXu3ZsiIyOlXqBlVSHKzc2lH3/8kZo0aUJr164Vlzs5OZEgCDR06FCaMWMGrVixgtLS0j55DFm3Uly6dIlatGghtlC/fPmSHjx4QH369CELCwsyMTEhFxcXat68ufji/i3ioQGlZ9u2baSjo0MeHh4UHR0ttc7FxYUEQaAFCxaIy3Jzc8nBwYEGDRr0t8GkNm3a0JgxY8S/Jf8Wt27dosDAQDEwJeuhopLW9+zsbGratGmJwNTs2bNJTk6OunfvTj169KBatWrJtKcWY6xIVFQU1a5dmxwcHMje3p6mTp0qzrz55MkTql69Oo0fP55CQ0OpZ8+eZG1tLT4POFjxbfsW6zyMVUbBwcGkpaVFurq6pKurS2ZmZn/by9DJyYmqV69Ox48fL/EsHTRoEPXt27dCpOlgjH05gYgIrNQREQRBAAC4urpCXV0dv/76K5KSkqChoYG+ffuiQ4cOmD9/PjIzM2Fvb49r165h5MiR2L59e6mUKTk5GcuXL8fVq1fh7OyMyMhI3L9/H56enmjSpAl2796N//znP3jy5AnU1dWxceNG2Nvbf/XnFhYWQk5OTuo7AYC4uDgMHz4c8vLyOHv2LGrXri2ui4qKQmhoKH755RdUq1YNd+/ehY6OzleXRdYCAgIwefJkbN26FSYmJggMDMTmzZuxc+dOuLi4IDs7G56enggICMC4ceOQn5+PR48e4eXLl7hx4wYUFRXF74dJO3DgANzc3LB9+3b07NkT1atXl1p/5coV+Pj44MmTJ5g/fz5ev36NEydOIDExEbGxseJ3KwgCBEFAbm4uCgoKoKqqCkEQsHTpUgQFBWH8+PFwd3cXjztz5kxERETg5MmTqFWrVqmeY3Z2NszMzKCiooLt27fDzMwMABAcHIyQkBCoqalh8uTJMDY2LtVyMFaVFBYWgoggJycHT09PaGpqorCwEKGhobC1tYWXlxfq1q2Lo0ePYvjw4dDR0YGGhgbOnTsHRUXFEr9l7NtUXnUexqqapKQkJCYm4t27d+jYsSPk5eXx/v17KCkpAQDu3bsHQRCQn58v1me6d++O27dvY8eOHejSpQsUFBTE433uvYExVomUY0Csyige3Y+Li6OWLVuKOW6IihL4tWzZkg4cOEBERT0mPDw86NGjR6XeMpecnEwTJ04kPT090tDQoOfPn4vrJC0au3fvpgULFsi0JTghIUH8Dvbs2UPDhw8noqLeUtbW1mRsbPzJfDnR0dH07NkzmZVDlnhoQOlJTU2lzp07iz0LJTIzMykqKoquXbtGRET379+nUaNGUYMGDcjOzo7c3NykvlvJvXjs2DFycnIiQ0NDmjhxIoWEhFBOTg4NGzaM2rVrRy4uLuTn50c//PADVa9eneLi4mR6PpJy3L9/n8LDw+nGjRtirrR3796RoaEhmZqa0vXr18X7MD8/n3OZMCZD8fHxUr95RESrV6+mNm3aUF5eHq1evZratWtHU6dOFYfyJSUlSU1Gws/siqW86jyMVWXF76XZs2eTubk5NWjQgNq0aUPTpk0T1/Xs2ZPq169PZ86cKXH/cf2HscqNg1JlaOvWreTi4kITJkwgov++mObk5JCBgQH16NGDAgMDqWvXrmRubi6uL+3AVEpKCk2ePJnatGkjFTz51NCxry1LYWEh5efnU48ePaht27bk4+ND8vLyYsLvwsLCTwamPk5s/a3hoQGlKzU1lVq0aEGhoaHiso0bN4rfr7a2NnXp0kVcJxlyI1G8cnPkyBFSUVGhJUuW0L59+2jw4MGkrq5ODx8+pNTUVFq7di117tyZLCwsyMnJiW7duiXTc5Hc18HBwaStrU2GhoZUu3ZtsrKyoqCgICIqCkwZGRlR27ZtKSoqirutMyZjwcHBpKioSHp6erRv3z6KiYkR19nZ2dGyZcuIiGjhwoVkaWlJ06dPLzErLL8kVUxlWedhjP3X4sWLSUNDgyIiIuj58+fk4eFBgiBIPX979uxJgiCUSNHAGKvcOChViiQV1sLCQkpOTqbhw4eThoYGDRgwQNxGUgm6d+8eGRkZUZs2bahbt27i+OuyehmVtB5aWlqKlXGi0q2QtWjRggRBIE9PT6nlxQNTrVu3FnNMfeuSkpJoypQp1L59e/rll1/IycmJTExMaOfOnXTp0iUaN24cWVlZUb169cjIyIjOnj1b3kWuMFJTU0lXV5fGjBlD4eHhNGjQIDIxMSEPDw86ffo0HTp0iPT19WnhwoVEJB2EKn4PvX79mnr06EGrVq0iIqKMjAyqW7cuTZkypcS9lpubKzX7nixFR0eTuro6bdiwgV68eEFnzpyhsWPHko6ODgUHBxNRUY9JLS0tsrW1/eaDsoxVJHl5eeTh4UE6OjrUpEkTsrW1JQcHBxo+fDglJCTQkiVLaNy4ceL2ixcvpsaNG0s1OLCKrTzqPIxVZe/evaN+/frRoUOHiKiogbBmzZoUEBBARCQ10/S0adP4XmSsiuGgVBm6ceMGubu7k7KyMm3btk1cLnmBzs3NpVevXpVb4tTk5GSaNGkSWVtbk4+PT6l9Tl5eHr17945MTU3JwMCA7Ozs6Pjx41KtzpLAVLNmzcja2poKCgoqRG8RHhpQes6ePUs1atSgxo0bk6mpKYWHh4s96dLT06l169Y0b948Ivp8MPfdu3dkZmZGUVFRlJCQQDo6OjR27Fhx/ZEjR2Q+VK84yTWwadMmsrW1lSrngwcPaNSoUWRnZyf2yMjJyaE///yz1MrDWFWVnJxMkydPJkdHR/Lw8KCYmBiytbWlgQMHUuvWrUkQBDp8+LC4/fbt2/klqZIpqzoPY1XRxz1JMzIyqGHDhhQeHk6nTp0iNTU1cZREXl4eLVu2jMLDw6X24WcuY1UHZ1MuZRs2bEDLli0BAGZmZpg4cSJGjhyJ5cuXY+/evQAABQUF5OfnQ1lZGZqamhAEAYWFhVJJ/sqCtrY2Zs2ahSZNmiA1NRVUSjnwlZSUoKqqioiICMTFxSE7Oxu+vr44efIkCgsLAQCCIMDGxgaHDh3Cnj17ICcnVyGSG2pra2P27Nno168f9PX1sX//fnHdhw8fAADDhg3D3LlzoaCggIKCgvIqaoVjb2+Phw8f4uzZs4iLi0OXLl2gqakprldXV0f9+vXFZOZpaWmIj4/HrVu3xG2ysrKgpqaG6OhodOnSBb169YK/vz8A4NmzZwgODsbjx49lfu1Ljlf8+n706BGSk5PFbQwNDdG/f3/ExcUhMzMTAFCtWjU0adJEpmVhjBU9q3/++Wfo6OggNjYW169fR0REBH766Sf06tULDRo0QLNmzcTtXV1dIS8vz8/sSqSs6jyMVUUfT9hTrVo1dO3aFVu2bMHgwYOxatUq/PjjjwCKJiG4dOkSUlJSpPaRl5cvs/IyxspZ+cbEKr8zZ86Qjo4O2draistiY2Ppxx9/pObNm9PevXvLsXSflpaWJjX0UBYkx0lMTKT4+HjKyMgQhySlpKRQ+/btqVOnTnTs2DEiIvr5559p1KhRMvns8sBDA8pOamoq9enThywtLcXhdrdu3aJ27dqRnp4eNWrUSKo3lK+vLwmCQL1795Y6jre3NzVv3pwSEhJKpZxRUVHk5eVF+fn5dP78eTIwMKDNmzfT27dvxW3+/PNPatq0qZi4nTFWupKSkmjixInUpk0bWr16tbhckvuP80ZVfqVR52GMEa1fv56MjY3FvwMDA0leXp4GDBhA6enpRET06tUr6t27N9na2nIdmbEqTCDipiFZkUxZWlxBQQEiIyMxYsQI6Orq4tKlSwCAuLg4BAYGYv/+/Thw4AC6d+9eHkX+nz51Pl+C/n8K17CwMMyaNQs5OTlQUFCAh4cHBg0ahEaNGuHFixcYPHgwXr9+jRo1auDOnTs4ceIErKysZHAm5SMlJQVLly7F9evXYWdnh8WLF5d3kSqVV69eYcuWLbh06RJSU1Pxxx9/QFlZGTdu3ICtrS1+/PFHODg4IDg4GIGBgVi9ejUmTJiA/Px8eHp6YuPGjZg9ezYKCwuRkpKC/fv34+LFi2jdunWplNfLywu//fYb7t69CwAYP348QkJCMG/ePPTo0QPa2tpYsGABDh8+jMuXL0NLS6tUysEYk5aSkoIlS5YgOjoajo6O8Pb2BlD0+80t9VWHrOo8jLEiZ8+ehaurK5o0aYKIiAgAwPLly+Hr6wszMzPIy8sjNzcX7969Q3R0NBQVFfm5y1gVxUGpUhAWFgZHR0fx7+KBqYYNG+LixYsAgOjoaERERGD69OmV/gH8+++/Y+jQoZg7dy7c3NywYMECBAUFYejQoZgwYQIaN26MV69eYdeuXcjKysKQIUOkhk5UVCkpKZg5cyaqVauGgICACjEEsaKIi4vDnDlz0KRJE6xcuRIKCgq4d+8ezMzM4OnpiUWLFgEAHj9+jGbNmmHSpElYuXKluL+vry9+//135Ofno1mzZvD09BSH2sqSJCj77t07NGvWDMOHD8fSpUsBAFOmTMHp06fx4sULGBoa4smTJzh58iTMzMxkXg7G2OcVb0To0qWL+PxgjDH29/6uYb5+/fq4fPkygKL3pAcPHiAxMREtW7bE6NGjoaCggA8fPpR56hLG2LeBg1IyIHnpBIC7d++iTZs2cHBwQFBQkLjNhw8f8Pvvv8PR0RF9+/ZFWFiY1DEqc8tAWloahg0bhg4dOsDHxwepqalo3749atWqhdevX8PR0RGTJ0+Gnp6e1HdZWaSnp6NmzZqQk5OrlOdXnt68eYMaNWpAEATk5+dj3rx52LJlC2bNmoWpU6cCKAo++fj4oG/fvujcuTPq1KkDJycnVKtWDUBRRerDhw9QUlKSWbk+/neW3N/Lly/HuXPnEBAQAD09PQDAtWvX8NdffwEALC0t0ahRI5mVgzH2z3EjAmOMfZ3/1TBffMTI5+pJjLGqiYNSXykxMRE6OjoAinoD2djY4Pjx45g1axbat28vleg6JSUF9vb2uHv3LsaMGYPNmzeXV7FLneTHJjExEWpqajh9+jTatGkDdXV12NrawtbWFgEBARg3bhxCQ0Ph6OgIHx+fSv1CzkMDSo/kektKSsKKFSsQFRWFkSNHIjMzE8uWLcOECRPQunVr7N27F8+ePUNycjKaNm2KKVOmoF+/fqVSpgsXLuD8+fPw9PSEiooKFBQUcPXqVfTu3RurVq2Cq6trqXwuY+zLcSMCY4x9mS9tmGeMMX5D/grnz5/HqFGjEBkZiWnTpqFPnz7Iz8+Ho6Mjli1bhsjISLi4uIjbKysrw8LCApcuXcKmTZvKseSlTxAEHDx4EHZ2dnj58iXs7e3RuHFjbN++HXp6eli2bBkAwMjICN999x2ePn0q9lyprDggVXokL47169eHl5cX2rVrh7Vr18LHxwfBwcFYtGgRBg0ahKCgIMTExMDLywuNGjWCgYGBzMtCRMjJyUFMTAxWrlyJ3r17w9vbG2lpabC0tMTPP/8MPz8/JCQkyPyzGWNfR0NDA3JycuIsnowxxj7t434NjRo1wtatWxETEyP1/qOgoIB27dqhWbNmOHLkiDjrHmOMSfDA3a+grq6O3NxcjBw5Eunp6bhz5444Rb2k98VPP/0EW1tbuLi4ICgoCPLy8mjfvj3k5OQqZVdVSctybm4uQkJC4OHhIfXin5aWhpycHBQWFgIAXrx4gdmzZ6N///6oXbt2eRWbVSLa2tqYPXs25OTkcOHCBcTGxqJLly4AIF53EydOlHnuAsm1LwgCVFRUMGPGDIwbNw5+fn64cOECDA0NMXPmTCgoKMDAwABxcXGVumcgYxUZNyIwxtjnFe/9v2zZMjRs2BDff/89HB0dIScnB09PT7i4uIgjRiQN84GBgbC0tCzPojPGvkEclPpCRIS2bdvCxsYGq1atgqWlJVJSUtC8eXMAgIqKChwdHdGwYUN4enpi165d0NTURGhoqNgKW9kCUkBRj5ULFy7Ax8cHGhoa6Nmzp9T6+vXr4+XLl5gyZQo+fPiAo0ePIjY2lgNSTKbq1q0Lb29vFBYW4tChQ/jw4QN+/vlnKCkpicGo0ghI/fHHHzhz5gzq16+Pdu3aoU2bNpg3bx5mz56N1atXIzIyEvHx8Xj8+DEEQUD//v1lVgbGGGOMsdJWPCD17NkznDlzBtevX4eamhr69esnNsxPnz69SjXMM8a+HOeU+kKSB3JISAjk5OSwfv16KCsrY/z48ejTp0+J7dPT01GrVi0IglDpZ5e4dOkShg8fjqSkJFy/fh3GxsZS5zxnzhzcu3cP2dnZWLZsGUxMTMq5xKyykkz1HhsbC3t7eyxYsKDUPuu3336Di4sLTE1NkZ6eDlVVVSxZsgS9e/cWt3n8+DHi4+OxbNkyrF+/HqampqVWHsYYY4yx0jJz5kxERERAS0sL169fR0ZGBnbs2IHBgwfj/fv3uH79On766ScARUOjQ0NDoaioyDlWGWMlcFDqXyj+EP04sBQVFQVvb2+oqqpi4sSJ6NWrFwBg27ZtGD16tLhdVUicWlhYiKioKLi4uKBJkyYIDw+HIAh4//691AxneXl5UFZWLseSsqogJSUF3t7eeP78OQ4cOCAOsZWl1NRUrFu3Dvr6+hg9ejSuXLmCwMBAhIeHw9/fX3weSJ4h3ELIGGOMsYpq//79cHd3x7lz59C0aVNkZGRgyZIl2LFjB3bv3o3BgweL21alhnnG2JfhoNQ/VDwg5e/vj7i4OLx9+xZOTk7o3r071NTUcPXqVXh7e0NBQQHdunXDxYsXERMTg6SkpErbIiAJst27dw/Pnz+HkpISGjduDF1dXVy+fBmDBw+GiYkJTp48CaBkMI+xsvDixQsARcP6ZO3mzZsYMWIEFBUV4e/vj7Zt2wIAbt26hTVr1uDMmTMICAhAr169xPulKgSnGWOMMVY5rV27FocPH0ZERIRYn8nPz8eYMWMQFhaG3bt3w8HBQer9h+s+jLHPqZyRklIgeah6eXlhzpw5qFGjhjj8bMGCBcjIyIClpSVWrFiBOnXq4PDhw8jPz8ezZ8/EqaUrG8mPS0hICLp27YpZs2Zh3Lhx+P777xEaGgpra2sEBQXh9u3bcHBwAAAOSLFyUbdu3VIJSAHAq1evoKuri3v37iEzM1NcbmJigmnTpqFnz54YPHgwzpw5I1bGuFLGGGOMsYpKTk4ON2/eRE5ODoCiRmdFRUU4OzsjMzMTw4cPx+HDh6X24boPY+xzOELwP3w85nnHjh04dOgQTp06BXNzcxw9ehSOjo7Izs5GXl4eFi9ejLZt22L9+vUgokrfVVUQBFy9ehVjxozB4sWLMX78eISFhcHJyUkcrmRjY4NDhw7Bzs4OTk5OCA4OLudSMyZb9vb2UFRUxPv37zF+/Hhs374d7du3BwAYGxtj/PjxUFZWhp6eXvkWlDHGGGPsX/hc/qehQ4dix44d+PHHH7FmzRpoaGgAALS0tDBx4kQoKipi+PDhkJeXx8CBA8u62IyxCoaH7/0PSUlJqF+/vjiN/NatW5GUlIR58+YhLCwMo0ePxvz58/H8+XNs3boVrq6umDt3LmrUqCEeo7Im85P0kvr1119x/vx5hIaGIiEhAZ07d0bPnj2xadMmAEBycjLq1auHq1evQkNDA4aGhuVccsa+nOS6v3//PjIzM5GRkQF7e3sAwJUrV7B8+XI8ffoU/v7+sLCwEPf7OJ8aY4wxxti3rPg7zLZt2xAbG4uCggKYm5tjzJgx2LVrFzZv3gwNDQ0sWbIE79+/x9y5c6GmpoaDBw/C09MTq1evRmhoKM82zBj7nypftERG4uLioKuri8OHD0NOTg5ycnIYMmQIxo0bh6SkJMyfPx8+Pj6YPHkypkyZAmVlZRw6dAiBgYFSx6mMASngv11wP3z4AF1dXaSkpMDGxgY9evTAhg0bAACnTp3CwYMHkZ2dDUtLSw5IsQpNEpAKDg5G165d4ezsjAEDBsDW1hZRUVGwsrLCjBkz0LBhQ0yaNAmRkZHivhyQYowxxlhFInmHmTlzJmbNmoVq1apBTk4OP//8M6ZPn47hw4fD3d0dWVlZaN26NZydnfHixQvs3r0bALBy5Up4e3ujadOm5XkajLEKoHJGTGSgXr16cHd3x/fff4/ffvsNAKCurg5tbW08evQIb9++FYeopaamokOHDpgzZw6mT59ensUucxoaGtixYwdatWqFgQMHwt/fX/wRCw4Oxq1bt8q5hIzJhiAIiIqKgpubGxYtWoRjx47h+vXrePfuHSZMmIBr167BxsYGU6ZMgZqaGnx8fJCbm1sp88kxxhhjrPI7f/48Dh8+jLCwMPj5+aFTp07Izc1Fs2bNIAgCRowYgXPnziEyMhJhYWGIjo6GkpIS8vPzAQBLlixBs2bNyvksGGPfusqX6EhG6tatiwULFkBZWRkDBgwo0fVURUUFR48ehZycHObOnYvatWtjzJgxEAShUk73LuklcufOHbx9+xaqqqowNTWFq6srIiMjsWPHDgwdOhRZWVn48OEDli1bhiNHjuDChQtQVVUt7+Iz9q98PEOM5O/Y2Fi0aNECLi4uUFRUhJycHC5fvgxLS0t4eXnh7NmzsLOzg6KiIvT09FCtWrVyPAvGGGOMsS+XmJgILS0ttG/fHiEhIXBzc8Pq1avh7u6Ot2/fIioqCt27dxdzaQJAQUEBFBUVy7HUjLGKhoNSxTx//hwqKirQ1NQEUBSY8vb2RmFhoVRgqnXr1ujQoQO2bNmCtWvXokGDBggJCRGneq9sASmgqJfIoUOHMGnSJBARtLW10a1bN6xcuRJ+fn5ITk5Gt27d0KBBA9SuXRtPnz7FyZMn0bx58/IuOmP/iiSHwsuXL5GQkABBENCmTRsAQEpKCjIyMqCsrAwAyMnJgYqKCrZv3w47OztER0fDwsICHTp0KM9TYIwxxhj7Vz6VB1dDQwONGjXCgQMHMHbsWKxcuRLjxo0DUJRL89ixY2jRogV0dXXFfSrjexBjrHRxovP/d/jwYYwZMwb169fH2LFjUbduXbi4uAAoSlI8Y8YMrFu3DkFBQXByckJWVpY4jM/a2hry8vKVcpY9SQ+RV69ewcHBAR4eHmjRogXOnj2L7du3w97eXkxqHhQUhNevX6N27dqwsLBAgwYNyrn0jP07kgpZfHw83N3doa6uDlVVVRw8eBAKCgq4efMmbGxssGDBAvz000/iftHR0fj+++9x4sQJGBkZleMZMMYYY4z9O8UDUqGhoWjTpg0aNmyI+Ph4dOjQAW/evMG6deswYcIEAEWNcgMHDkTdunWxfft2qd7ljDH2b1WuCMoXev/+Pc6dO4cPHz7g1atXCA0NxZMnT7B06VIYGRlh/PjxcHR0hLq6OpydnXHixAn06NEDpqam4jEKCgoqXUAKKOohdeXKFWzZsgXNmjXDoEGDoKamhqZNm6JWrVrw8/PD2LFjERgYiCFDhpR3cRn7YkQEOTk53LlzBx06dMD48eMxbtw46OrqQk5ODkQEQ0NDzJw5Exs3bgQRwdPTE2/fvsXx48ehoKCAmjVrlvdpMMYYY4z9Y5L6DwDMmjULu3btwowZMzB27Fi0aNECe/fuhYODA+7fv4/Q0FB899138PPzw4sXL3D06FFxpAgHphhjX4p7Sv2/Fy9ewNfXF48fP0bLli0xbdo0hIaG4uTJk7h58yZyc3NhYGCAy5cvo6CgADExMeKQnsosLy8PS5cuRWBgIGrXro3//Oc/4rqMjAwcOHAAa9asQevWrbF///5yLCljXy89PR39+/eHubk51q5dKy4v3oKYkJCAPXv2wNfXF3Xq1EGNGjWQnJyM33//Hebm5uVVdMYYY4yxL7Zo0SL8+uuvOHHiBJo3bw41NTUx2BQWFgYfHx+kpaVBX18f2traCAoKgqKiYqXMpcsYK1sclComKSkJS5cuxdWrV+Hq6ip2Ub137x5SUlKwY8cO3Lt3D2lpabh7926l7Bn1KU+fPsXOnTuxfPlyTJ06FYsXLxbXvX37Ftu2bcPu3btx7Ngx1KtXrxxLytjXiY+PR79+/bBt2zZ06NChRG4FSeUsPz8ff/31F06dOgUtLS1YWlpCX1+/nErNGGOMMfbl0tPT4ezsDFdXV/zwww9ITEzEo0ePsHXrVtja2mLYsGHIy8tDRkYGlJSUoKWlBUEQKmXqEsZY2eOg1EeSk5OxdOlSREdHo3///pg1a5a4TvJCKvn/yvgglpxbcnIy8vLyoKKigrp16yIzMxOrVq3CwYMH4ezsjPnz54v7ZGZmoqCggIcusQpv3759GDlyJN6/fw9BED6Z9DM7Oxu3b9+GhYVFOZWSMcYYY0x2Xr9+DWNjY4waNQrdu3fHxo0b8fjxYwiCgIcPH2L27NmYNm2a1D48ZI8xJityf79J1VKvXj34+PjAwsICR44cwfLly8V1BQUFACC+rFbWgFRYWBi6du0KBwcHmJubw9vbG2lpaZg0aRKcnZ1x8OBBLFq0SNxPXV2dA1KsUtDT04OCggJCQkIAoERACgC2bduG2bNn4/3792VdPMYYY4wxmatVqxYWLlyIjRs3om/fvmjUqBGWLFmCqKgo9OnTB3FxcSX24YAUY0xWOCj1Cdra2lKBqdmzZwOAVBDqUy+rFZ0gCAgPD8fw4cMxbtw4XLt2DR4eHlixYgViYmKgqamJcePG4fvvv8f69eulAnaMVQaNGjVC9erVsWvXLiQkJIjLi3coffLkCdq0aQNFRcXyKCJjjDHGmMy5ubkhLi4O165dw/Lly9G1a1cUFhYiKSmJZ9RmjJUqHr73P6SkpGDmzJmoVq0aAgICKnWLgGSY0oQJE1BYWIhNmzbh+fPnsLOzg729Pfz9/cVtk5OTsWvXLjg5OaFJkyblWGrGZC8kJATff/89hgwZAi8vL7Ro0QJA0bC9xYsXY9++fTh9+jSMjIzKuaSMMcYYY7KXlZWFuLg4LF++HAkJCbhx40alGyHCGPt2cFDqb6Snp6NmzZrilPCVITD1qTw5EkOGDEH//v0xcOBAGBgYwMHBAf7+/hAEAUFBQahduza6dOnCM22wSquwsBCBgYGYOHEiDAwMYGVlhWrVqiExMRFRUVE4efIkzMzMyruYjDHGGGMyR0SIiIjAqlWrkJ+fj6NHj/Ise4yxUsVBqX/ofwVyKhLJeTx//hwRERHIzs5Gr169oKurCwCYOnUqTp06hXfv3sHR0RGrVq2CoqIi8vPzMWLECBgaGmLu3LncWsIqvejoaPj5+eHPP/+Euro6rK2t4ebmBkNDw/IuGmOMMcZYqcnLy0N8fDxMTU0hJydXKSd3Yox9OzgoVYVIAlJ37tzBDz/8ABMTE9SvX18qN9Rff/2FESNGICEhAffv34eqqioKCgowd+5c7N69G+Hh4fxSzqoMbhVkjDHGWFVWWRrmGWPfLg5KVRGSoYd37txBx44dMWHCBMyYMQPVq1cHABw9ehTy8vLo3bs3goKCsGjRImRlZaFdu3bIzs5GdHQ0Tp06xcOWWJVSfMhuZRm+yxhjjDHGGGPfCg5KVSHp6ekYMGAAWrVqhXXr1onLly9fDm9vb3Tq1AmzZs1Ct27d8OeffyIgIABv3ryBvr4+hgwZAgMDg3IsPWOMMcYYY4wxxioTDkpVIXfv3kXfvn0RGBiITp06QU5ODv7+/pg8eTLWrFmDsLAwyMvLY+LEiejTp095F5cxxhhjjDHGGGOVGAelqpA9e/bA1dUV+fn54jCk58+f4/Hjx+jYsSNu376NqVOn4u3bt9iyZQtatWoFgIctMcYYY4wxxhhjTPY4a10VoqenBwUFBYSGhgIoCjbp6uqiY8eOKCwshLGxMZydnQEAWlpa4n4ckGKMMcYYY4wxxpiscVCqCtHT00ONGjWwc+dOJCQkSAWbJLNq3L9/H3p6evjuu+/Kq5iMMcYYY4wxxhirAjgoVYXo6upi48aNOHnyJObMmYP4+Hhx3du3bzFz5kxs27YN8+bNg7q6ejmWlDHGGGOMMcYYY5Ud55SqYgoKCrBlyxZMnDgRBgYGsLa2hqKiIhITE3Ht2jWcOHECZmZm5V1MxhhjjDHGGGOMVXIclKqirl69ihUrVuDRo0dQV1dHhw4d4ObmBgMDg/IuGmOMMcYYY4wxxqoADkpVYQUFBZCXly/vYjDGGGOMMcYYY6wK4pxSVZgkuTlQNBMfY4wxxhhjjDHGWFnhnlKMMcYYY4wxxhhjrMxxTynGGGOMMcYYY4wxVuY4KMUYY4wxxhhjjDHGyhwHpRhjjDHGGGOMMcZYmeOgFGOMMcYYY4wxxhgrcxyUYowxxhhjjDHGGGNljoNSjDHGGGOMMcYYY6zMcVCKMcYYY4wxxhhjjJU5DkoxxhhjjH0lPT09rFmz5qu3+Ra5urrC0dHxq45x4cIFCIKAN2/eyKRMjDHGGKscOCjFGGOMMfYZz549w+jRo1G/fn0oKSmhUaNGmDJlCtLS0v71sWJiYuDu7i6zsv3TIFdFDYYxxhhjrPLjoBRjjDHG2Cf89ddfaNu2LR4+fIj9+/fjzz//hL+/P8LDw2FlZYX09PR/dbw6depAVVW1lErLGGOMMVbxcFCKMcYYY+wTJkyYACUlJZw+fRqdOnVCw4YN0atXL5w9exaJiYnw8fGR2j4zMxMuLi747rvvoKOjgw0bNkit/7jH0ps3bzBmzBjUqVMH1atXR5cuXXDz5k2pfY4ePYp27dqhWrVqqF27NgYMGAAA6Ny5MxISEjBt2jQIggBBEL7oHAsKCuDm5gZ9fX2oqKigadOmWLt27Se3XbBggVjWH3/8Ee/fvxfXFRYWwtfXVzyOqakpgoODv6hMjDHGGKs6OCjFGGOMMfaR9PR0nDp1CuPHj4eKiorUOm1tbfzwww84ePAgiEhc7ufnB1NTU8TGxsLLywtTpkzBmTNnPvsZgwcPRmpqKn7//Xdcv34d5ubmsLe3F3tgHT9+HAMGDEDv3r0RGxuL8PBwWFhYAABCQkKgq6uLhQsXIjk5GcnJyV90noWFhdDV1cWhQ4cQHx+PuXPnYtasWQgKCpLaLjw8HHfv3sWFCxewf/9+hISEYMGCBeJ6X19f7Nq1C/7+/rhz5w6mTZuGYcOGISIi4ovKxRhjjLGqQaG8C8AYY4wx9q15+PAhiAjNmzf/5PrmzZvj9evXePnyJbS0tAAANjY28PLyAgAYGRkhMjISv/zyC7p161Zi/0uXLiE6OhqpqalQVlYGAKxcuRJhYWEIDg6Gu7s7lixZgqFDh0oFf0xNTQEAGhoakJeXh7q6OrS1tb/4PBUVFaWOr6+vjytXriAoKAhDhgwRlyspKWHbtm1QVVVFy5YtsXDhQsyYMQOLFi1Cfn4+li5dirNnz8LKygoA0LhxY1y6dAkBAQHo1KnTF5ePMcYYY5UbB6UYY4wxxj6jeE+ovyMJyBT/+3MJxm/evImsrCxoampKLc/JycGjR48AAHFxcRg7duy/K/AX2LBhA7Zt24anT58iJycH79+/R+vWraW2MTU1lcqHZWVlhaysLDx79gxZWVnIzs4uEXx7//49zMzMSr38jDHGGKu4OCjFGGOMMfYRAwMDCIKAu3fvinmcirt79y5q1aqFOnXqfNHxs7KyUK9ePVy4cKHEupo1awJAiWGDpeHAgQPw9PTEqlWrYGVlBXV1dfj5+eHq1av/+BhZWVkAioYb6ujoSK2T9AJjjDHGGPsUDkoxxhhjjH1EU1MT3bp1w8aNGzFt2jSpAFFKSgr27t2LESNGSCUYj4qKkjpGVFTUZ4f/mZubIyUlBQoKCtDT0/vkNq1atUJ4eDhGjRr1yfVKSkooKCj4l2cmLTIyEtbW1hg/fry4TNJTq7ibN28iJydH/B6ioqKgpqaGBg0aQENDA8rKynj69CkP1WOMMcbYv8KJzhljjDHGPmH9+vXIy8tDjx49cPHiRTx79gwnT55Et27doKOjgyVLlkhtHxkZiRUrVuDBgwfYsGEDDh06hClTpnzy2F27doWVlRUcHR1x+vRpPHnyBJcvX4aPjw+uXbsGAJg3bx7279+PefPm4e7du7h16xaWL18uHkNPTw8XL15EYmIiXr169T/PJTExEXFxcVL/e/36NQwNDXHt2jWcOnUKDx48wJw5cxATE1Ni//fv38PNzQ3x8fE4ceIE5s2bh4kTJ0JOTg7q6urw9PTEtGnTsHPnTjx69Ag3btzAunXrsHPnzn/7tTPGGGOsCuGgFGOMMcbYJ0gCNo0bN8aQIUPQpEkTuLu7w87ODleuXIGGhobU9j/99BOuXbsGMzMzLF68GKtXr0aPHj0+eWxBEHDixAnY2tpi1KhRMDIywtChQ5GQkIC6desCADp37oxDhw7hyJEjaN26Nbp06YLo6GjxGAsXLsSTJ0/QpEmTvx1GuHLlSpiZmUn97/jx4xg3bhwGDhwIZ2dnWFpaIi0tTarXlIS9vT0MDQ1ha2sLZ2dn9OvXD/PnzxfXL1q0CHPmzIGvry+aN2+Onj174vjx49DX1/+nXzdjjDHGqiCB/k0GT8YYY4wx9kXq1auHRYsWYcyYMeVdFMYYY4yxbwLnlGKMMcYYK0XZ2dmIjIzEixcv0LJly/IuDmOMMcbYN4OH7zHGGGOMlaLNmzdj6NChmDp1KqysrMq7OIwxxhhj3wwevscYY4wxxhhjjDHGyhz3lGKMMcYYY4wxxhhjZY6DUowxxhhjjDHGGGOszHFQijHGGGOMMcYYY4yVOQ5KMcYYY4wxxhhjjLEyx0EpxhhjjDHGGGOMMVbmOCjFGGOMMcYYY4wxxsocB6UYY4wxxhhjjDHGWJnjoBRjjDHGGGOMMcYYK3MclGKMMcYYY4wxxhhjZe7/AJsuPZS07KS1AAAAAElFTkSuQmCC",
+ "image/png": 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",
"text/plain": [
""
]
diff --git a/annotate/methods_reformat_annotate.out b/annotate/methods_reformat_annotate.out
index 7f9319c..9d6793a 100644
--- a/annotate/methods_reformat_annotate.out
+++ b/annotate/methods_reformat_annotate.out
@@ -69,15 +69,6 @@ matches_whole_text: false
subject_start: 2
subject_end: 12
----
-predicate_id: rdfs:label
-object_id: https://w3id.org/aio/Function
-object_label: Function
-match_string: function
-matches_whole_text: false
-subject_start: 158
-subject_end: 165
-
---
predicate_id: rdfs:label
object_id: http://purl.org/dc/terms/title
@@ -204,15 +195,6 @@ matches_whole_text: false
subject_start: 173
subject_end: 177
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: model
-matches_whole_text: false
-subject_start: 211
-subject_end: 215
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -276,15 +258,6 @@ matches_whole_text: false
subject_start: 2
subject_end: 12
----
-predicate_id: rdfs:label
-object_id: https://w3id.org/aio/Function
-object_label: Function
-match_string: function
-matches_whole_text: false
-subject_start: 44
-subject_end: 51
-
---
predicate_id: oio:hasExactSynonym
object_id: https://w3id.org/aio/GELUFunction
@@ -321,15 +294,6 @@ matches_whole_text: false
subject_start: 15
subject_end: 17
----
-predicate_id: rdfs:label
-object_id: https://w3id.org/aio/Function
-object_label: Function
-match_string: Function
-matches_whole_text: false
-subject_start: 27
-subject_end: 34
-
---
predicate_id: rdfs:label
object_id: http://purl.org/dc/terms/description
@@ -420,15 +384,6 @@ matches_whole_text: false
subject_start: 67
subject_end: 73
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 27
-subject_end: 31
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -627,15 +582,6 @@ matches_whole_text: false
subject_start: 184
subject_end: 200
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: model
-matches_whole_text: false
-subject_start: 184
-subject_end: 188
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -681,15 +627,6 @@ matches_whole_text: false
subject_start: 2
subject_end: 12
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: model
-matches_whole_text: false
-subject_start: 254
-subject_end: 258
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -744,15 +681,6 @@ matches_whole_text: false
subject_start: 22
subject_end: 36
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 22
-subject_end: 26
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -807,15 +735,6 @@ matches_whole_text: false
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predicate_id: rdfs:label
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@@ -145725,15 +124890,6 @@ matches_whole_text: false
subject_start: 27
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----
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---
predicate_id: rdfs:label
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@@ -145761,15 +124917,6 @@ matches_whole_text: false
subject_start: 19
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----
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---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -145788,15 +124935,6 @@ matches_whole_text: false
subject_start: 2
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----
-predicate_id: rdfs:label
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-
---
predicate_id: oio:hasExactSynonym
object_id: https://w3id.org/aio/VectorAutoregression
@@ -145824,15 +124962,6 @@ matches_whole_text: false
subject_start: 2
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----
-predicate_id: rdfs:label
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---
predicate_id: rdfs:label
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@@ -145851,15 +124980,6 @@ matches_whole_text: false
subject_start: 43
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----
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---
predicate_id: rdfs:label
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@@ -145932,15 +125052,6 @@ matches_whole_text: false
subject_start: 149
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----
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---
predicate_id: rdfs:label
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@@ -145977,15 +125088,6 @@ matches_whole_text: false
subject_start: 37
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----
-predicate_id: oio:hasExactSynonym
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-
---
predicate_id: rdfs:label
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@@ -146013,15 +125115,6 @@ matches_whole_text: false
subject_start: 33
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----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
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-subject_start: 557
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-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -146085,15 +125178,6 @@ matches_whole_text: false
subject_start: 2
subject_end: 6
----
-predicate_id: oio:hasExactSynonym
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-object_label: Model
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-subject_start: 33
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-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -146121,15 +125205,6 @@ matches_whole_text: false
subject_start: 10
subject_end: 13
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/word2vec
-object_label: word2vec
-match_string: Word2Vec
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-subject_start: 15
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-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/word2vec
@@ -146139,15 +125214,6 @@ matches_whole_text: false
subject_start: 15
subject_end: 22
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/word2vec
-object_label: word2vec
-match_string: Word2Vec
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-subject_start: 39
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-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/word2vec
@@ -146175,24 +125241,6 @@ matches_whole_text: false
subject_start: 60
subject_end: 63
----
-predicate_id: rdfs:label
-object_id: https://w3id.org/aio/Function
-object_label: Function
-match_string: function
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-subject_start: 197
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-
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
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-subject_start: 388
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-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -146220,15 +125268,6 @@ matches_whole_text: false
subject_start: 210
subject_end: 213
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/word2vec
-object_label: word2vec
-match_string: Word2Vec
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-subject_start: 43
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-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/word2vec
@@ -146265,15 +125304,6 @@ matches_whole_text: false
subject_start: 2
subject_end: 12
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
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-subject_start: 38
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-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -146292,15 +125322,6 @@ matches_whole_text: false
subject_start: 2
subject_end: 6
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
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-subject_start: 44
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-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -146373,15 +125394,6 @@ matches_whole_text: false
subject_start: 2
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----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
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-subject_start: 680
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-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -146499,15 +125511,6 @@ matches_whole_text: false
subject_start: 51
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----
-predicate_id: oio:hasExactSynonym
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-object_label: Model
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-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -146553,15 +125556,6 @@ matches_whole_text: false
subject_start: 27
subject_end: 30
----
-predicate_id: oio:hasExactSynonym
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-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -146616,15 +125610,6 @@ matches_whole_text: false
subject_start: 271
subject_end: 284
----
-predicate_id: oio:hasExactSynonym
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---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -146652,15 +125637,6 @@ matches_whole_text: false
subject_start: 2
subject_end: 6
----
-predicate_id: oio:hasExactSynonym
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-object_label: Model
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---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -146670,15 +125646,6 @@ matches_whole_text: false
subject_start: 33
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----
-predicate_id: oio:hasExactSynonym
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-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -146796,15 +125763,6 @@ matches_whole_text: false
subject_start: 2
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----
-predicate_id: oio:hasExactSynonym
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-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -146868,15 +125826,6 @@ matches_whole_text: false
subject_start: 161
subject_end: 165
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
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-subject_start: 633
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-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -146922,15 +125871,6 @@ matches_whole_text: false
subject_start: 27
subject_end: 33
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
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-subject_start: 22
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-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -147012,15 +125952,6 @@ matches_whole_text: false
subject_start: 88
subject_end: 94
----
-predicate_id: rdfs:label
-object_id: https://w3id.org/aio/Function
-object_label: Function
-match_string: Function
-matches_whole_text: false
-subject_start: 21
-subject_end: 28
-
---
predicate_id: rdfs:label
object_id: http://purl.org/dc/terms/description
@@ -147075,15 +126006,6 @@ matches_whole_text: false
subject_start: 2
subject_end: 6
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
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-subject_start: 29
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-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -147147,15 +126069,6 @@ matches_whole_text: false
subject_start: 2
subject_end: 12
----
-predicate_id: rdfs:label
-object_id: https://w3id.org/aio/Function
-object_label: Function
-match_string: function
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-subject_start: 238
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-
---
predicate_id: rdfs:label
object_id: http://purl.org/dc/terms/title
@@ -147219,15 +126132,6 @@ matches_whole_text: false
subject_start: 110
subject_end: 111
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/LanguageModel
-object_label: Language Model
-match_string: language model
-matches_whole_text: false
-subject_start: 148
-subject_end: 161
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/LanguageModel
@@ -147237,15 +126141,6 @@ matches_whole_text: false
subject_start: 148
subject_end: 161
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: model
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-subject_start: 157
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-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -147300,15 +126195,6 @@ matches_whole_text: false
subject_start: 2
subject_end: 6
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
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-subject_start: 51
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-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -147327,15 +126213,6 @@ matches_whole_text: false
subject_start: 2
subject_end: 12
----
-predicate_id: rdfs:label
-object_id: https://w3id.org/aio/Function
-object_label: Function
-match_string: function
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-subject_start: 307
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-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Network
@@ -147354,15 +126231,6 @@ matches_whole_text: false
subject_start: 2
subject_end: 6
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
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-subject_start: 33
-subject_end: 37
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -147381,24 +126249,6 @@ matches_whole_text: false
subject_start: 2
subject_end: 12
----
-predicate_id: rdfs:label
-object_id: https://w3id.org/aio/Function
-object_label: Function
-match_string: function
-matches_whole_text: false
-subject_start: 154
-subject_end: 161
-
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: model
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-subject_start: 64
-subject_end: 68
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -147453,15 +126303,6 @@ matches_whole_text: false
subject_start: 101
subject_end: 119
----
-predicate_id: rdfs:label
-object_id: https://w3id.org/aio/Function
-object_label: Function
-match_string: function
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-subject_start: 445
-subject_end: 452
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Layer
@@ -147570,15 +126411,6 @@ matches_whole_text: false
subject_start: 21
subject_end: 34
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
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-subject_start: 22
-subject_end: 26
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -147606,15 +126438,6 @@ matches_whole_text: false
subject_start: 51
subject_end: 69
----
-predicate_id: rdfs:label
-object_id: https://w3id.org/aio/Function
-object_label: Function
-match_string: function
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-subject_start: 165
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-
---
predicate_id: rdfs:label
object_id: http://purl.org/dc/terms/title
@@ -147633,15 +126456,6 @@ matches_whole_text: false
subject_start: 36
subject_end: 54
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
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-subject_start: 33
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-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -147795,15 +126609,6 @@ matches_whole_text: false
subject_start: 2
subject_end: 6
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
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-subject_start: 49
-subject_end: 53
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -147822,15 +126627,6 @@ matches_whole_text: false
subject_start: 62
subject_end: 75
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
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-subject_start: 27
-subject_end: 31
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -147921,15 +126717,6 @@ matches_whole_text: false
subject_start: 18
subject_end: 20
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 27
-subject_end: 31
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -147939,15 +126726,6 @@ matches_whole_text: false
subject_start: 27
subject_end: 31
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
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-subject_start: 42
-subject_end: 46
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -147984,15 +126762,6 @@ matches_whole_text: false
subject_start: 479
subject_end: 498
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/LanguageModel
-object_label: Language Model
-match_string: language model
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-subject_start: 508
-subject_end: 521
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/LanguageModel
@@ -148002,15 +126771,6 @@ matches_whole_text: false
subject_start: 508
subject_end: 521
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/MaskedLanguageModel
-object_label: Masked Language Model
-match_string: masked language model
-matches_whole_text: false
-subject_start: 501
-subject_end: 521
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/MaskedLanguageModel
@@ -148020,15 +126780,6 @@ matches_whole_text: false
subject_start: 501
subject_end: 521
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
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-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -148074,15 +126825,6 @@ matches_whole_text: false
subject_start: 2
subject_end: 6
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
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-subject_start: 48
-subject_end: 52
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -148146,15 +126888,6 @@ matches_whole_text: false
subject_start: 339
subject_end: 343
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: model
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-subject_start: 95
-subject_end: 99
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -148200,15 +126933,6 @@ matches_whole_text: false
subject_start: 28
subject_end: 34
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
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-subject_start: 33
-subject_end: 37
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -148272,15 +126996,6 @@ matches_whole_text: false
subject_start: 180
subject_end: 196
----
-predicate_id: rdfs:label
-object_id: https://w3id.org/aio/Function
-object_label: Function
-match_string: function
-matches_whole_text: false
-subject_start: 428
-subject_end: 435
-
---
predicate_id: oio:hasExactSynonym
object_id: https://w3id.org/aio/ResidualNeuralNetwork
@@ -148389,15 +127104,6 @@ matches_whole_text: false
subject_start: 42
subject_end: 69
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: model
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-subject_start: 132
-subject_end: 136
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -148497,15 +127203,6 @@ matches_whole_text: false
subject_start: 41
subject_end: 47
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
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-subject_start: 16
-subject_end: 20
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -148677,15 +127374,6 @@ matches_whole_text: false
subject_start: 2
subject_end: 6
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
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-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
diff --git a/annotate/methods_reformat_annotate_AIOonly.txt b/annotate/methods_reformat_annotate_AIOonly.txt
index 731e905..5b06ef1 100644
--- a/annotate/methods_reformat_annotate_AIOonly.txt
+++ b/annotate/methods_reformat_annotate_AIOonly.txt
@@ -5,8 +5,7 @@ match_string: Normalization
matches_whole_text: false
subject_start: 20
subject_end: 32
-
----
+--
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Normalization
object_label: Normalization
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----
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----
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----
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----
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----
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----
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----
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----
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----
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----
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----
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----
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----
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----
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----
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----
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----
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predicate_id: rdfs:label
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----
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predicate_id: rdfs:label
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----
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----
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predicate_id: oio:hasExactSynonym
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----
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----
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----
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----
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----
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----
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----
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----
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----
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----
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----
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----
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diff --git a/annotate/methods_reformat_collection_annotate.out b/annotate/methods_reformat_collection_annotate.out
index 632e865..c71606c 100644
--- a/annotate/methods_reformat_collection_annotate.out
+++ b/annotate/methods_reformat_collection_annotate.out
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@@ -1176,24 +726,6 @@ matches_whole_text: false
subject_start: 39
subject_end: 43
----
-predicate_id: rdfs:label
-object_id: https://w3id.org/aio/Function
-object_label: Function
-match_string: Function
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-subject_start: 21
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-
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
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-subject_start: 36
-subject_end: 40
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1212,15 +744,6 @@ matches_whole_text: false
subject_start: 16
subject_end: 27
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
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-subject_start: 30
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-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1239,15 +762,6 @@ matches_whole_text: false
subject_start: 16
subject_end: 28
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
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-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1266,15 +780,6 @@ matches_whole_text: false
subject_start: 16
subject_end: 32
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
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-subject_start: 16
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-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1284,15 +789,6 @@ matches_whole_text: false
subject_start: 16
subject_end: 20
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
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-subject_start: 43
-subject_end: 47
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1302,15 +798,6 @@ matches_whole_text: false
subject_start: 43
subject_end: 47
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 34
-subject_end: 38
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1320,15 +807,6 @@ matches_whole_text: false
subject_start: 34
subject_end: 38
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
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-subject_start: 38
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-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1374,15 +852,6 @@ matches_whole_text: false
subject_start: 16
subject_end: 28
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
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-subject_start: 20
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-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1392,15 +861,6 @@ matches_whole_text: false
subject_start: 20
subject_end: 24
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
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-subject_start: 33
-subject_end: 37
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1419,15 +879,6 @@ matches_whole_text: false
subject_start: 24
subject_end: 45
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 43
-subject_end: 47
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1439,15 +890,6 @@ subject_end: 47
---
predicate_id: rdfs:label
-object_id: https://w3id.org/aio/Function
-object_label: Function
-match_string: Function
-matches_whole_text: false
-subject_start: 23
-subject_end: 30
-
----
-predicate_id: oio:hasExactSynonym
object_id: https://w3id.org/aio/Model
object_label: Model
match_string: Model
@@ -1455,24 +897,6 @@ matches_whole_text: false
subject_start: 38
subject_end: 42
----
-predicate_id: rdfs:label
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 38
-subject_end: 42
-
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 35
-subject_end: 39
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1491,15 +915,6 @@ matches_whole_text: false
subject_start: 27
subject_end: 33
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
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-subject_start: 30
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-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1509,15 +924,6 @@ matches_whole_text: false
subject_start: 30
subject_end: 34
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
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-subject_start: 28
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-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1527,15 +933,6 @@ matches_whole_text: false
subject_start: 28
subject_end: 32
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 33
-subject_end: 37
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1545,15 +942,6 @@ matches_whole_text: false
subject_start: 33
subject_end: 37
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
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-subject_start: 32
-subject_end: 36
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1563,15 +951,6 @@ matches_whole_text: false
subject_start: 32
subject_end: 36
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
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-subject_start: 34
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-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1590,15 +969,6 @@ matches_whole_text: false
subject_start: 27
subject_end: 33
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
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-subject_start: 35
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-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1608,15 +978,6 @@ matches_whole_text: false
subject_start: 35
subject_end: 39
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
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-subject_start: 41
-subject_end: 45
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1626,15 +987,6 @@ matches_whole_text: false
subject_start: 41
subject_end: 45
----
-predicate_id: rdfs:label
-object_id: https://w3id.org/aio/Function
-object_label: Function
-match_string: Function
-matches_whole_text: false
-subject_start: 33
-subject_end: 40
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Network
@@ -1662,15 +1014,6 @@ matches_whole_text: false
subject_start: 16
subject_end: 37
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 36
-subject_end: 40
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1698,15 +1041,6 @@ matches_whole_text: false
subject_start: 16
subject_end: 38
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 44
-subject_end: 48
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1716,15 +1050,6 @@ matches_whole_text: false
subject_start: 44
subject_end: 48
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 37
-subject_end: 41
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1734,15 +1059,6 @@ matches_whole_text: false
subject_start: 37
subject_end: 41
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 27
-subject_end: 31
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1788,15 +1104,6 @@ matches_whole_text: false
subject_start: 16
subject_end: 28
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 38
-subject_end: 42
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1815,15 +1122,6 @@ matches_whole_text: false
subject_start: 21
subject_end: 39
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 33
-subject_end: 37
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1833,15 +1131,6 @@ matches_whole_text: false
subject_start: 33
subject_end: 37
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 37
-subject_end: 41
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1851,15 +1140,6 @@ matches_whole_text: false
subject_start: 37
subject_end: 41
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 34
-subject_end: 38
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1869,15 +1149,6 @@ matches_whole_text: false
subject_start: 34
subject_end: 38
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 47
-subject_end: 51
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1887,15 +1158,6 @@ matches_whole_text: false
subject_start: 47
subject_end: 51
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 40
-subject_end: 44
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1914,15 +1176,6 @@ matches_whole_text: false
subject_start: 16
subject_end: 35
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 30
-subject_end: 34
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1932,15 +1185,6 @@ matches_whole_text: false
subject_start: 30
subject_end: 34
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 41
-subject_end: 45
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1959,15 +1203,6 @@ matches_whole_text: false
subject_start: 21
subject_end: 34
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 36
-subject_end: 40
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1986,15 +1221,6 @@ matches_whole_text: false
subject_start: 21
subject_end: 37
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 31
-subject_end: 35
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -2004,15 +1230,6 @@ matches_whole_text: false
subject_start: 31
subject_end: 35
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 34
-subject_end: 38
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -2022,15 +1239,6 @@ matches_whole_text: false
subject_start: 34
subject_end: 38
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 41
-subject_end: 45
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -2049,15 +1257,6 @@ matches_whole_text: false
subject_start: 16
subject_end: 35
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 38
-subject_end: 42
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -2076,15 +1275,6 @@ matches_whole_text: false
subject_start: 21
subject_end: 27
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 20
-subject_end: 24
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -2094,15 +1284,6 @@ matches_whole_text: false
subject_start: 20
subject_end: 24
----
-predicate_id: rdfs:label
-object_id: https://w3id.org/aio/Function
-object_label: Function
-match_string: Function
-matches_whole_text: false
-subject_start: 22
-subject_end: 29
-
---
predicate_id: oio:hasExactSynonym
object_id: https://w3id.org/aio/VectorAutoregression
@@ -2121,15 +1302,6 @@ matches_whole_text: false
subject_start: 22
subject_end: 38
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 27
-subject_end: 31
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -2139,15 +1311,6 @@ matches_whole_text: false
subject_start: 27
subject_end: 31
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 36
-subject_end: 40
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -2157,15 +1320,6 @@ matches_whole_text: false
subject_start: 36
subject_end: 40
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 22
-subject_end: 26
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -2175,15 +1329,6 @@ matches_whole_text: false
subject_start: 22
subject_end: 26
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 42
-subject_end: 46
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -2193,15 +1338,6 @@ matches_whole_text: false
subject_start: 42
subject_end: 46
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 44
-subject_end: 48
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -2211,15 +1347,6 @@ matches_whole_text: false
subject_start: 44
subject_end: 48
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 30
-subject_end: 34
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -2229,15 +1356,6 @@ matches_whole_text: false
subject_start: 30
subject_end: 34
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 34
-subject_end: 38
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -2247,15 +1365,6 @@ matches_whole_text: false
subject_start: 34
subject_end: 38
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 39
-subject_end: 43
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -2265,15 +1374,6 @@ matches_whole_text: false
subject_start: 39
subject_end: 43
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 37
-subject_end: 41
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -2283,15 +1383,6 @@ matches_whole_text: false
subject_start: 37
subject_end: 41
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 48
-subject_end: 52
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -2301,15 +1392,6 @@ matches_whole_text: false
subject_start: 48
subject_end: 52
----
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 31
-subject_end: 35
-
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
diff --git a/annotate/methods_reformat_collection_annotate_AIOonly.txt b/annotate/methods_reformat_collection_annotate_AIOonly.txt
index 485889b..ca01a4a 100644
--- a/annotate/methods_reformat_collection_annotate_AIOonly.txt
+++ b/annotate/methods_reformat_collection_annotate_AIOonly.txt
@@ -1,11 +1,3 @@
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 29
-subject_end: 33
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -14,14 +6,6 @@ matches_whole_text: false
subject_start: 29
subject_end: 33
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 36
-subject_end: 40
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -30,14 +14,6 @@ matches_whole_text: false
subject_start: 36
subject_end: 40
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 35
-subject_end: 39
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object_id: https://w3id.org/aio/Model
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@@ -1174,14 +718,6 @@ matches_whole_text: false
subject_start: 34
subject_end: 38
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 38
-subject_end: 42
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1222,14 +758,6 @@ matches_whole_text: false
subject_start: 16
subject_end: 28
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 20
-subject_end: 24
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1238,14 +766,6 @@ matches_whole_text: false
subject_start: 20
subject_end: 24
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 33
-subject_end: 37
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1262,14 +782,6 @@ matches_whole_text: false
subject_start: 24
subject_end: 45
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 43
-subject_end: 47
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1279,14 +791,6 @@ subject_start: 43
subject_end: 47
--
predicate_id: rdfs:label
-object_id: https://w3id.org/aio/Function
-object_label: Function
-match_string: Function
-matches_whole_text: false
-subject_start: 23
-subject_end: 30
---
-predicate_id: oio:hasExactSynonym
object_id: https://w3id.org/aio/Model
object_label: Model
match_string: Model
@@ -1299,22 +803,6 @@ object_id: https://w3id.org/aio/Model
object_label: Model
match_string: Model
matches_whole_text: false
-subject_start: 38
-subject_end: 42
---
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 35
-subject_end: 39
---
-predicate_id: rdfs:label
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
subject_start: 35
subject_end: 39
--
@@ -1326,14 +814,6 @@ matches_whole_text: false
subject_start: 27
subject_end: 33
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 30
-subject_end: 34
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1342,14 +822,6 @@ matches_whole_text: false
subject_start: 30
subject_end: 34
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 28
-subject_end: 32
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1358,14 +830,6 @@ matches_whole_text: false
subject_start: 28
subject_end: 32
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 33
-subject_end: 37
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1374,14 +838,6 @@ matches_whole_text: false
subject_start: 33
subject_end: 37
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 32
-subject_end: 36
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1390,14 +846,6 @@ matches_whole_text: false
subject_start: 32
subject_end: 36
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 34
-subject_end: 38
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1408,19 +856,11 @@ subject_end: 38
--
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Network
-object_label: Network
-match_string: Network
-matches_whole_text: false
-subject_start: 27
-subject_end: 33
---
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
+object_label: Network
+match_string: Network
matches_whole_text: false
-subject_start: 35
-subject_end: 39
+subject_start: 27
+subject_end: 33
--
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
@@ -1430,14 +870,6 @@ matches_whole_text: false
subject_start: 35
subject_end: 39
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 41
-subject_end: 45
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1447,14 +879,6 @@ subject_start: 41
subject_end: 45
--
predicate_id: rdfs:label
-object_id: https://w3id.org/aio/Function
-object_label: Function
-match_string: Function
-matches_whole_text: false
-subject_start: 33
-subject_end: 40
---
-predicate_id: rdfs:label
object_id: https://w3id.org/aio/Network
object_label: Network
match_string: Network
@@ -1478,14 +902,6 @@ matches_whole_text: false
subject_start: 16
subject_end: 37
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 36
-subject_end: 40
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1510,14 +926,6 @@ matches_whole_text: false
subject_start: 16
subject_end: 38
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 44
-subject_end: 48
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1526,14 +934,6 @@ matches_whole_text: false
subject_start: 44
subject_end: 48
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 37
-subject_end: 41
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1542,14 +942,6 @@ matches_whole_text: false
subject_start: 37
subject_end: 41
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 27
-subject_end: 31
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1590,14 +982,6 @@ matches_whole_text: false
subject_start: 16
subject_end: 28
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 38
-subject_end: 42
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1614,14 +998,6 @@ matches_whole_text: false
subject_start: 21
subject_end: 39
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 33
-subject_end: 37
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1630,14 +1006,6 @@ matches_whole_text: false
subject_start: 33
subject_end: 37
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 37
-subject_end: 41
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1646,14 +1014,6 @@ matches_whole_text: false
subject_start: 37
subject_end: 41
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 34
-subject_end: 38
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1662,14 +1022,6 @@ matches_whole_text: false
subject_start: 34
subject_end: 38
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 47
-subject_end: 51
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1678,14 +1030,6 @@ matches_whole_text: false
subject_start: 47
subject_end: 51
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 40
-subject_end: 44
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1702,14 +1046,6 @@ matches_whole_text: false
subject_start: 16
subject_end: 35
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 30
-subject_end: 34
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1718,14 +1054,6 @@ matches_whole_text: false
subject_start: 30
subject_end: 34
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 41
-subject_end: 45
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1742,14 +1070,6 @@ matches_whole_text: false
subject_start: 21
subject_end: 34
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 36
-subject_end: 40
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1766,14 +1086,6 @@ matches_whole_text: false
subject_start: 21
subject_end: 37
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 31
-subject_end: 35
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1782,14 +1094,6 @@ matches_whole_text: false
subject_start: 31
subject_end: 35
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 34
-subject_end: 38
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1798,14 +1102,6 @@ matches_whole_text: false
subject_start: 34
subject_end: 38
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 41
-subject_end: 45
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1822,14 +1118,6 @@ matches_whole_text: false
subject_start: 16
subject_end: 35
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 38
-subject_end: 42
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1846,14 +1134,6 @@ matches_whole_text: false
subject_start: 21
subject_end: 27
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 20
-subject_end: 24
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1862,14 +1142,6 @@ matches_whole_text: false
subject_start: 20
subject_end: 24
--
-predicate_id: rdfs:label
-object_id: https://w3id.org/aio/Function
-object_label: Function
-match_string: Function
-matches_whole_text: false
-subject_start: 22
-subject_end: 29
---
predicate_id: oio:hasExactSynonym
object_id: https://w3id.org/aio/VectorAutoregression
object_label: VAR
@@ -1886,14 +1158,6 @@ matches_whole_text: false
subject_start: 22
subject_end: 38
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 27
-subject_end: 31
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1902,14 +1166,6 @@ matches_whole_text: false
subject_start: 27
subject_end: 31
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 36
-subject_end: 40
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1918,14 +1174,6 @@ matches_whole_text: false
subject_start: 36
subject_end: 40
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 22
-subject_end: 26
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1934,14 +1182,6 @@ matches_whole_text: false
subject_start: 22
subject_end: 26
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 42
-subject_end: 46
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1950,14 +1190,6 @@ matches_whole_text: false
subject_start: 42
subject_end: 46
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 44
-subject_end: 48
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1966,14 +1198,6 @@ matches_whole_text: false
subject_start: 44
subject_end: 48
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 30
-subject_end: 34
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1982,14 +1206,6 @@ matches_whole_text: false
subject_start: 30
subject_end: 34
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 34
-subject_end: 38
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -1998,14 +1214,6 @@ matches_whole_text: false
subject_start: 34
subject_end: 38
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 39
-subject_end: 43
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -2014,14 +1222,6 @@ matches_whole_text: false
subject_start: 39
subject_end: 43
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 37
-subject_end: 41
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -2030,14 +1230,6 @@ matches_whole_text: false
subject_start: 37
subject_end: 41
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 48
-subject_end: 52
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
@@ -2046,14 +1238,6 @@ matches_whole_text: false
subject_start: 48
subject_end: 52
--
-predicate_id: oio:hasExactSynonym
-object_id: https://w3id.org/aio/Model
-object_label: Model
-match_string: Model
-matches_whole_text: false
-subject_start: 31
-subject_end: 35
---
predicate_id: rdfs:label
object_id: https://w3id.org/aio/Model
object_label: Model
diff --git a/annotate/run_all.sh b/annotate/run_all.sh
index c57ea32..4d31171 100755
--- a/annotate/run_all.sh
+++ b/annotate/run_all.sh
@@ -2,6 +2,7 @@
grep 'area\":' methods_reformat.txt | sort | uniq > methods_reformat_area.txt
grep 'collection\":' methods_reformat.txt | sort | uniq > methods_reformat_collection.txt
+#--output-type yaml/tsv
runoak -i ../aio.owl annotate --text-file methods_reformat.txt > methods_reformat_annotate.out
# --matches-whole-text
@@ -14,6 +15,6 @@ source PWC_AIO_counts.sh
grep match_string methods_reformat_collection_annotate_AIOonly.txt | sort | uniq | wc
- grep object_label methods_reformat_annotate_AIOonly.txt | sort | uniq | wc
+grep object_label methods_reformat_annotate_AIOonly.txt | sort | uniq | wc
diff --git a/annotate/top_50_aio_annotations.pdf b/annotate/top_50_aio_annotations.pdf
index 5bca16b..514c4e8 100644
Binary files a/annotate/top_50_aio_annotations.pdf and b/annotate/top_50_aio_annotations.pdf differ
diff --git a/annotate/top_50_aio_annotations.png b/annotate/top_50_aio_annotations.png
index 2110687..4f29bf3 100644
Binary files a/annotate/top_50_aio_annotations.png and b/annotate/top_50_aio_annotations.png differ