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

Spasic, Irena ORCID: https://orcid.org/0000-0002-8132-3885, Ananiadou, Sophia, McNaught, John and Kumar, Anand 2005. Text mining and ontologies in biomedicine: making sense of raw text. Briefings in Bioinformatics 6 (3) , pp. 239-251. 10.1093/bib/6.3.239

Full text not available from this repository.

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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QH Natural history > QH301 Biology
Uncontrolled Keywords:	owg; spasic; biomedical text mining;
Publisher:	Oxford University Press
ISSN:	1477-4054
Last Modified:	17 Oct 2022 09:56
URI:	https://orca.cardiff.ac.uk/id/eprint/6220

Citation Data

Cited 231 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item