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Constructing a biodiversity terminological inventory
The increasing growth of literature in biodiversity presents challenges to users who need to discover pertinent information in an efficient and timely manner. In response, text mining techniques offer solutions by facilitating the automated discovery of knowledge from large textual data. An importan...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5393592/ https://www.ncbi.nlm.nih.gov/pubmed/28414821 http://dx.doi.org/10.1371/journal.pone.0175277 |
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author | Nguyen, Nhung T. H. Soto, Axel J. Kontonatsios, Georgios Batista-Navarro, Riza Ananiadou, Sophia |
author_facet | Nguyen, Nhung T. H. Soto, Axel J. Kontonatsios, Georgios Batista-Navarro, Riza Ananiadou, Sophia |
author_sort | Nguyen, Nhung T. H. |
collection | PubMed |
description | The increasing growth of literature in biodiversity presents challenges to users who need to discover pertinent information in an efficient and timely manner. In response, text mining techniques offer solutions by facilitating the automated discovery of knowledge from large textual data. An important step in text mining is the recognition of concepts via their linguistic realisation, i.e., terms. However, a given concept may be referred to in text using various synonyms or term variants, making search systems likely to overlook documents mentioning less known variants, which are albeit relevant to a query term. Domain-specific terminological resources, which include term variants, synonyms and related terms, are thus important in supporting semantic search over large textual archives. This article describes the use of text mining methods for the automatic construction of a large-scale biodiversity term inventory. The inventory consists of names of species, amongst which naming variations are prevalent. We apply a number of distributional semantic techniques on all of the titles in the Biodiversity Heritage Library, to compute semantic similarity between species names and support the automated construction of the resource. With the construction of our biodiversity term inventory, we demonstrate that distributional semantic models are able to identify semantically similar names that are not yet recorded in existing taxonomies. Such methods can thus be used to update existing taxonomies semi-automatically by deriving semantically related taxonomic names from a text corpus and allowing expert curators to validate them. We also evaluate our inventory as a means to improve search by facilitating automatic query expansion. Specifically, we developed a visual search interface that suggests semantically related species names, which are available in our inventory but not always in other repositories, to incorporate into the search query. An assessment of the interface by domain experts reveals that our query expansion based on related names is useful for increasing the number of relevant documents retrieved. Its exploitation can benefit both users and developers of search engines and text mining applications. |
format | Online Article Text |
id | pubmed-5393592 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-53935922017-05-04 Constructing a biodiversity terminological inventory Nguyen, Nhung T. H. Soto, Axel J. Kontonatsios, Georgios Batista-Navarro, Riza Ananiadou, Sophia PLoS One Research Article The increasing growth of literature in biodiversity presents challenges to users who need to discover pertinent information in an efficient and timely manner. In response, text mining techniques offer solutions by facilitating the automated discovery of knowledge from large textual data. An important step in text mining is the recognition of concepts via their linguistic realisation, i.e., terms. However, a given concept may be referred to in text using various synonyms or term variants, making search systems likely to overlook documents mentioning less known variants, which are albeit relevant to a query term. Domain-specific terminological resources, which include term variants, synonyms and related terms, are thus important in supporting semantic search over large textual archives. This article describes the use of text mining methods for the automatic construction of a large-scale biodiversity term inventory. The inventory consists of names of species, amongst which naming variations are prevalent. We apply a number of distributional semantic techniques on all of the titles in the Biodiversity Heritage Library, to compute semantic similarity between species names and support the automated construction of the resource. With the construction of our biodiversity term inventory, we demonstrate that distributional semantic models are able to identify semantically similar names that are not yet recorded in existing taxonomies. Such methods can thus be used to update existing taxonomies semi-automatically by deriving semantically related taxonomic names from a text corpus and allowing expert curators to validate them. We also evaluate our inventory as a means to improve search by facilitating automatic query expansion. Specifically, we developed a visual search interface that suggests semantically related species names, which are available in our inventory but not always in other repositories, to incorporate into the search query. An assessment of the interface by domain experts reveals that our query expansion based on related names is useful for increasing the number of relevant documents retrieved. Its exploitation can benefit both users and developers of search engines and text mining applications. Public Library of Science 2017-04-17 /pmc/articles/PMC5393592/ /pubmed/28414821 http://dx.doi.org/10.1371/journal.pone.0175277 Text en © 2017 Nguyen et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Nguyen, Nhung T. H. Soto, Axel J. Kontonatsios, Georgios Batista-Navarro, Riza Ananiadou, Sophia Constructing a biodiversity terminological inventory |
title | Constructing a biodiversity terminological inventory |
title_full | Constructing a biodiversity terminological inventory |
title_fullStr | Constructing a biodiversity terminological inventory |
title_full_unstemmed | Constructing a biodiversity terminological inventory |
title_short | Constructing a biodiversity terminological inventory |
title_sort | constructing a biodiversity terminological inventory |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5393592/ https://www.ncbi.nlm.nih.gov/pubmed/28414821 http://dx.doi.org/10.1371/journal.pone.0175277 |
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