Conference proceeding
Using UMLS-based re-weighting terms as a query expansion strategy
2006 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING
01 Jan 2006
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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.
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Details
- Title
- Using UMLS-based re-weighting terms as a query expansion strategy
- Creators
- Weizhong Zhu - Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USAXuheng Xu - Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USAXiaohua Hu - Drexel UniversityIl-Yeol Song - Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USARobert B. Allen - Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA
- Contributors
- Y Q Zhang (Editor)T Y Lin (Editor)
- Publication Details
- 2006 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING
- Conference
- 2006 IEEE International Conference on Granular Computing
- Publisher
- IEEE
- Number of pages
- 2
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science (Informatics)
- Identifiers
- 991019170548304721
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- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Theory & Methods