Text mining and ontologies in biomedicine: making sense of raw text.
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.
Spasic
Irena
I
School of Chemistry, The University of Manchester, Sackville Street, PO Box 88, Manchester M60 1QD, UK. i.spasic@manchester.ac.uk
Ananiadou
Sophia
S
McNaught
John
J
Kumar
Anand
A
D000043:Abstracting and Indexing / Q000379:methods; D001185:Artificial Intelligence; D001695:Biology; D001877:Books; D003628:Database Management Systems; D016206:Databases, Bibliographic; D004282:Documentation / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D008511:Medicine; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods; D010506:Periodicals as Topic; D009626:Terminology as Topic