Literature‐related discovery

Abstract

IntroductionDiscovery in science is the generation of novel, interesting, plausible, and intelligible knowledge about the objects of study. Literature-related discovery (LRD) is the linking of two or more concepts that have heretofore not been linked (i.e., disjoint), in order to produce new knowledge (i.e., potential discovery). Two major variants of LRD are: open discovery systems (ODS), where one starts with a problem and generates a potential solution (or vice versa), and closed discovery systems (CDS), where one starts with a problem and a potential solution and generates linking mechanism(s).This chapter reviews the state-of-the-art in ODS LRD only. It examines the major LRD concepts, evaluates each concept in detail from the perspective of discovery capability, and examines the level of potential discovery reported in the literature from each concept's implementation. In the evaluation of potential discovery claimed in the published literature, a vetting process is used that requires both characteristics of ODS LRD to be present in order for potential discovery to be affirmed: concepts are linked that have not been linked previously and novel, interesting, plausible, and intelligible knowledge is produced.The major conclusions are that, until recently, most of the reported ODS LRD techniques 5-3 have not generated discovery and this lack of discovery has hampered the growth of ODS LRD substantially. However, ODS LRD techniques have been developed that allow significantly greater amounts of potential discovery to be generated systematically.Discovery is ascertaining something previously unknown or unrecognized. More formally, "Discovery in science is the generation of novel, interesting, plausible, and intelligible knowledge about the objects of study" (Valdes-Perez, 1999, p. 336). It can result from uncovering previously unknown information, from synthesis of publicly available knowledge whose independent segments have never been combined, and/or through invention. In turn, the discovery could derive from logical exploitation of a knowledge base and/or from spontaneous creativity (e.g., Edisonian discoveries from trial and error) (Kostoff, 2003). Innovation reflects the metamorphosis from present practice to some new, ideally "better" practice. It can be based on existing non-implemented knowledge. It can follow discovery directly or resuscitate dormant discovery that has languished for decades.Literature-related discovery (LRD) is a systematic approach to bridging unconnected disciplines based on text mining procedures. LRD allows potentially radical discovery to be hypothesized using either the technical literature alone, or the literature and its authors.In the LRD context, discovery is linking two or more literature concepts that have heretofore not been linked (i.e., disjoint), in order to produce novel, interesting, plausible, and intelligible knowledge. Thus, simply linking two or more disparate concepts is a necessary, but not sufficient, condition for LRD. In particular, concepts may be disjoint b...

Text mining

“…In contrast to the web mining chapter that explores a range of data types, this chapter considers text exclusively and includes techniques developed in the information science and computational linguistics communities. Similarly, this chapter relates closely to Kostoff and his colleagues' review of literature‐based discovery (Kostoff et al, 2007) and their subsequent ARIST chapter titled “Literature‐related Discovery” (Kostoff, Block, Solka, Briggs, Rushenberg, Stump, et al, 2009), but it is wider in scope than LBD alone.…”

Rediscovering Don Swanson:The Past, Present and Future of Literature-based Discovery

“…Don’s first inclination was to filter out B-terms that did not have adequate frequency of mentions in each literature, implying that he was focusing on the cores (Swanson & Smalheiser, 1997). In contrast, Kostoff et al (2009), Petrič et al (2010), and Workman et al (2016) have argued that low-frequency terms which reside in the penumbra of one or both fields may sometimes be more promising for finding links that are interesting and unexpected.…”

Literature‐based discovery: Beyond the ABCs

“…Reelin has been shown to bind to certain proteins, and an LBD analysis identified other proteins (that share certain features with the known set) as promising reelin‐binding proteins (Homayouni, Heinrich, Wei, & Berry, 2005). By their very nature, similarity algorithms will find only incremental discoveries—those that are similar to what in machine learning is called “the training set” (see also Kostoff et al, 2009).…”

“…The goal of LBD is really to generate or assess new hypotheses that might represent potential scientific discoveries and, hence, are worthy of follow‐up in the laboratory or clinic. The term LBD can be ambiguous or misleading (Kostoff, 2007; Kostoff et al, 2009) and Bekhuis (2006) has proposed that it should be replaced with some alternative term such as “exploratory mining.” “Discovery” has many different meanings in different contexts and at different stages in the cycle of scientific discovery (Grinnell, 2009). An LBD system might be very useful when it discovers things that are novel to the investigator doing the search, even if it is well known to other experts or even to the scientific community at large.…”