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Text mining and ontologies in biomedicine: making sense of raw text Spasic I; Ananiadou S; McNaught J; Kumar ABrief Bioinform 2005[Sep]; 6 (3): 239-51The 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.|*Artificial Intelligence[MESH]|*Database Management Systems[MESH]|*Databases, Bibliographic[MESH]|*Natural Language Processing[MESH]|*Periodicals as Topic[MESH]|Abstracting and Indexing/methods[MESH]|Biology[MESH]|Books[MESH]|Documentation/*methods[MESH]|Information Storage and Retrieval/*methods[MESH]|Medicine[MESH]|Pattern Recognition, Automated/methods[MESH]|Terminology as Topic[MESH] |
