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Literature‐based discovery: Beyond the ABCs

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  • Neil R. Smalheiser

Abstract

Literature‐based discovery (LBD) refers to a particular type of text mining that seeks to identify nontrivial assertions that are implicit, and not explicitly stated, and that are detected by juxtaposing (generally a large body of) documents. In this review, I will provide a brief overview of LBD, both past and present, and will propose some new directions for the next decade. The prevalent ABC model is not “wrong”; however, it is only one of several different types of models that can contribute to the development of the next generation of LBD tools. Perhaps the most urgent need is to develop a series of objective literature‐based interestingness measures, which can customize the output of LBD systems for different types of scientific investigations.

Suggested Citation

  • Neil R. Smalheiser, 2012. "Literature‐based discovery: Beyond the ABCs," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(2), pages 218-224, February.
    • Handle: RePEc:bla:jamist:v:63:y:2012:i:2:p:218-224
    DOI: 10.1002/asi.21599
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    Cited by:

    1. Herman H H B M van Haagen & Peter A C 't Hoen & Barend Mons & Erik A Schultes, 2013. "Generic Information Can Retrieve Known Biological Associations: Implications for Biomedical Knowledge Discovery," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-9, November.
    2. 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.
    3. Agniv Adhikari & Paramita Das & Abhik Mukherjee, 2019. "Generating a representative keyword subset pertaining to an academic conference series," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 749-770, May.
    4. Yuya Kajikawa, 2022. "Reframing evidence in evidence-based policy making and role of bibliometrics: toward transdisciplinary scientometric research," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5571-5585, September.

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