Using UMLS-based re-weighting terms as a query expansion strategy

Search engines have significantly improved the efficiency of bio-medical literature searching. These search engines, however, still return many results that are irrelevant to the intention of a user's query. To improve precision and recall, various query expansion strategies are widely used. In this paper, we explore the three widely used query expansion strategies local analysis, global analysis, and ontology-based term reweighting across various search engines. Through experiments, we show that ontology-based term re-weighting works best. Term re-weighting reformulates queries with selection of key original query terms and re-weights these key terms and their associated synonyms from UMLS. The results of experiments show that with LUCENE and LEMUR, the average precision is enhanced by up to 20.3% and 12.1%, respectively, compared to baseline runs. We believe the principles of this term re-weighting strategy may be extended and utilized in other bio-medical domains.