Text mining and ontologies in biomedicine: making sense of raw text.

by: I Spasic, S Ananiadou, J McNaught, A Kumar

Brief Bioinform, Vol. 6, No. 3. (September 2005), pp. 239-251.

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Abstract

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.

@article{citeulike:405825, abstract = {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.}, address = {School of Chemistry, The University of Manchester, Sackville Street, PO Box 88, Manchester M60 1QD, UK. i.spasic@manchester.ac.uk}, author = {Spasic, I. and Ananiadou, S. and McNaught, J. and Kumar, A. }, citeulike-article-id = {405825}, doi = {http://dx.doi.org/10.1093/bib/6.3.239}, issn = {1467-5463}, journal = {Brief Bioinform}, keywords = {biomedical, ontology, text\_mining}, month = {September}, number = {3}, pages = {239--251}, posted-at = {2006-07-27 15:27:01}, priority = {3}, title = {Text mining and ontologies in biomedicine: making sense of raw text.}, url = {http://dx.doi.org/10.1093/bib/6.3.239}, volume = {6}, year = {2005} }

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TY - JOUR ID - citeulike:405825 L3 - citeulike-article-id:405825 N2 - 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. IS - 3 JF - Brief Bioinform SN - 1467-5463 AD - School of Chemistry, The University of Manchester, Sackville Street, PO Box 88, Manchester M60 1QD, UK. i.spasic@manchester.ac.uk EP - 251 TI - Text mining and ontologies in biomedicine: making sense of raw text. VL - 6 SP - 239 KW - biomedical KW - ontology KW - text_mining AU - Spasic, I AU - Ananiadou, S AU - McNaught, J AU - Kumar, A PY - 2005/09// UR - http://dx.doi.org/10.1093/bib/6.3.239 ER -

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