Text mining and ontologies in biomedicine: Making sense of raw text
Postdoctoral research associate in the School of Chemistry and the Manchester Interdisciplinary Biocentre at the University of Manchester. Her research interests include biomedical text mining, machine learning and bioinformatics.
Co-director of the UK National Centre for Text Mining and a Reader in Computer Science at the University of Salford. Her research interests are in the areas of computational terminology and biomedical text mining.
Lecturer in the School of Informatics at the University of Manchester and an Associate Director of the UK National Centre for Text Mining. His research interests include information extraction and computational lexicography.
Alexander von Humboldt research fellow in the Faculty of Medicine at the University of Leipzig and a member of the Institute for Formal Ontology and Medical Information Science at Saarland University in Saarbrücken. His research interests include medical and biomedical knowledge representation, data models and ontologies
Irena Spasic, School of Chemistry, The University of Manchester, Sackville Street, PO Box 88, Manchester M60 1QD,UK Tel: +44 (0)161 306 4414 Fax: +44 (0)161 306 4556 E-mail: i.spasic{at}manchester.ac.uk
The volume of biomedical literature is increasing at such a rate that it is becoming difficult to locate, retrieve and manage the reported information without text mining, which aims to automatically distill information, extract facts, discover implicit links and generate hypotheses relevant to user needs. Ontologies, as conceptual models, provide the necessary framework for semantic representation of textual information. The principal link between text and an ontology is terminology, which maps terms to domain-specific concepts. This paper summarises different approaches in which ontologies have been used for text-mining applications in biomedicine.
Keywords: text mining, ontology, terminology, information extraction, information retrieval
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