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Extending PubMed Related Article (PMRA) for Multiple Citations

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Advances in Data Mining. Applications and Theoretical Aspects (ICDM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8557))

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Abstract

PubMed is the most comprehensive citation database in the field of biomedicine. It contains over 23 million citations from MEDLINE, life science journals and books. However, retrieving relevant information from PubMed is challenging due to its size and rapid growth. Keyword based information retrieval is not adequate in PubMed. Many tools have been developed to enhance the quality of information retrieval from PubMed. PubMed Related Article (PMRA) feature is one approach developed to help the users retrieve information efficiently. It finds highly related citations to a given citation. This study focuses on extending the PMRA feature to multiple citations in the context of personalized information retrieval. Our experimental results show that the extended PMRA feature using the words appearing in two or more citations is able to find more relevant articles than using the PMRA feature on individual PubMed citations.

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Author information

Authors and Affiliations

  1. Center for Computational Sciences, MTSU, Murfreesboro, TN, USA

    Sachintha Pitigala

  2. Department of Computer Science, MTSU, Murfreesboro, TN, USA

    Cen Li

Authors
  1. Sachintha Pitigala
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  2. Cen Li
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Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, IBaI, Leipzig, Germany

    Petra Perner

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© 2014 Springer International Publishing Switzerland

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Pitigala, S., Li, C. (2014). Extending PubMed Related Article (PMRA) for Multiple Citations. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2014. Lecture Notes in Computer Science(), vol 8557. Springer, Cham. https://doi.org/10.1007/978-3-319-08976-8_5

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  • DOI: https://doi.org/10.1007/978-3-319-08976-8_5

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08975-1

  • Online ISBN: 978-3-319-08976-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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