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Knowledge Extraction and Semantic Annotation of Text from the Encyclopedia of Life

Numerous digitization and ontological initiatives have focused on translating biological knowledge from narrative text to machine-readable formats. In this paper, we describe two workflows for knowledge extraction and semantic annotation of text data objects featured in an online biodiversity aggreg...

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Detalles Bibliográficos
Autores principales: Thessen, Anne E., Parr, Cynthia Sims
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3940440/
https://www.ncbi.nlm.nih.gov/pubmed/24594988
http://dx.doi.org/10.1371/journal.pone.0089550
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author Thessen, Anne E.
Parr, Cynthia Sims
author_facet Thessen, Anne E.
Parr, Cynthia Sims
author_sort Thessen, Anne E.
collection PubMed
description Numerous digitization and ontological initiatives have focused on translating biological knowledge from narrative text to machine-readable formats. In this paper, we describe two workflows for knowledge extraction and semantic annotation of text data objects featured in an online biodiversity aggregator, the Encyclopedia of Life. One workflow tags text with DBpedia URIs based on keywords. Another workflow finds taxon names in text using GNRD for the purpose of building a species association network. Both workflows work well: the annotation workflow has an F1 Score of 0.941 and the association algorithm has an F1 Score of 0.885. Existing text annotators such as Terminizer and DBpedia Spotlight performed well, but require some optimization to be useful in the ecology and evolution domain. Important future work includes scaling up and improving accuracy through the use of distributional semantics.
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spelling pubmed-39404402014-03-06 Knowledge Extraction and Semantic Annotation of Text from the Encyclopedia of Life Thessen, Anne E. Parr, Cynthia Sims PLoS One Research Article Numerous digitization and ontological initiatives have focused on translating biological knowledge from narrative text to machine-readable formats. In this paper, we describe two workflows for knowledge extraction and semantic annotation of text data objects featured in an online biodiversity aggregator, the Encyclopedia of Life. One workflow tags text with DBpedia URIs based on keywords. Another workflow finds taxon names in text using GNRD for the purpose of building a species association network. Both workflows work well: the annotation workflow has an F1 Score of 0.941 and the association algorithm has an F1 Score of 0.885. Existing text annotators such as Terminizer and DBpedia Spotlight performed well, but require some optimization to be useful in the ecology and evolution domain. Important future work includes scaling up and improving accuracy through the use of distributional semantics. Public Library of Science 2014-03-03 /pmc/articles/PMC3940440/ /pubmed/24594988 http://dx.doi.org/10.1371/journal.pone.0089550 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
spellingShingle Research Article
Thessen, Anne E.
Parr, Cynthia Sims
Knowledge Extraction and Semantic Annotation of Text from the Encyclopedia of Life
title Knowledge Extraction and Semantic Annotation of Text from the Encyclopedia of Life
title_full Knowledge Extraction and Semantic Annotation of Text from the Encyclopedia of Life
title_fullStr Knowledge Extraction and Semantic Annotation of Text from the Encyclopedia of Life
title_full_unstemmed Knowledge Extraction and Semantic Annotation of Text from the Encyclopedia of Life
title_short Knowledge Extraction and Semantic Annotation of Text from the Encyclopedia of Life
title_sort knowledge extraction and semantic annotation of text from the encyclopedia of life
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3940440/
https://www.ncbi.nlm.nih.gov/pubmed/24594988
http://dx.doi.org/10.1371/journal.pone.0089550
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