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Constructing an associative concept space for literature‐based discovery

Author

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  • C. Christiaan van der Eijk
  • Erik M. van Mulligen
  • Jan A. Kors
  • Barend Mons
  • Jan van den Berg

Abstract

Scientific literature is often fragmented, which implies that certain scientific questions can only be answered by combining information from various articles. In this paper, a new algorithm is proposed for finding associations between related concepts present in literature. To this end, concepts are mapped to a multidimensional space by a Hebbian type of learning algorithm using co‐occurrence data as input. The resulting concept space allows exploration of the neighborhood of a concept and finding potentially novel relationships between concepts. The obtained information retrieval system is useful for finding literature supporting hypotheses and for discovering previously unknown relationships between concepts. Tests on artificial data show the potential of the proposed methodology. In addition, preliminary tests on a set of Medline abstracts yield promising results.

Suggested Citation

  • C. Christiaan van der Eijk & Erik M. van Mulligen & Jan A. Kors & Barend Mons & Jan van den Berg, 2004. "Constructing an associative concept space for literature‐based discovery," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 55(5), pages 436-444, March.
    • Handle: RePEc:bla:jamist:v:55:y:2004:i:5:p:436-444
    DOI: 10.1002/asi.10392
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    Cited by:

    1. Andrej Kastrin & Dimitar Hristovski, 2021. "Scientometric analysis and knowledge mapping of literature-based discovery (1986–2020)," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1415-1451, February.

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