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Hypothesis generation guided by co-word clustering

Author

Listed:
  • Johannes Stegmann

    (Free University Berlin, Medical Library University Hospital Benjamin Franklin)

  • Guenter Grohmann

    (University Hospital Free University Berlin)

Abstract

Co-word analysis was applied to keywords assigned to MEDLINE documents contained in sets of complementary but disjoint literatures. In strategical diagrams of disjoint literatures, based on internal density and external centrality of keyword-containing clusters, intermediate terms (linking the disjoint partners) were found in regions of below-median centrality and density. Terms representing the disjoint literature themes were found in close vicinity in strategical diagrams of intermediate literatures. Based on centrality-density ratios, characteristic values were found which allow a rapid identification of clusters containing possible intermediate and disjoint partner terms. Applied to the already investigated disjoint pairs Raynaud"s Disease - Fish Oil, Migraine - Magnesium, the method readily detected known and unknown (but relevant) intermediate and disjoint partner terms. Application of the method to the literature on Prions led to Manganese as possible disjoint partner term. It is concluded that co-word clustering is a powerful method for literature-based hypothesis generation and knowledge discovery.

Suggested Citation

  • Johannes Stegmann & Guenter Grohmann, 2003. "Hypothesis generation guided by co-word clustering," Scientometrics, Springer;Akadémiai Kiadó, vol. 56(1), pages 111-135, January.
    • Handle: RePEc:spr:scient:v:56:y:2003:i:1:d:10.1023_a:1021954808804
    DOI: 10.1023/A:1021954808804
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    References listed on IDEAS

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    1. Michael D. Gordon & Susan Dumais, 1998. "Using latent semantic indexing for literature based discovery," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 49(8), pages 674-685.
    2. Neal Coulter & Ira Monarch & Suresh Konda, 1998. "Software engineering as seen through its research literature: A study in co‐word analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 49(13), pages 1206-1223.
    3. Michael D. Gordon & Robert K. Lindsay, 1996. "Toward discovery support systems: A replication, re‐examination, and extension of Swanson's work on literature‐based discovery of a connection between Raynaud's and fish oil," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 47(2), pages 116-128, February.
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    Cited by:

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    3. Lv, Yanhua & Ding, Ying & Song, Min & Duan, Zhiguang, 2018. "Topology-driven trend analysis for drug discovery," Journal of Informetrics, Elsevier, vol. 12(3), pages 893-905.
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    5. Daniele Rotolo & Loet Leydesdorff, 2014. "Matching MEDLINE/PubMed Data with Web of Science (WOS): A Routine in R language," SPRU Working Paper Series 2014-14, SPRU - Science Policy Research Unit, University of Sussex Business School.
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    10. Leydesdorff, Loet & Welbers, Kasper, 2011. "The semantic mapping of words and co-words in contexts," Journal of Informetrics, Elsevier, vol. 5(3), pages 469-475.

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