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Learning to Refine Expansion Terms for Biomedical Information Retrieval Using Semantic Resources.

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    This study introduces a new framework for biomedical query expansion using learning-to-rank methods. The approach effectively identifies relevant terms to improve information retrieval for biologists.

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    Area of Science:

    • Biomedical Informatics
    • Information Retrieval
    • Computational Biology

    Background:

    • The rapid growth of biomedical literature poses challenges for researchers staying current.
    • Existing information retrieval methods struggle with domain-specific terminology in biomedical queries.
    • Query expansion techniques require refinement for effective biomedical information retrieval.

    Purpose of the Study:

    • To develop an improved biomedical query expansion framework.
    • To enhance the relevance of retrieved biomedical literature for researchers.
    • To address the challenge of domain-specific terms in biomedical information retrieval.

    Main Methods:

    • Proposed a biomedical query expansion framework utilizing learning-to-rank methods.
    • Implemented a pseudo-relevance feedback method based on Medical Subject Headings (MeSH) to select candidate terms.
    • Represented candidate terms using feature vectors incorporating corpus-based and resource-based features.

    Main Results:

    • The proposed method successfully identified more relevant terms for query expansion.
    • Demonstrated significant improvements in biomedical information retrieval performance.
    • Experimental results on TREC genomics datasets validated the framework's effectiveness.

    Conclusions:

    • The learning-to-rank based query expansion framework effectively refines term selection for biomedical information retrieval.
    • This approach enhances the ability of biologists to access the latest research.
    • The method offers a promising solution for navigating the expanding biomedical literature.