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Passage-Based Bibliographic Coupling: An Inter-Article Similarity Measure for Biomedical Articles

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  • Rey-Long Liu

Abstract

Biomedical literature is an essential source of biomedical evidence. To translate the evidence for biomedicine study, researchers often need to carefully read multiple articles about specific biomedical issues. These articles thus need to be highly related to each other. They should share similar core contents, including research goals, methods, and findings. However, given an article r, it is challenging for search engines to retrieve highly related articles for r. In this paper, we present a technique PBC (Passage-based Bibliographic Coupling) that estimates inter-article similarity by seamlessly integrating bibliographic coupling with the information collected from context passages around important out-link citations (references) in each article. Empirical evaluation shows that PBC can significantly improve the retrieval of those articles that biomedical experts believe to be highly related to specific articles about gene-disease associations. PBC can thus be used to improve search engines in retrieving the highly related articles for any given article r, even when r is cited by very few (or even no) articles. The contribution is essential for those researchers and text mining systems that aim at cross-validating the evidence about specific gene-disease associations.

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  • Rey-Long Liu, 2015. "Passage-Based Bibliographic Coupling: An Inter-Article Similarity Measure for Biomedical Articles," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-22, October.
    • Handle: RePEc:plo:pone00:0139245
    DOI: 10.1371/journal.pone.0139245
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    References listed on IDEAS

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    7. Xinhai Liu & Shi Yu & Frizo Janssens & Wolfgang Glänzel & Yves Moreau & Bart De Moor, 2010. "Weighted hybrid clustering by combining text mining and bibliometrics on a large‐scale journal database," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(6), pages 1105-1119, June.
    8. Kevin W. Boyack & Richard Klavans, 2010. "Co‐citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
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    Cited by:

    1. Rey-Long Liu, 2017. "A new bibliographic coupling measure with descriptive capability," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(2), pages 915-935, February.
    2. Dejian Yu & Wanru Wang & Shuai Zhang & Wenyu Zhang & Rongyu Liu, 2017. "Hybrid self-optimized clustering model based on citation links and textual features to detect research topics," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-21, October.
    3. Rons, Nadine, 2018. "Bibliometric approximation of a scientific specialty by combining key sources, title words, authors and references," Journal of Informetrics, Elsevier, vol. 12(1), pages 113-132.

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