MedEvi: retrieving textual evidence of relations between biomedical concepts from Medline.
other · Level V
Where this comes from
- Record sourced from PubMed, PMID 18400773.
- Also identified by DOI 10.1093/bioinformatics/btn117 and PMC identifier 2387223.
- Licence recorded as CC BY-NC.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
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. http://www.ebi.ac.uk/tc-test/textmining/medevi/
Medical subject headings
- Abstracting and Indexing
- Artificial Intelligence
- Database Management Systems
- Information Storage and Retrieval
- MEDLINE
- Natural Language Processing
- Vocabulary, Controlled