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Lexical Enrichment of Biomedical Ontologies

Lexical Enrichment of Biomedical Ontologies

Nils Reiter, Paul Buitelaar
Copyright: © 2009 |Pages: 18
ISBN13: 9781605662749|ISBN10: 1605662747|ISBN13 Softcover: 9781616925284|EISBN13: 9781605662756
DOI: 10.4018/978-1-60566-274-9.ch007
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MLA

Reiter, Nils, and Paul Buitelaar. "Lexical Enrichment of Biomedical Ontologies." Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration, edited by Violaine Prince and Mathieu Roche, IGI Global, 2009, pp. 124-141. https://doi.org/10.4018/978-1-60566-274-9.ch007

APA

Reiter, N. & Buitelaar, P. (2009). Lexical Enrichment of Biomedical Ontologies. In V. Prince & M. Roche (Eds.), Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration (pp. 124-141). IGI Global. https://doi.org/10.4018/978-1-60566-274-9.ch007

Chicago

Reiter, Nils, and Paul Buitelaar. "Lexical Enrichment of Biomedical Ontologies." In Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration, edited by Violaine Prince and Mathieu Roche, 124-141. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-274-9.ch007

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Abstract

This chapter is concerned with lexical enrichment of ontologies, that is how to enrich a given ontology with lexical information derived from a semantic lexicon such as WordNet or other lexical resources. The authors present an approach towards the integration of both types of resources, in particular for the human anatomy domain as represented by the Foundational Model of Anatomy and for the molecular biology domain as represented by an ontology of biochemical substances. The chapter describes our approach on enriching these biomedical ontologies with information derived from WordNet and Wikipedia by matching ontology class labels to entries in WordNet and Wikipedia. In the first case the authors acquire WordNet synonyms for the ontology class label, whereas in the second case they acquire multilingual translations as provided by Wikipedia. A particular point of emphasis here is on selecting the appropriate interpretation of ambiguous ontology class labels through sense disambiguation, which we address by use of a simple algorithm that selects the most likely sense for an ambiguous term by statistical signi?cance of co-occurring words in a domain corpus. Acquired synonyms and translations are added to the ontology by use of the LingInfo model, which provides an ontology-based lexicon model for the annotation of ontology classes with (multilingual) terms and their linguistic properties.

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