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Related Experiment Video

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Multi-field query expansion is effective for biomedical dataset retrieval.

Mohamed Reda Bouadjenek1, Karin Verspoor1

  • 1School of Computing and Information Systems, The University of Melbourne, Parkville, VIC, 3010, Australia.

Database : the Journal of Biological Databases and Curation
|December 9, 2017
PubMed
Summary

This study introduces a query reformulation method for retrieving diverse biomedical datasets. The lexicon-based query expansion offers an efficient and accurate approach for large-scale biomedical data search engines.

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Area of Science:

  • Biomedical Informatics
  • Information Retrieval
  • Data Science

Background:

  • Biomedical datasets often have heterogeneous schemas, posing challenges for effective information retrieval.
  • Existing methods may struggle to efficiently search and retrieve relevant data from diverse biomedical sources.

Purpose of the Study:

  • To propose and evaluate a query reformulation method for retrieving biomedical datasets with heterogeneous schemas.
  • To compare the effectiveness of two query expansion strategies: Rocchio method and a biomedical lexicon.

Main Methods:

  • Developed a method to transform initial queries into multi-field queries enriched with relevant terms.
  • Implemented and compared two query expansion strategies: Rocchio and a biomedical lexicon-based approach.
  • Conducted a comprehensive evaluation on the bioCADDIE dataset collection.

Main Results:

  • The multi-field query method improved Mean Average Precision (MAP) from 0.2171/0.2669 to 0.2996.
  • Rocchio expansion improved MAP to 0.335, outperforming the lexicon method by approximately 3%.
  • The lexicon-based method demonstrated superior efficiency, requiring fewer computational resources.

Conclusions:

  • The lexicon-based query expansion offers a favorable trade-off between efficiency and accuracy for large-scale biomedical data search.
  • The proposed method shows competitive retrieval performance, ranking second in P@10 and NDCG in the bioCADDIE challenge.
  • Findings can inform the development and improvement of biomedical data discovery tools.