IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Lexical Enrichment of Biomedical Ontologies

Lexical Enrichment of Biomedical Ontologies
View Sample PDF
Author(s): Nils Reiter (Heidelberg University, Germany)and Paul Buitelaar (National University of Ireland Galway, UK)
Copyright: 2009
Pages: 18
Source title: Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration
Source Author(s)/Editor(s): Violaine Prince (University Montpellier 2, France)and Mathieu Roche (University Montpellier 2, France)
DOI: 10.4018/978-1-60566-274-9.ch007

Purchase

View Lexical Enrichment of Biomedical Ontologies on the publisher's website for pricing and purchasing information.

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.

Related Content

Linkon Chowdhury, Md Sarwar Kamal, Shamim H. Ripon, Sazia Parvin, Omar Khadeer Hussain, Amira Ashour, Bristy Roy Chowdhury. © 2024. 20 pages.
Mousomi Roy. © 2024. 21 pages.
Nassima Dif, Zakaria Elberrichi. © 2024. 20 pages.
Pyingkodi Maran, Shanthi S., Thenmozhi K., Hemalatha D., Nanthini K.. © 2024. 16 pages.
Mohamed Nadjib Boufenara, Mahmoud Boufaida, Mohamed Lamine Berkane. © 2024. 16 pages.
Meroua Daoudi, Souham Meshoul, Samia Boucherkha. © 2024. 25 pages.
Zhongyu Lu, Qiang Xu, Murad Al-Rajab, Lamogha Chiazor. © 2024. 56 pages.
Body Bottom