Knowledge Extraction and Semantic Annotation of Text from the Encyclopedia of Life
Identifieur interne : 000507 ( Ncbi/Merge ); précédent : 000506; suivant : 000508Knowledge Extraction and Semantic Annotation of Text from the Encyclopedia of Life
Auteurs : Anne E. Thessen [États-Unis] ; Cynthia Sims Parr [États-Unis]Source :
- PLoS ONE [ 1932-6203 ] ; 2014.
Abstract
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.
Url:
DOI: 10.1371/journal.pone.0089550
PubMed: 24594988
PubMed Central: 3940440
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<front><div type="abstract" xml:lang="en"><p>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.</p>
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<article-id pub-id-type="doi">10.1371/journal.pone.0089550</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject>
</subj-group>
<subj-group subj-group-type="Discipline-v2"><subject>Biology</subject>
<subj-group><subject>Computational biology</subject>
<subj-group><subject>Text mining</subject>
</subj-group>
</subj-group>
<subj-group><subject>Ecology</subject>
<subj-group><subject>Biodiversity</subject>
</subj-group>
</subj-group>
</subj-group>
<subj-group subj-group-type="Discipline-v2"><subject>Computer science</subject>
<subj-group><subject>Algorithms</subject>
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<subj-group><subject>Information technology</subject>
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<subj-group><subject>Text mining</subject>
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<subj-group subj-group-type="Discipline-v2"><subject>Social and behavioral sciences</subject>
<subj-group><subject>Communications</subject>
<subj-group><subject>Semantics</subject>
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<subj-group><subject>Information science</subject>
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<subj-group><subject>Linguistics</subject>
<subj-group><subject>Natural language</subject>
<subject>Semantics</subject>
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<title-group><article-title>Knowledge Extraction and Semantic Annotation of Text from the Encyclopedia of Life</article-title>
<alt-title alt-title-type="running-head">Information Extraction and Annotation of Text</alt-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Thessen</surname>
<given-names>Anne E.</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
<xref ref-type="corresp" rid="cor1"><sup>*</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Parr</surname>
<given-names>Cynthia Sims</given-names>
</name>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
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<aff id="aff1"><label>1</label>
<addr-line>Arizona State University, School of Life Sciences, Tempe, Arizona, United States of America</addr-line>
</aff>
<aff id="aff2"><label>2</label>
<addr-line>National Museum of Natural History, Smithsonian Institution, Washington, District of Columbia, United States of America</addr-line>
</aff>
<contrib-group><contrib contrib-type="editor"><name><surname>Rocha</surname>
<given-names>Luis M.</given-names>
</name>
<role>Editor</role>
<xref ref-type="aff" rid="edit1"></xref>
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<aff id="edit1"><addr-line>Indiana University, United States of America</addr-line>
</aff>
<author-notes><corresp id="cor1">* E-mail: <email>annethessen@gmail.com</email>
</corresp>
<fn fn-type="conflict"><p><bold>Competing Interests: </bold>
The authors have declared that no competing interests exist.</p>
</fn>
<fn fn-type="con"><p>Conceived and designed the experiments: AET. Performed the experiments: AET. Analyzed the data: AET. Contributed reagents/materials/analysis tools: AET CSP. Wrote the paper: AET CSP.</p>
</fn>
</author-notes>
<pub-date pub-type="collection"><year>2014</year>
</pub-date>
<pub-date pub-type="epub"><day>3</day>
<month>3</month>
<year>2014</year>
</pub-date>
<volume>9</volume>
<issue>3</issue>
<elocation-id>e89550</elocation-id>
<history><date date-type="received"><day>4</day>
<month>9</month>
<year>2013</year>
</date>
<date date-type="accepted"><day>21</day>
<month>1</month>
<year>2014</year>
</date>
</history>
<permissions><copyright-year>2014</copyright-year>
<license><license-p>This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.</license-p>
</license>
</permissions>
<abstract><p>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.</p>
</abstract>
<funding-group><funding-statement>Funding for CSP and AET came from the Encyclopedia of Life. Funding for the Encyclopedia of Life comes from the John D. and Catherine T. MacArthur Foundation, the Alfred P. Sloan Foundation, the participating institutions in the Encyclopedia of Life consortium, and individual donations from users around the world. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</funding-statement>
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<affiliations><list><country><li>États-Unis</li>
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