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Evaluation of Query Expansion Using MeSH in PubMed
Zhiyong Lu1, Won Kim, W John Wilbur
1National Center for Biotechnology Information, NCBI, National Library of Medicine, Bethesda, MD, 20894 USA,
Information Retrieval
|September 24, 2009
Summary
This study shows that using Medical Subject Headings (MeSH) for automatic query expansion in PubMed generally improves search results. However, these improvements may not be noticeable for everyday PubMed users.
Area of Science:
- Medical Informatics
- Information Retrieval
- Biomedical Literature Search
Background:
- PubMed is a primary resource for biomedical literature.
- Efficiently searching MEDLINE citations is crucial for researchers.
- Automatic Term Mapping (ATM) is a PubMed feature for query expansion.
Purpose of the Study:
- To evaluate the effectiveness of Medical Subject Headings (MeSH) in PubMed's Automatic Term Mapping (ATM) for query expansion.
- To compare retrieval performance using MeSH-expanded queries versus queries based solely on text words.
Main Methods:
- Boolean searches were conducted on 64 topics and approximately 160,000 MEDLINE citations from TREC Genomics Tracks (2006-2007).
- Queries were automatically constructed and expanded using ATM with [MeSH Terms], [Text Words], and [All Fields] tags.
- Retrieval performance was assessed using F-measure and mean rank precision.
Main Results:
- Query expansion using MeSH generally enhanced retrieval performance.
- The study characterized two mechanisms of MeSH field utilization in MEDLINE citations.
- Performance gains were observed when comparing MeSH-expanded queries to text-word-only queries.
Conclusions:
- Automatic query expansion with MeSH in PubMed can improve information retrieval.
- The practical impact of these improvements on end-users in real-world scenarios may be limited.
- Further investigation into user-centric retrieval effectiveness is warranted.
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