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Applications of Ontologies and Text Mining in the Biomedical Domain
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Author(s): A. Jimeno-Yepes (European Bioinformatic Institute, UK), R. Berlanga-Llavori (Universitat Jaume I, Spain)and D. Rebholz-Schuchmann (European Bioinformatic Institute, UK)
Copyright: 2010
Pages: 23
Source title:
Ontology Theory, Management and Design: Advanced Tools and Models
Source Author(s)/Editor(s): Faiez Gargouri (Higher Institute of Informatics and Multimedia of Sfax, Tunisia)and Wassim Jaziri (Higher Institute of Informatics and Multimedia of Sfax, Tunisia)
DOI: 10.4018/978-1-61520-859-3.ch012
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Abstract
Ontologies represent domain knowledge that improves user interaction and interoperability between applications. In addition, ontologies deliver precious input to text mining techniques in the biomedical domain, which might improve the performance in different text mining tasks. This chapter will explore on the mutual benefits for ontologies and text mining techniques. Ontology development is a time consuming task. Most efforts are spent in the acquisition of terms that represent concepts in real life. This process can use the existing scientific literature and the World Wide Web. The identification of concept labels, i.e. terms, from these sources using text mining solutions improves ontology development since the literature resources make reference to existing terms and concepts. Furthermore, automatic text processing techniques profit from ontological resources in different tasks, for example in the disambiguation of terms and the enrichment of terminological resources for the text mining solution. One of the most important text mining tasks that exploits ontological resources consists of the mapping of concepts to terms in textual sources (e.g. named entity recognition, semantic indexing) and the expansion of queries in information retrieval.
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