title: MedEvi: retrieving textual evidence of relations between biomedical concepts from Medline.
abstract: Search engines running on MEDLINE abstracts have been widely used by biologists to find publications that are related to their research. The existing search engines such as PubMed, however, have limitations when applied for the task of seeking textual evidence of relations between given concepts. The limitations are mainly due to the problem that the search engines do not effectively deal with multi-term queries which may imply semantic relations between the terms. To address this problem, we present MedEvi, a novel search engine that imposes positional restriction on occurrences matching multi-term queries, based on the observation that terms with semantic relations which are explicitly stated in text are not found too far from each other. MedEvi further identifies additional keywords of biological and statistical significance from local context of matching occurrences in order to help users reformulate their queries for better results.
  
  
  BACKGROUND
  http://www.ebi.ac.uk/tc-test/textmining/medevi/
authors.affiliation: EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB101SD, UK. kim@ebi.ac.uk
authors.forename: Jung-Jae
authors.initials: JJ
authors.lastname: Kim
authors.forename: Piotr
authors.initials: P
authors.lastname: Pezik
authors.forename: Dietrich
authors.initials: D
authors.lastname: Rebholz-Schuhmann
mesh_terms: D000043:Abstracting and Indexing / Q000379:methods*; D001185:Artificial Intelligence; D003628:Database Management Systems; D016247:Information Storage and Retrieval / Q000379:methods*; D016239:MEDLINE; D009323:Natural Language Processing; D018875:Vocabulary, Controlled