Open AccessLiterature‐based discovery: Beyond the ABCs
Author(s) -
Smalheiser Neil R.
Publication year - 2012
Publication title -
journal of the american society for information science and technology
Language(s) - English
Resource type - Journals
eISSN - 1532-2890
pISSN - 1532-2882
DOI - 10.1002/asi.21599
Subject(s) - computer science , data science , scientific discovery , information retrieval , cognitive science , psychology
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.