Knowledge-intensive conceptual retrieval and passage extraction of biomedical literature
Author(s) -
Wei Zhou,
Clement Yu,
Neil R. Smalheiser,
Vetle I. Torvik,
Jie Hong
Publication year - 2007
Publication title -
citeseer x (the pennsylvania state university)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.1145/1277741.1277853
Subject(s) - computer science , information retrieval , domain (mathematical analysis) , domain knowledge , information extraction , knowledge extraction , knowledge retrieval , data science , artificial intelligence , data retrieval , mathematical analysis , mathematics
This paper presents a study of incorporating domain-specific knowledge (i.e., information about concepts and relationships between concepts in a certain domain) in an information retrieval (IR) system to improve its effectiveness in retrieving biomedical literature. The effects of different types of domain-specific knowledge in performance contribution are examined. Based on the TREC platform, we show that appropriate use of domain-specific knowledge in a proposed conceptual retrieval model yields about 23% improvement over the best reported result in passage retrieval in the Genomics Track of TREC 2006.
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