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WOS_ecoevo.bib
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@article{ WOS:000366536100003,
Author = {Vilares, Manuel and Fernandez, Milagros and Blanco, Adrian},
Title = {Supporting knowledge discovery for biodiversity},
Journal = {DATA \& KNOWLEDGE ENGINEERING},
Year = {2015},
Volume = {100},
Number = {A},
Pages = {34-53},
Month = {NOV},
Abstract = {A proposal for text mining as a support for knowledge discovery on
biological descriptions is introduced. Our aim is both to sustain the
curation of databases and to offer an alternative representation frame
for accessing information in the biodiversity domain. We work on raw
texts with minimum human intervention, applying natural language
processing to integrate linguistic and domain knowledge in a
mathematical model that makes it possible to capture concepts and
relationships between them in a computable form, using conceptual
graphs. This provides a reasoning basis for determining semantic
disjointedness or subsumption, as well as sub and super-concept
relationships. (C) 2015 Elsevier B.V. All rights reserved.},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Article},
Language = {English},
Affiliation = {Vilares, M (Corresponding Author), Univ Vigo, Dept Comp Sci, Campus Lagoas S-N, Orense 32004, Spain.
Vilares, Manuel; Fernandez, Milagros; Blanco, Adrian, Univ Vigo, Dept Comp Sci, Orense 32004, Spain.},
DOI = {10.1016/j.datak.2015.08.002},
ISSN = {0169-023X},
EISSN = {1872-6933},
Keywords = {Knowledge discovery; Natural language processing; Text mining},
Keywords-Plus = {INFORMATION-RETRIEVAL; CONCEPTUAL GRAPHS; LANGUAGE; COMPLEXITY; SEARCH},
Research-Areas = {Computer Science},
Web-of-Science-Categories = {Computer Science, Artificial Intelligence; Computer Science, Information
Systems},
Author-Email = {vilares@uvigo.es
mfgavilanes@uvigo.es
adbgonzalez@uvigo.es},
ResearcherID-Numbers = {Fernández-Gavilanes, Milagros/D-1661-2018
Fernández-Gavilanes, Milagros/R-6741-2018},
ORCID-Numbers = {Fernández-Gavilanes, Milagros/0000-0001-7596-3790
Fernández-Gavilanes, Milagros/0000-0001-7596-3790},
Funding-Acknowledgement = {Ministry of Science and InnovationSpanish Government
{[}FFI2014-51978-C2-1-R]; Autonomous Government of Galicia {[}R2014/029,
R2014/034]},
Funding-Text = {Partially funded by the Ministry of Science and Innovation through
project FFI2014-51978-C2-1-R, and by the Autonomous Government of
Galicia through projects R2014/029 and R2014/034.},
Number-of-Cited-References = {66},
Times-Cited = {1},
Usage-Count-Last-180-days = {0},
Usage-Count-Since-2013 = {6},
Journal-ISO = {Data Knowl. Eng.},
Doc-Delivery-Number = {CY6RI},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000366536100003},
DA = {2022-03-29},
}
@article{ WOS:000462027600008,
Author = {Chaix, Estelle and Deleger, Louise and Bossy, Robert and Nedellec,
Claire},
Title = {Text mining tools for extracting information about microbial
biodiversity in food},
Journal = {FOOD MICROBIOLOGY},
Year = {2019},
Volume = {81},
Number = {SI},
Pages = {63-75},
Month = {AUG},
Note = {2nd Symposium on Microbial Spoilers in Food (SPOILERS), Quimper, FRANCE,
JUN 28-30, 2017},
Abstract = {Information on food microbial diversity is scattered across millions of
scientific papers. Researchers need tools to assist their bibliographic
search in such large collections. Text mining and knowledge engineering
methods are useful to automatically and efficiently find relevant
information in Life Science. This work describes how the Alvis text
mining platform has been applied to a large collection of PubMed
abstracts of scientific papers in the food microbiology domain. The
information targeted by our work is microorganisms, their habitats and
phenotypes. Two knowledge resources, the NCBI taxonomy and the
OntoBiotope ontology were used to detect this information in texts. The
result of the text mining process was indexed and is presented through
the AlvisIR Food on-line semantic search engine. In this paper, we also
show through two illustrative examples the great potential of this new
tool to assist in studies on ecological diversity and the origin of
microbial presence in food. (C) 2018 The Authors. Published by Elsevier
Ltd.},
Publisher = {ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD},
Address = {24-28 OVAL RD, LONDON NW1 7DX, ENGLAND},
Type = {Article; Proceedings Paper},
Language = {English},
Affiliation = {Chaix, E; Nedellec, C (Corresponding Author), Univ Paris Saclay, MaIAGE, INRA, F-78350 Jouy En Josas, France.
Chaix, Estelle; Deleger, Louise; Bossy, Robert; Nedellec, Claire, Univ Paris Saclay, MaIAGE, INRA, F-78350 Jouy En Josas, France.},
DOI = {10.1016/j.fm.2018.04.011},
ISSN = {0740-0020},
EISSN = {1095-9998},
Keywords = {Microbial biodiversity; Text mining; Information extraction; Food
spoilage},
Keywords-Plus = {SPORE-FORMING BACTERIA; SP NOV.; COXIELLA-BURNETII; ONTOLOGY; GROWTH;
CONTAMINATION; VEGETABLES; CHEESES; STORAGE; MILK},
Research-Areas = {Biotechnology \& Applied Microbiology; Food Science \& Technology;
Microbiology},
Web-of-Science-Categories = {Biotechnology \& Applied Microbiology; Food Science \& Technology;
Microbiology},
Author-Email = {estelle.chaix@inra.fr
claire.nedellec@inra.fr},
ORCID-Numbers = {Nedellec, Claire/0000-0002-0577-0595
Chaix, Estelle/0000-0003-0975-7854
Deleger, Louise/0000-0003-1399-4828},
Funding-Acknowledgement = {OpenMinTeD project {[}EC/H2020-EINFRA 654021]},
Funding-Text = {This work was supported by the OpenMinTeD project (EC/H2020-EINFRA
654021). We would like to thank biologists from the French National
Institute for Agricultural Research (Inra) Florilege working group and
Food Microbiome project, for their participation in the enrichment of
the OntoBiotope Habitat ontology. We would like to thank Olivier Couvert
for this advice on spore-forming microorganisms. We are grateful to the
Inra MIGALE bioinformatics platform for providing computational
resources. We would like to express our gratitude to Philippe Bessieres,
who is deceased, and who contributed to the preliminary studies of this
work.},
Number-of-Cited-References = {54},
Times-Cited = {12},
Usage-Count-Last-180-days = {0},
Usage-Count-Since-2013 = {77},
Journal-ISO = {Food Microbiol.},
Doc-Delivery-Number = {HP9QH},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S)},
Unique-ID = {WOS:000462027600008},
OA = {hybrid, Green Published, Green Submitted},
DA = {2022-03-29},
}
@article{ WOS:000582113700039,
Author = {Jung, Hoon and Lee, Bong Gyou},
Title = {Research trends in text mining: Semantic network and main path analysis
of selected journals},
Journal = {EXPERT SYSTEMS WITH APPLICATIONS},
Year = {2020},
Volume = {162},
Month = {DEC 30},
Abstract = {In this study, network and main path analyses were conducted on 1856
studies related to text mining, by extracting keywords and citation
information from the text of each paper. Our findings indicate that
research papers on text mining have been published in 45 academic
disciplines in the 1980s and 1990s, 105 disciplines in the 2000s, and
171 disciplines in the 2010s. The results show that using text mining as
a research topic and method has rapidly increased. We also demonstrate
that the main theme of text mining research is discourse and content
analysis in the 1980s and 1990s, biology and data mining in the 2000s,
and medicine and advanced text mining in the 2010s. Moreover, we
examined the main citation path for text mining studies and suggest that
the main focus of text mining studies has evolved from information
science to information systems and technology management. Additionally,
influential papers have been recently published in fields such as
architecture and social ecology revealing the wide scope of text mining.
This article presents an understanding of previously unexplored research
trends in text mining and how these trends shed light on the most
influential academic papers in the field.},
Publisher = {PERGAMON-ELSEVIER SCIENCE LTD},
Address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND},
Type = {Article},
Language = {English},
Affiliation = {Lee, BG (Corresponding Author), Yonsei Univ, Seoul 03722, South Korea.
Jung, Hoon, Hana Inst Finance, Seoul 07321, South Korea.
Lee, Bong Gyou, Yonsei Univ, Seoul 03722, South Korea.},
DOI = {10.1016/j.eswa.2020.113851},
Article-Number = {113851},
ISSN = {0957-4174},
EISSN = {1873-6793},
Keywords = {Text mining; Research trends; Keywords; Semantic network analysis; Main
path analysis},
Keywords-Plus = {WEB},
Research-Areas = {Computer Science; Engineering; Operations Research \& Management Science},
Web-of-Science-Categories = {Computer Science, Artificial Intelligence; Engineering, Electrical \&
Electronic; Operations Research \& Management Science},
Author-Email = {bglee@yonsei.ac.kr},
Number-of-Cited-References = {28},
Times-Cited = {11},
Usage-Count-Last-180-days = {29},
Usage-Count-Since-2013 = {84},
Journal-ISO = {Expert Syst. Appl.},
Doc-Delivery-Number = {OG8FU},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000582113700039},
DA = {2022-03-29},
}
@article{ WOS:000417345900008,
Author = {Faggiolani, Chiara and Verna, Lorenzo and Vivarelli, Maurizio},
Title = {Text mining and network science to analyze the complexity of reading.
Principles, methods, application experiences},
Journal = {JLIS.IT},
Year = {2017},
Volume = {8},
Number = {3},
Pages = {115-136},
Abstract = {This paper proposes some reflections concerning the practice of reading,
its conceptual structure and its transformations, the blurred profile of
the information ecology in which it is inserted. At the same time
illustrates some outcomes of a research project conducted with tools of
text mining and network science on the social reading platform aNobii.
The paper presents these main topics: a) general overview of reading's
context; b) short discussion reading as a complex system; c)
presentation of some central concepts of network science and of its
applications; d) introduction to text mining with some results of
analysis of aNobii's reviews; e) conclusions and prospectives.},
Publisher = {UNIV STUDI FIRENZE, DIPT STUDI MEDIOEVO \& RINASCIMENTO},
Address = {PIAZZA BRUNELLESCHI 3-4, FLORENCE, 50121, ITALY},
Type = {Article},
Language = {Italian},
Affiliation = {Faggiolani, C (Corresponding Author), Univ Roma La Sapienza, Rome, Italy.
Faggiolani, Chiara, Univ Roma La Sapienza, Rome, Italy.
Verna, Lorenzo, Tykli Srl, Turin, Italy.
Vivarelli, Maurizio, Univ Turin, Turin, Italy.},
DOI = {10.4403/jlis.it-12414},
ISSN = {2038-5366},
EISSN = {2038-1026},
Keywords = {Reading; Social Reading; Complex System; Text mining; Network Science;
aNobii},
Research-Areas = {Information Science \& Library Science},
Web-of-Science-Categories = {Information Science \& Library Science},
Author-Email = {chiara.faggiolani@uniroma1.it},
Number-of-Cited-References = {42},
Times-Cited = {3},
Usage-Count-Last-180-days = {1},
Usage-Count-Since-2013 = {5},
Journal-ISO = {JLIS.it},
Doc-Delivery-Number = {FP1BR},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000417345900008},
DA = {2022-03-29},
}
@article{ WOS:000526142200003,
Author = {Dayeen, Fazle Rabbi and Sharma, Abhinav S. and Derrible, Sybil},
Title = {A text mining analysis of the climate change literature in industrial
ecology},
Journal = {JOURNAL OF INDUSTRIAL ECOLOGY},
Year = {2020},
Volume = {24},
Number = {2, SI},
Pages = {276-284},
Month = {APR},
Abstract = {The literature on climate change research has evolved tremendously since
the 1990s. The goal of this study is to use text mining to review the
climate change literature and study the evolution of the main trends
over time. Specific keywords from articles published in the special
issue `` Industrial Ecology for Climate Change Adaptation and
Resilience{''} in the Journal of Industrial Ecology are first selected.
Details of over 35,000 publications containing these keywords are
downloaded from the Web of Science from 1990 to 2018. The number of
publications and co-occurrence of keywords are analyzed. Moreover,
latent Dirichlet allocation (LDA)-a probabilistic approach that can
retrieve topics from large and unstructured text documents-is applied on
the abstracts to uncover the main topics (consisting of new terms) that
naturally emerge from them. The evolution in time of the importance of
some emerging topics is then analyzed on the basis of their relative
frequency. Overall, a rapid growth in climate change publications is
observed. Terms such as ``climate change adaptation{''} appear on the
rise, whereas other terms are declining such as ``pollution.{''}
Moreover, several terms tend to co-occur frequently, such as ``climate
change adaptation{''} and ``resilience.{''} The database collected and
the LiTCoF (Literature Topic Co-occurrence and Frequency) Python-based
tool developed for this study are also made openly accessible. This
article met the requirements for a gold - gold JIE data openness badge
described .},
Publisher = {WILEY},
Address = {111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
Type = {Article},
Language = {English},
Affiliation = {Derrible, S (Corresponding Author), Univ Illinois, Dept Civil \& Mat Engn, Chicago, IL 60607 USA.
Dayeen, Fazle Rabbi, Univ Illinois, Dept Phys, Chicago, IL 60680 USA.
Sharma, Abhinav S., Univ New South Wales, Sch Photovolta \& Renewable Energy Engn, Sydney, NSW, Australia.
Derrible, Sybil, Univ Illinois, Dept Civil \& Mat Engn, Chicago, IL 60607 USA.},
DOI = {10.1111/jiec.12998},
ISSN = {1088-1980},
EISSN = {1530-9290},
Keywords = {academic publishing; climate change; industrial ecology; resilience;
text mining; topic modeling},
Keywords-Plus = {RESILIENCE; EVOLUTION},
Research-Areas = {Science \& Technology - Other Topics; Engineering; Environmental
Sciences \& Ecology},
Web-of-Science-Categories = {Green \& Sustainable Science \& Technology; Engineering, Environmental;
Environmental Sciences},
Author-Email = {derrible@uic.edu},
ResearcherID-Numbers = {Dayeen, Fazle/E-1896-2019
},
ORCID-Numbers = {Dayeen, Fazle/0000-0003-3006-9343
Sharma, Abhinav/0000-0002-2707-0303
Derrible, Sybil/0000-0002-2939-6016},
Number-of-Cited-References = {17},
Times-Cited = {5},
Usage-Count-Last-180-days = {8},
Usage-Count-Since-2013 = {30},
Journal-ISO = {J. Ind. Ecol.},
Doc-Delivery-Number = {LD6LK},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000526142200003},
OA = {Bronze},
DA = {2022-03-29},
}
@article{ WOS:000439819200001,
Author = {Schober, Andreas and Simunovic, Nenad and Darabant, Andras and Stern,
Tobias},
Title = {Identifying sustainable forest management research narratives: a text
mining approach},
Journal = {JOURNAL OF SUSTAINABLE FORESTRY},
Year = {2018},
Volume = {37},
Number = {6},
Pages = {537-554},
Abstract = {Although it is obvious that research regarding Sustainable Forest
Management (SFM) is context specific and developed over time, not many
research papers yet intended to investigate these changes. As a matter
of fact, the number of scientific publications addressing SFM is
relatively high. Hence, such a wide field cannot be sufficiently covered
by traditional literature review approaches. With this paper, we aim at
identifying the most convergent narratives within the SFM-research
landscape by applying a text mining methodology to recent scientific
literature. By doing so, we generated results that indicate that there
may have been three phases in the evolution of SFM-research: the early
phase covers in particular issues regarding land use in tropical and
developing countries. Furthermore, papers in this phase tend to focus on
general concepts or policy issues. In contrast, the second phase is
characterized by a larger share of publications in forestry focused
journals. This process is seemingly connected with issues like forest
management, certification, forest stand management and the development
of sustainability indicators. A third phase can be observed by the
relative downturn of publications in forest-focused journals between
2005 and 2010. A new focus in this period is climate change.},
Publisher = {TAYLOR \& FRANCIS INC},
Address = {530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA},
Type = {Article},
Language = {English},
Affiliation = {Schober, A (Corresponding Author), Karl Franzens Univ Graz, Inst Syst Sci Innovat \& Sustainabil Res, Merangasse 18-1, A-8010 Graz, Austria.
Schober, Andreas; Stern, Tobias, Karl Franzens Univ Graz, Inst Syst Sci Innovat \& Sustainabil Res, Merangasse 18-1, A-8010 Graz, Austria.
Simunovic, Nenad, Kompetenzzentrum Holz Wood K Plus, Market Anal \& Innovat Res Team, Vienna, Austria.
Darabant, Andras, Univ Nat Resources \& Life Sci, Ctr Dev Res, Vienna, Austria.},
DOI = {10.1080/10549811.2018.1437451},
ISSN = {1054-9811},
EISSN = {1540-756X},
Keywords = {Sustainable forest management (SFM); SFM meta study; text mining; topic
modelling; topics in SFM; evolution of SFM},
Keywords-Plus = {INDICATORS; CERTIFICATION; BIODIVERSITY; CRITERIA; LESSONS; SYSTEM},
Research-Areas = {Forestry},
Web-of-Science-Categories = {Forestry},
Author-Email = {andreas.schober@uni-graz.at},
ResearcherID-Numbers = {Stern, Tobias/V-9665-2017
},
ORCID-Numbers = {Stern, Tobias/0000-0003-2336-5910},
Number-of-Cited-References = {55},
Times-Cited = {16},
Usage-Count-Last-180-days = {6},
Usage-Count-Since-2013 = {16},
Journal-ISO = {J. Sustain. For.},
Doc-Delivery-Number = {GO2PU},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000439819200001},
OA = {hybrid},
DA = {2022-03-29},
}
@article{ WOS:000600072400007,
Author = {Cho, Nam-Wook and Lee, Moung-Jin},
Title = {Exploring Changes in Coastal Environment Policy Using Text Mining: A
Case Study in South Korea},
Journal = {JOURNAL OF COASTAL RESEARCH},
Year = {2020},
Number = {102},
Pages = {47-53},
Month = {FAL},
Abstract = {Land development activities performed in coastal areas are accompanied
by environmental impacts, and Environmental Impact Assessment (EIA) is
used to mitigate such impacts. The characteristics of coastal
environmental policy can be determined based on the content of EIA
reports for development projects However, owing to the vast amounts of
information regarding various environmental factors, including air,
water, and soil, contained in such reports, their potential for analysis
is limited. This study analyzed the terms of agreement for development
projects-theresult of EIA of coastal areas-using bibliometrics, a method
that is widely used to analyze trends in academic research. Data on the
EIA of coastal development projects conducted in South Korea over the
past 25 years were collected, and the surveyed period was sub-divided
into five-year periods to build datasets through text mining.
Subsequently, trends in coastal environmental policy were analyzed over
an extended period. The results of the analysis indicated that keywords
related to the water environment showed the highest appearance frequency
for the entire period due to the characteristics of coastal development
projects. Keywords related to the natural ecological environment
continued to show an increasing trend, while those related to the land
environment showed a continuously decreasing trend. Based on
correspondence analysis, the five sub-divided periods were broadly
classified into three groups, and the principal inertia of the analysis
was found to be approximately 89.6 \%. The characteristics of the
coastal environmental policy that were emphasized for each period were
determined; land and life environments were found to be emphasized for
the period between 1994 and 1999; water environments were emphasized
between 2000 and 2009; and natural ecology environments were emphasized
between 2010 and 2019. By contrast, atmospheric and socio-economic
environments did not show a significant correlation. This study proposes
methods of utilizing information provided by EIA reports to analyze
environmental policy and offers new insights specifically applicable to
coastal environmental policy.},
Publisher = {COASTAL EDUCATION \& RESEARCH FOUNDATION},
Address = {5130 NW 54TH STREET, COCONUT CREEK, FL 33073 USA},
Type = {Article},
Language = {English},
Affiliation = {Lee, MJ (Corresponding Author), Korea Environm Inst KEI, Ctr Environm Data Strategy, Sejong, South Korea.
Cho, Nam-Wook, Korea Environm Inst KEI, Ctr Environm Assessment Monitoring, Sejong, South Korea.
Lee, Moung-Jin, Korea Environm Inst KEI, Ctr Environm Data Strategy, Sejong, South Korea.},
DOI = {10.2112/SI102-006.1},
ISSN = {0749-0208},
EISSN = {1551-5036},
Keywords = {Coastal development project; environmental impact assessment; text
mining; correspondence analysis},
Keywords-Plus = {INFORMATION},
Research-Areas = {Environmental Sciences \& Ecology; Physical Geography; Geology},
Web-of-Science-Categories = {Environmental Sciences; Geography, Physical; Geosciences,
Multidisciplinary},
Author-Email = {leemj@kei.re.kr},
Funding-Acknowledgement = {Basic Science Research Program through the National Research Foundation
of Korea (NRF) - Ministry of Education {[}NRF2018R1D1A1B07041203];
`Future Forecasting Government Management Support: A Study on the
Modeling of Climate and Environment for the Transition to Data Science'
- National Research Council for Economics, Humanities and Social
Sciences},
Funding-Text = {This research was conducted at the Korea Environment Institute (KEI)
with support from the Basic Science Research Program through the
National Research Foundation of Korea (NRF) funded by the Ministry of
Education (NRF2018R1D1A1B07041203). This research was also conducted
with support from `Future Forecasting Government Management Support: A
Study on the Modeling of Climate and Environment for the Transition to
Data Science' funded by the National Research Council for Economics,
Humanities and Social Sciences.},
Number-of-Cited-References = {31},
Times-Cited = {2},
Usage-Count-Last-180-days = {1},
Usage-Count-Since-2013 = {4},
Journal-ISO = {J. Coast. Res.},
Doc-Delivery-Number = {PG9UX},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000600072400007},
DA = {2022-03-29},
}
@article{ WOS:000516824600273,
Author = {Juventia, Stella D. and Jones, Sarah K. and Laporte, Marie-Angelique and
Remans, Roseline and Villani, Chiara and Estrada-Carmona, Natalia},
Title = {Text Mining National Commitments towards Agrobiodiversity Conservation
and Use},
Journal = {SUSTAINABILITY},
Year = {2020},
Volume = {12},
Number = {2},
Month = {JAN 2},
Abstract = {Capturing countries' commitments for measuring and monitoring progress
towards certain goals, including the Sustainable Development Goals
(SDGs), remains underexplored. The Agrobiodiversity Index bridges this
gap by using text mining techniques to quantify countries' commitments
towards safeguarding and using agrobiodiversity for healthy diets,
sustainable agriculture, and effective genetic resource management. The
Index extracts potentially relevant sections of official documents,
followed by manual sifting and scoring to identify
agrobiodiversity-related commitments and assign scores. Our aim is to
present the text mining methodology used in the Agrobiodiversity Index
and the calculated commitments scores for nine countries while
identifying methodological improvements to strengthen it. Our results
reveal that levels of commitment towards using and protecting
agrobiodiversity vary between countries, with most showing the strongest
commitments to enhancing agrobiodiversity for genetic resource
management followed by healthy diets. No commitments were found in any
country related to some specific themes including varietal diversity,
seed diversity, and functional diversity. The revised text mining
methodology can be used for benchmarking, learning, and improving
policies to enable conservation and sustainable use of agrobiodiversity.
This low-cost, rapid, remotely applicable approach to capture and
analyse policy commitments can be readily applied for tracking progress
towards meeting other sustainability objectives.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Article},
Language = {English},
Affiliation = {Estrada-Carmona, N (Corresponding Author), Alliance Biovers Int, Parc Sci Agropolis 2, F-34397 Montpellier, France.
Estrada-Carmona, N (Corresponding Author), CIAT, Parc Sci Agropolis 2, F-34397 Montpellier, France.
Juventia, Stella D.; Jones, Sarah K.; Laporte, Marie-Angelique; Estrada-Carmona, Natalia, Alliance Biovers Int, Parc Sci Agropolis 2, F-34397 Montpellier, France.
Juventia, Stella D.; Jones, Sarah K.; Laporte, Marie-Angelique; Estrada-Carmona, Natalia, CIAT, Parc Sci Agropolis 2, F-34397 Montpellier, France.
Juventia, Stella D., Wageningen Univ \& Res, Farming Syst Ecol Grp, NL-6700 AK Wageningen, Netherlands.
Remans, Roseline; Villani, Chiara, Alliance Biovers Int, Via Tre Denari 472-a, I-00054 Maccarese, Fiumicino, Italy.
Remans, Roseline; Villani, Chiara, CIAT, Via Tre Denari 472-a, I-00054 Maccarese, Fiumicino, Italy.},
DOI = {10.3390/su12020715},
Article-Number = {715},
EISSN = {2071-1050},
Keywords = {target monitoring; public policy; healthy diets; genetic resources;
sustainable agriculture; agricultural biodiversity; artificial
intelligence},
Keywords-Plus = {INDEX; INDICATORS},
Research-Areas = {Science \& Technology - Other Topics; Environmental Sciences \& Ecology},
Web-of-Science-Categories = {Green \& Sustainable Science \& Technology; Environmental Sciences;
Environmental Studies},
Author-Email = {juventia.stella@wur.nl
s.jones@cgiar.org
m.a.laporte@cgiar.org
r.remans@cgiar.org
c.villani@cgiar.org
n.e.carmona@cgiar.org},
ResearcherID-Numbers = {CARMONA, NATALIA ESTRADA/AAN-7794-2020
},
ORCID-Numbers = {CARMONA, NATALIA ESTRADA/0000-0003-4329-5470
Villani, Chiara/0000-0001-9750-3867
Laporte, Marie-Angelique/0000-0002-8461-9745
Jones, Sarah/0000-0002-9422-5563
Remans, Roseline/0000-0003-3659-8529
Juventia, Stella Dimitri/0000-0003-4393-624X},
Funding-Acknowledgement = {European CommissionEuropean CommissionEuropean Commission Joint Research
Centre {[}FOOD/2016/378-156, FOOD/2017/391-245]; Italian Development
Cooperation},
Funding-Text = {This project was financially supported by the European Commission
(FOOD/2016/378-156 and FOOD/2017/391-245) and the Italian Development
Cooperation.},
Number-of-Cited-References = {40},
Times-Cited = {3},
Usage-Count-Last-180-days = {6},
Usage-Count-Since-2013 = {11},
Journal-ISO = {Sustainability},
Doc-Delivery-Number = {KQ3KE},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000516824600273},
OA = {Green Published, gold},
DA = {2022-03-29},
}
@inproceedings{ WOS:000425804300003,
Author = {Batista-Navarro, Riza and Zerva, Chrysoula and Nguyen, Nhung T. H. and
Ananiadou, Sophia},
Editor = {LossioVentura, JA and AlatristaSalas, H},
Title = {A Text Mining-Based Framework for Constructing an RDF-Compliant
Biodiversity Knowledge Repository},
Booktitle = {INFORMATION MANAGEMENT AND BIG DATA},
Series = {Communications in Computer and Information Science},
Year = {2017},
Volume = {656},
Pages = {30-42},
Note = {3rd Annual International Symposium on Information Management and Big
Data (SIMBig), Cusco, PERU, SEP 01-03, 2016},
Organization = {Univ Andina Cusco; Univ Florida; Univ Pacifico; Pontificia Univ Catolica
Peru; Lab Informatique Robotique \& Micro Electronique Montpellier; Univ
Montpellier},
Abstract = {In our aim to make the information encapsulated by biodiversity
literature more accessible and searchable, we have developed a text
mining-based framework for automatically transforming text into a
structured knowledge repository. A text mining workflow employing
information extraction techniques, i.e., named entity recognition and
relation extraction, was implemented in the Argo platform and was
subsequently applied on biodiversity literature to extract structured
information. The resulting annotations were stored in a repository
following the emerging Open Annotation standard, thus promoting
interoperability with external applications. Accessible as a SPARQL
endpoint, the repository facilitates knowledge discovery over a huge
amount of biodiversity literature by retrieving annotations matching
user-specified queries. We present some use cases to illustrate the
types of queries that the knowledge repository currently accommodates.},
Publisher = {SPRINGER INTERNATIONAL PUBLISHING AG},
Address = {GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND},
Type = {Proceedings Paper},
Language = {English},
Affiliation = {Ananiadou, S (Corresponding Author), Univ Manchester, Sch Comp Sci, Manchester M13 9PL, Lancs, England.
Batista-Navarro, Riza; Zerva, Chrysoula; Nguyen, Nhung T. H.; Ananiadou, Sophia, Univ Manchester, Sch Comp Sci, Manchester M13 9PL, Lancs, England.},
DOI = {10.1007/978-3-319-55209-5\_3},
ISSN = {1865-0929},
EISSN = {1865-0937},
ISBN = {978-3-319-55209-5; 978-3-319-55208-8},
Research-Areas = {Computer Science},
Web-of-Science-Categories = {Computer Science, Information Systems; Computer Science, Theory \&
Methods},
Author-Email = {riza.batista@manchester.ac.uk
chrysoula.zerva@manchester.ac.uk
nhung.nguyen@manchester.ac.uk
sophia.ananiadou@manchester.ac.uk},
ResearcherID-Numbers = {Batista-Navarro, Riza/ABD-6210-2021
},
ORCID-Numbers = {Zerva, Chrysoula/0000-0002-4031-9492
Batista-Navarro, Riza/0000-0001-6693-7531},
Funding-Acknowledgement = {British CouncilThe British Council in India {[}172722806]; Engineering
and Physical Sciences Research CouncilUK Research \& Innovation
(UKRI)Engineering \& Physical Sciences Research Council (EPSRC)
{[}EP/1038099/1]},
Funding-Text = {We would like to thank Prof. Marilou Nicolas for her valuable inputs.
This work is funded by the British Council {[}172722806 (COPIOUS)], and
is partially supported by the Engineering and Physical Sciences Research
Council {[}EP/1038099/1 (CDT)].},
Number-of-Cited-References = {13},
Times-Cited = {3},
Usage-Count-Last-180-days = {0},
Usage-Count-Since-2013 = {2},
Doc-Delivery-Number = {BJ5DO},
Web-of-Science-Index = {Conference Proceedings Citation Index - Science (CPCI-S)},
Unique-ID = {WOS:000425804300003},
DA = {2022-03-29},
}
@inproceedings{ WOS:000574778100014,
Author = {Kaur, Pawandeep and Kiesel, Dora},
Editor = {Kerren, A and Hurter, C and Braz, J},
Title = {Combining Image and Caption Analysis for Classifying Charts in
Biodiversity Texts},
Booktitle = {IVAPP: PROCEEDINGS OF THE 15TH INTERNATIONAL JOINT CONFERENCE ON
COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS,
VOL 3: IVAPP},
Year = {2020},
Pages = {157-168},
Note = {15th International Joint Conference on Computer Vision, Imaging and
Computer Graphics Theory and Applications (VISIGRAPP) / 11th
International Conference on Information Visualization Theory and
Applications (IVAPP), Valletta, MALTA, FEB 27-29, 2020},
Abstract = {Chart type classification through caption analysis is a new area of
study. Distinct keywords in the captions that relate to the
visualization vocabulary (e.g., for scatterplot: dot, y-axis, x-axis,
bubble) and keywords from the specific domain (e.g., species richness,
species abundance, phylogenetic associations in the case of biodiversity
research), serve as parameters to train a text classifier. For better
chart comprehensibility, along with the visual characteristics of the
chart, a classifier should also understand these parameters well. Such
conceptual/semantic chart classifiers then will not only be useful for
chart classification purposes but also for other visualization studies.
One of the applications of such a classifier is in the creation of the
domain knowledge-assisted visualization recommendation system, where
these text classifiers can provide the recommendation of visualization
types based on the classification of the text provided along with the
dataset. Motivated by this use case, in this paper, we have explored our
idea of semantic chart classifiers. We have taken the assistance of
state-of-the-art natural language processing (NLP) and computer vision
algorithms to create a biodiversity domain-based visualization
classifier. With an average test accuracy (F1-score) of 92.2\% over all
15 classes, we can prove that our classifiers can differentiate between
different chart types conceptually and visually.},
Publisher = {SCITEPRESS},
Address = {AV D MANUELL, 27A 2 ESQ, SETUBAL, 2910-595, PORTUGAL},
Type = {Proceedings Paper},
Language = {English},
Affiliation = {Kaur, P (Corresponding Author), Friedrich Schiller Univ, Heinz Nixdorf Chair Distributed Informat Syst, Jena, Germany.
Kaur, Pawandeep, Friedrich Schiller Univ, Heinz Nixdorf Chair Distributed Informat Syst, Jena, Germany.
Kiesel, Dora, Bauhaus Univ, Fac Media, VR Grp, Weimar, Germany.},
DOI = {10.5220/0008946701570168},
ISBN = {978-989-758-402-2},
Keywords = {Caption Analysis; Chart Classification; Natural Language Processing;
NLP; Caption Classification; Visualization Recommendation; Chart
Recognition},
Research-Areas = {Computer Science},
Web-of-Science-Categories = {Computer Science, Software Engineering},
Funding-Acknowledgement = {DFG Priority Program 1374 Infrastructure-Biodiversity-Exploratories
{[}KO 2209 / 12-2]},
Funding-Text = {The work has been funded by the DFG Priority Program 1374
Infrastructure-Biodiversity-Exploratories (KO 2209 / 12-2). We would
like to thanks our project principal investigator Birgitta Konig-Ries
for her valuable feedback.},
Number-of-Cited-References = {30},
Times-Cited = {0},
Usage-Count-Last-180-days = {1},
Usage-Count-Since-2013 = {1},
Doc-Delivery-Number = {BQ0XS},
Web-of-Science-Index = {Conference Proceedings Citation Index - Science (CPCI-S)},
Unique-ID = {WOS:000574778100014},
DA = {2022-03-29},
}
@inproceedings{ WOS:000389021000044,
Author = {Batista-Navarro, Riza and Hammock, Jennifer and Ulate, William and
Ananiadou, Sophia},
Editor = {Fuhr, N and Kovacs, L and Risse, T and Nejdl, W},
Title = {A Text Mining Framework for Accelerating the Semantic Curation of
Literature},
Booktitle = {RESEARCH AND ADVANCED TECHNOLOGY FOR DIGITAL LIBRARIES, TPDL 2016},
Series = {Lecture Notes in Computer Science},
Year = {2016},
Volume = {9819},
Pages = {459-462},
Note = {20th International Conference on Theory and Practice of Digital
Libraries (TPDL), Hannover, GERMANY, SEP 05-09, 2016},
Organization = {Elsevier B V; Ex Libris; L3S Res Ctr; German Natl Lib Sci \& Technol},
Abstract = {The Biodiversity Heritage Library is the world's largest digital library
of biodiversity literature. Currently containing almost 40 million
pages, the library can be explored with a search interface employing
keyword-matching, which unfortunately fails to address issues brought
about by ambiguity. Helping alleviate these issues are tools that
automatically attach semantic metadata to documents, e.g., biodiversity
concept recognisers. However, gold standard, semantically annotated
textual corpora are critical for the development of these advanced
tools. In the biodiversity domain, such corpora are almost non-existent
especially since the construction of semantically annotated resources is
typically a time-consuming and laborious process. Aiming to accelerate
the development of a corpus of biodiversity documents, we propose a text
mining framework that hastens curation through an iterative
feedback-loop process of (1) manual annotation, and (2) training and
application of statistical concept recognition models. Even after only a
few iterations, our curators were observed to have spent less time and
effort on annotation.},
Publisher = {SPRINGER INT PUBLISHING AG},
Address = {GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND},
Type = {Proceedings Paper},
Language = {English},
Affiliation = {Batista-Navarro, R (Corresponding Author), Univ Manchester, Sch Comp Sci, Manchester M13 9PL, Lancs, England.
Batista-Navarro, Riza; Ananiadou, Sophia, Univ Manchester, Sch Comp Sci, Manchester M13 9PL, Lancs, England.
Hammock, Jennifer, Smithsonian Inst, Washington, DC 20560 USA.
Ulate, William, Missouri Bot Garden, St Louis, MO USA.},
DOI = {10.1007/978-3-319-43997-6\_44},
ISSN = {0302-9743},
ISBN = {978-3-319-43997-6; 978-3-319-43996-9},
Research-Areas = {Computer Science; Information Science \& Library Science},
Web-of-Science-Categories = {Computer Science, Information Systems; Computer Science, Theory \&
Methods; Information Science \& Library Science},
Author-Email = {riza.batista@manchester.ac.uk},
ResearcherID-Numbers = {Batista-Navarro, Riza/ABD-6210-2021
Ulate, William/AAW-3144-2020
},
ORCID-Numbers = {Ulate, William/0000-0003-2863-2491
Batista-Navarro, Riza/0000-0001-6693-7531
Hammock, Jennifer/0000-0002-9943-2342},
Number-of-Cited-References = {3},
Times-Cited = {1},
Usage-Count-Last-180-days = {0},
Usage-Count-Since-2013 = {3},
Doc-Delivery-Number = {BG4OK},
Web-of-Science-Index = {Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH)},
Unique-ID = {WOS:000389021000044},
OA = {Green Submitted},
DA = {2022-03-29},
}
@article{ WOS:000390057600002,
Author = {Sinclair, Lucas and Ijaz, Umer Z. and Jensen, Lars Juhl and Coolen,
Marco J. L. and Gubry-Rangin, Cecile and Chronakova, Alica and Oulas,
Anastasis and Pavloudi, Christina and Schnetzer, Julia and Weimann,
Aaron and Ijaz, Ali and Eiler, Alexander and Quince, Christopher and
Pafilis, Evangelos},
Title = {Seqenv: linking sequences to environments through text mining},
Journal = {PEERJ},
Year = {2016},
Volume = {4},
Month = {DEC 20},
Abstract = {Understanding the distribution of taxa and associated traits across
different environments is one of the central questions in microbial
ecology. High-throughput sequencing (HTS) studies are presently
generating huge volumes of data to address this biogeographical topic.
However, these studies are often focused on specific environment types
or processes leading to the production of individual, unconnected
datasets. The large amounts of legacy sequence data with associated
metadata that exist can be harnessed to better place the genetic
information found in these surveys into a wider environmental context.
Here we introduce a software program, seqenv, to carry out precisely
such a task. It automatically performs similarity searches of short
sequences against the ``nt{''} nucleotide database provided by NCBI and,
out of every hit, extracts if it is available the textual metadata
field. After collecting all the isolation sources from all the search
results, we run a text mining algorithm to identify and parse words that
are associated with the Environmental Ontology (EnvO) controlled
vocabulary. This, n turn, enables us to determine both in which
environments individual sequences or taxa have previously been observed
and, by weighted summation of those results, to summarize complete
samples. We present two demonstrative applications of seqenv to a survey
of ammonia oxidizing archaea as well as to a plankton paleome dataset
from the Black Sea. These demonstrate the ability of the tool to reveal
novel patterns in HTS and its utility in the fields of environmental
source tracking, paleontology, and s of microbial biogeography.},
Publisher = {PEERJ INC},
Address = {341-345 OLD ST, THIRD FLR, LONDON, EC1V 9LL, ENGLAND},
Type = {Article},
Language = {English},
Affiliation = {Pafilis, E (Corresponding Author), Hellen Ctr Marine Res, Inst Marine Biol Biotechnol \& Aquaculture IMBBC, Iraklion, Greece.
Quince, C (Corresponding Author), Univ Warwick, Warwick Med Sch, Warwick, England.
Sinclair, Lucas; Eiler, Alexander, Uppsala Univ, Dept Ecol \& Genet, Limnol, Uppsala, Sweden.
Ijaz, Umer Z., Univ Glasgow, Sch Engn, Infrastruct \& Environm Res Div, Glasgow, Lanark, Scotland.
Jensen, Lars Juhl, Univ Copenhagen, Fac Hlth \& Med Sci, Novo Nordisk Fdn Ctr Prot Res, Copenhagen, Denmark.
Coolen, Marco J. L., Curtin Univ Technol, Dept Chem, WA OIGC, Bentley, WA, Australia.
Gubry-Rangin, Cecile, Univ Aberdeen, Inst Biol \& Environm Sci, Aberdeen, Scotland.
Chronakova, Alica, Czech Acad Sci, Ctr Biol, Inst Soil Biol, Ceske Budejovice, Czech Republic.
Oulas, Anastasis, Cyprus Inst Neurol \& Genet, Bioinformat Grp, Nicosia, Cyprus.
Oulas, Anastasis; Pavloudi, Christina; Pafilis, Evangelos, Hellen Ctr Marine Res, Inst Marine Biol Biotechnol \& Aquaculture IMBBC, Iraklion, Greece.
Schnetzer, Julia, Max Planck Inst Marine Microbiol, Microbial Genom \& Bioinformat Grp, Dept Mol Ecol, Bremen, Germany.
Weimann, Aaron, Helmholtz Ctr Infect Res, Computat Biol Infect Res, Braunschweig, Germany.
Ijaz, Ali, Univ Western Sydney, Hawkesbury Inst Environm, Sydney, NSW, Australia.
Quince, Christopher, Univ Warwick, Warwick Med Sch, Warwick, England.},
DOI = {10.7717/peerj.2690},
Article-Number = {e2690},
ISSN = {2167-8359},
Keywords = {Bioinformatics; Ecology; Microbiology; Genomics; Sequence analysis; Text
processing; Statistics; Pipeline; Open source software},
Research-Areas = {Science \& Technology - Other Topics},
Web-of-Science-Categories = {Multidisciplinary Sciences},
Author-Email = {c.quince@warwick.ac.uk
pafilis@hcmr.gr},
ResearcherID-Numbers = {Eiler, Alexander/V-9220-2017
Jensen, Lars Juhl/C-6492-2008
Chroňáková, Alica/AAS-1476-2020
Chronakova, Alica/G-1422-2014
Pavloudi, Christina/H-6608-2019
Gubry-Rangin, Cecile/ABF-4560-2020
},
ORCID-Numbers = {Eiler, Alexander/0000-0001-9916-9567
Jensen, Lars Juhl/0000-0001-7885-715X
Chroňáková, Alica/0000-0002-3316-6862
Chronakova, Alica/0000-0002-3316-6862
Pavloudi, Christina/0000-0001-5106-6067
Gubry-Rangin, Cecile/0000-0002-5937-2496
Sinclair, Lucas/0000-0003-4134-3571
Quince, Christopher/0000-0003-1884-8440
Oulas, Anastasis/0000-0002-9350-7577},
Funding-Acknowledgement = {Swedish Foundation for strategic researchSwedish Foundation for
Strategic Research {[}ICA10-0015]; NERC IRFUK Research \& Innovation
(UKRI)Natural Environment Research Council (NERC) {[}NE/L011956/1]; Novo
Nordisk FoundationNovo Nordisk FoundationNovocure Limited
{[}NNF14CC0001]; European Commission FP7-REGPOT project MARBIGENEuropean
CommissionEuropean Commission Joint Research Centre {[}264089];
LifeWatchGreece Research Infrastructure {[}384676-94/GSRT/NSRF CE]; MRC
Cloud InfrastructureUK Research \& Innovation (UKRI)Medical Research
Council UK (MRC) {[}MR/L015080/1, MR/M50161X/1]; Environment Research
Council Fellowship {[}NE/J019151/1]; MRCUK Research \& Innovation
(UKRI)Medical Research Council UK (MRC) {[}MR/L015080/1] Funding Source:
UKRI; NERCUK Research \& Innovation (UKRI)Natural Environment Research
Council (NERC) {[}NE/J019151/1, NE/K016342/1, NE/L011956/1] Funding
Source: UKRI; Medical Research CouncilUK Research \& Innovation
(UKRI)Medical Research Council UK (MRC)European Commission
{[}MR/L015080/1, MR/M50161X/1] Funding Source: researchfish; Natural
Environment Research CouncilUK Research \& Innovation (UKRI)Natural
Environment Research Council (NERC) {[}NE/J019151/1, NE/K016342/1,
NE/L011956/1] Funding Source: researchfish; Novo Nordisk Foundation
Center for Protein Research {[}PI Lars Juhl Jensen] Funding Source:
researchfish},
Funding-Text = {Lucas Sinclair and Alexander Eiler were funded by the Swedish Foundation
for strategic research (ICA10-0015). Umer Zeeshan Ijaz was funded by
NERC IRF (NE/L011956/1). Lars Juhl Jensen was funded by the Novo Nordisk
Foundation (NNF14CC0001). Evangelos Pafilis was supported by the
European Commission FP7-REGPOT project MARBIGEN (grant agreement
\#264089) and the LifeWatchGreece Research Infrastructure
(384676-94/GSRT/NSRF C\&E). Christopher Quince is funded through the MRC
Cloud Infrastructure for Microbial Bioinformatics (CLIMB) project
(MR/L015080/1) through fellowship (MR/M50161X/1). Cecile Gubry was
funded by the Environment Research Council Fellowship (NE/J019151/1).
The funders had no role in study design, data collection and analysis,
decision to publish, or preparation of the manuscript.},
Number-of-Cited-References = {20},
Times-Cited = {15},
Usage-Count-Last-180-days = {0},
Usage-Count-Since-2013 = {20},
Journal-ISO = {PeerJ},
Doc-Delivery-Number = {EF1AO},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000390057600002},
OA = {Green Accepted, Green Submitted, Green Published, gold},
DA = {2022-03-29},
}
@inproceedings{ WOS:000626021405073,
Author = {Martino, Alessio and De Santis, Enrico and Rizzi, Antonello},
Book-Group-Author = {IEEE},
Title = {An Ecology-based Index for Text Embedding and Classification},
Booktitle = {2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)},
Series = {IEEE International Joint Conference on Neural Networks (IJCNN)},
Year = {2020},
Note = {International Joint Conference on Neural Networks (IJCNN) held as part
of the IEEE World Congress on Computational Intelligence (IEEE WCCI),
ELECTR NETWORK, JUL 19-24, 2020},
Organization = {IEEE; IEEE Computat Intelligence Soc; Int Neural Network Soc},
Abstract = {Natural language processing and text mining applications have gained a
growing attention and diffusion in the computer science and machine
learning communities. In this work, a new embedding scheme is proposed
for solving text classification problems. The embedding scheme relies on
a statistical assessment of relevant words within a corpus using a
compound index originally proposed in ecology: this allows to spot
relevant parts of the overall text (e.g., words) on the top of which the
embedding is performed following a Granular Computing approach. The
employment of statistically meaningful words not only eases the
computational burden and the embedding space dimensionality, but also
returns a more interpretable model. Our approach is tested on both
synthetic datasets and benchmark datasets against well-known embedding
techniques, with remarkable results both in terms of performances and
computational complexity.},
Publisher = {IEEE},
Address = {345 E 47TH ST, NEW YORK, NY 10017 USA},
Type = {Proceedings Paper},
Language = {English},
Affiliation = {Martino, A (Corresponding Author), Univ Roma La Sapienza, Dept Informat Engn Elect \& Telecommun, Via Eudossiana 18, I-00184 Rome, Italy.
Martino, Alessio; De Santis, Enrico; Rizzi, Antonello, Univ Roma La Sapienza, Dept Informat Engn Elect \& Telecommun, Via Eudossiana 18, I-00184 Rome, Italy.},
ISSN = {2161-4393},
ISBN = {978-1-7281-6926-2},
Keywords = {Text Classification; Natural Language Processing; Granular Computing;
Support Vector Machine; Explainable Artificial Intelligence; Supervised
Learning; Embedding Spaces},
Research-Areas = {Computer Science},
Web-of-Science-Categories = {Computer Science, Artificial Intelligence; Computer Science, Hardware \&
Architecture},
Author-Email = {alessio.martino@uniroma1.it
enrico.desantis@uniroma1.it
antonello.rizzi@uniroma1.it},
ResearcherID-Numbers = {Martino, Alessio/S-8362-2019},
ORCID-Numbers = {Martino, Alessio/0000-0003-1730-5436},
Number-of-Cited-References = {22},
Times-Cited = {0},
Usage-Count-Last-180-days = {1},
Usage-Count-Since-2013 = {1},
Doc-Delivery-Number = {BQ9MM},
Web-of-Science-Index = {Conference Proceedings Citation Index - Science (CPCI-S)},
Unique-ID = {WOS:000626021405073},
DA = {2022-03-29},
}
@article{ WOS:000594532900002,
Author = {Do, Min Seock and Choi, Green and Hwang, Jae-Woong and Lee, Ji-Yeon and
Hur, Wee-Haeng and Choi, Yu-Seong and Son, Seok-Jun and Kwon, In-Ki and
Yoo, Sung-Yeon and Nam, Hyung-Kyu},
Title = {Research topics and trends of endangered species using text mining in
Korea},
Journal = {JOURNAL OF ASIA-PACIFIC BIODIVERSITY},
Year = {2020},
Volume = {13},
Number = {4},
Pages = {518-523},
Month = {DEC 1},
Abstract = {Continuous development due to human activities has extinctionized many
floral and faunal species on earth and seriously threatened the
ecosystem. Many countries are working hard to protect and restore
endangered species by conducting a range of studies. This study
conducted text mining and sentiment analysis based on the results of the
previous studies on endangered species to present practical protection
and conservation methods through identifying the research trend of
endangered species in South Korea. This study found 451 peer-reviewed
papers and abstracts. One hundred eight-seven species were studied, and
the number of publications exponentially increased from the 1980s to the
2010s. The largest number of studies was conducted in the field of
ecology (203 publications), and majority of studies examined plants (223
publications). ``Taxa{''}, ``plant{''}, ``study{''}, and ``Korea{''}
appeared the most regardless of weighting factors. It was found that
words associated with plant identification, habitat area, and
environmental characteristics, those affecting the survival of species
and those related to conservation policies formed a network
relationship. The results of this study showed that researchers used
positive words more than negative words from the 1990s to the 2010s, but
the use of positive words decreased over time. The results implied that
the restoration of endangered species was hard socially and
scientifically. Understanding the research trend on endangered species
in South Korea will be helpful in developing and proposing important
directions for endangered species restoration research planned at the
national level. We hope that various efforts are given to improve
endangered species restoration research techniques and raise social
awareness for producing practical achievements. (C) 2020 National
Science Museum of Korea (NSMK) and Korea National Arboretum (KNA),
Publishing Services by Elsevier.},
Publisher = {NATL SCIENCE MUSEUM \& KOREAN NATL ARBORETUM},
Address = {481 DAEDEOK-DAERO, YUSEONG-GU, DAEJEON, 34143, SOUTH KOREA},
Type = {Article},
Language = {English},
Affiliation = {Nam, HK (Corresponding Author), Natl Inst Biol Resources, Natl Migratory Birds Ctr, Incheon 22689, South Korea.
Do, Min Seock, Natl Inst Biol Resources, Anim Resources Div, Incheon 22689, South Korea.
Choi, Green; Son, Seok-Jun, Kyung Hee Univ, Korea Inst Ornithol, Seoul 130701, South Korea.
Choi, Green; Son, Seok-Jun, Kyung Hee Univ, Dept Biol, Seoul 130701, South Korea.
Hwang, Jae-Woong; Lee, Ji-Yeon; Hur, Wee-Haeng; Choi, Yu-Seong; Nam, Hyung-Kyu, Natl Inst Biol Resources, Natl Migratory Birds Ctr, Incheon 22689, South Korea.
Son, Seok-Jun, Natl Res Inst Cultural Heritage Cultural Heritage, Daejeon 35204, South Korea.
Kwon, In-Ki; Yoo, Sung-Yeon, Natl Inst Ecol, Res Ctr Endangered Species, Yeongyang 36531, South Korea.},
DOI = {10.1016/j.japb.2020.09.008},
EISSN = {2287-9544},
Keywords = {South Korea; Biodiversity; Species restoration; Big data analysis;
Research trend},
Keywords-Plus = {GENETIC DIVERSITY; CAPTIVE PROPAGATION; REINTRODUCTION; BIODIVERSITY;
RESTORATION},
Research-Areas = {Biodiversity \& Conservation; Life Sciences \& Biomedicine - Other
Topics},
Web-of-Science-Categories = {Biodiversity Conservation; Biology},
Author-Email = {namhk2703@korea.kr},
Number-of-Cited-References = {38},
Times-Cited = {0},
Usage-Count-Last-180-days = {1},
Usage-Count-Since-2013 = {2},
Journal-ISO = {J. Asia-Pac. Biodivers.},
Doc-Delivery-Number = {OY9AZ},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000594532900002},
OA = {gold},
DA = {2022-03-29},
}
@article{ WOS:000561118800008,
Author = {Hui, Iris and Ulibarri, Nicola and Cain, Bruce},
Title = {Patterns of Participation and Representation in a Regional Water
Collaboration},
Journal = {POLICY STUDIES JOURNAL},
Year = {2020},
Volume = {48},
Number = {3},
Pages = {754-781},
Month = {AUG},
Abstract = {Regional collaboration has become a popular means to manage shared
resources and address cross-jurisdictional boundary issues. The question
of who participates in the process, who directly affects decisions, and
who benefits from those decisions is critical for understanding the
broader value created by regional collaborations. We apply a variety of
text mining techniques to meeting minutes to measure how stakeholder
participation evolved over nine years of an Integrated Regional Water
Management collaboration. We observe that a diverse set of organizations
representing differing interests participated to some extent in the