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Cited by 18Information Retrieval and Search BehaviorBiomedical Text Mining and OntologiesRecommender Systems and Techniques

The DOI matches its Crossref record; 22 of the 26 references it cites were found in a registry or an index.

Improving accuracy for identifying related PubMed queries by an integrated approach

Open access
Lu, Zhiyong, Wilbur, W. John·2009·Journal of Biomedical Informatics
DOI 10.1016/j.jbi.2008.12.006PMC PMC2764279
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Abstract

PubMed is the most widely used tool for searching biomedical literature online. As with many other online search tools, a user often types a series of multiple related queries before retrieving satisfactory results to fulfill a single information need. Meanwhile, it is also a common phenomenon to see a user type queries on unrelated topics in a single session. In order to study PubMed users' search strategies, it is necessary to be able to automatically separate unrelated queries and group together related queries. Here, we report a novel approach combining both lexical and contextual analyses for segmenting PubMed query sessions and identifying related queries and compare its performance with the previous approach based solely on concept mapping. We experimented with our integrated approach on sample data consisting of 1539 pairs of consecutive user queries in 351 user sessions. The prediction results of 1396 pairs agreed with the gold-standard annotations, achieving an overall accuracy of 90.7%. This demonstrates that our approach is significantly better than the previously published method. By applying this approach to a one day query log of PubMed, we found that a significant proportion of information needs involved more than one PubMed query, and that most of the consecutive queries for the same information need are lexically related. Finally, the proposed PubMed distance is shown to be an accurate and meaningful measure for determining the contextual similarity between biological terms. The integrated approach can play a critical role in handling real-world PubMed query log data as is demonstrated in our experiments.

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  • Term weighting was shown to be a more effective strategy compared with term proximity.

    “Although term weighting was shown to be a more effective strategy compared with term proximity, certain aspects of term proximity seem useful for MEDLINE retrieval.”

    From Understanding PubMed(R) user search behavior through log analysis (2009) · cites this paper as [80]

    The Methods and Discussion sections compare lexical and contextual analyses for identifying related PubMed queries, not term weighting and term proximity for MEDLINE retrieval. Searches for “term weighting,” “term proximity,” and their retrieval context found no passage addressing the claimed comparison; page numbers were not available in our copy. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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4 of the 26 references couldn’t be matched to a known paper.

Research works printed but not linked: 2

  1. He D, Göker A. Detecting session boundaries from web user logs. In: Proceedings of the BCS-IRSG 22nd annual colloquium on information retrieval research, 2000.
  2. Hersh W, Bhupatiraju RT. TREC genomics track overview. In: Proceedings of the twelfth text retrieval conference (TREC 2003), 2003. · DOI

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Metadata

Authors
Lu, Z., & Wilbur, W. J.
Affiliations
  • National Center for Biotechnology Information (US)
  • United States National Library of Medicine (US)
Corresponding author
Lu, Zhiyong
ORCID
Lu, Zhiyong
Venue
Journal of Biomedical Informatics
Publisher
Elsevier BV
Published
2009-10
Volume
42
Issue
5
Pages
831-838
ISSN
1532-0464 (ISSN-L)
DOI
10.1016/j.jbi.2008.12.006
PMC
PMC2764279
OpenAlex
W2138067727
Cited by
18
References
26
Citations by year
2026: 1 · 2024: 1 · 2022: 1 · 2015: 3 · 2013: 4 · 2012: 1
FWCI
7.22
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Biomedical Informatics (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
National Institutes of Health (Z99 LM999999)
Topics
  • Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
  • Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
  • Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
Keywords
PubMed, biomedical literature retrieval, lexical analysis, contextual analysis, concept mapping, query logs, information needs
MeSH terms
Medical Informatics, Terminology as Topic, Information Storage and Retrieval, PubMed

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