Literature-based discovery: Beyond the ABCs

Authors:
Neil R. Smalheiser
Affiliations:
University of Illinois at Chicago, Psychiatric Institute MC912, 1601 W. Taylor Street, Chicago, IL 60612
Venue:
Journal of the American Society for Information Science and Technology
Year:
2012

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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. © 2012 Wiley Periodicals, Inc.