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Literature-related discovery (LRD): Methodology
Authors:Ronald N Kostoff [Author Vitae]  Michael B Briggs [Author Vitae] [Author Vitae]  Robert L Rushenberg [Author Vitae]
Institution:a Office of Naval Research, 875 N. Randolph St., Arlington, VA 22217, USA
b Arlington, VA 22204, USA
c Naval Surface Weapons Center Dahlgren Division, Dahlgren, VA 22448-5100, USA
d DDL-OMNI Engineering, LLC, 8260 Greensboro Drive, McLean, VA 22201, USA
Abstract:Literature-related discovery (LRD) 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. LRD has two components: Literature-based discovery (LBD) generates potential discovery through literature analysis alone, whereas literature-assisted discovery (LAD) generates potential discovery through a combination of literature analysis and interactions among selected literature authors. In turn, there are two types of LBD and LAD: open discovery systems (ODS), where one starts with a problem and arrives at a solution, and closed discovery systems (CDS), where one starts with a problem and a solution, then determines the mechanism(s) that links them.The generic methodology for identifying potential discovery candidates through ODS LRD, focusing mainly on its ODS LBD component, is described in this paper. A comprehensive flow chart showing the details of our systematic potential discovery generation process, including the evolution of the flow chart steps through each of the studies performed, is presented. Also shown is a vetting procedure that insures potential discoveries claimed are potential discoveries realized. The semantic filters that replace the numerical filters of other ODS LBD approaches are overviewed. The rationale for addressing the five topics studied (Raynaud's Phenomenon (RP), Cataracts, Parkinson's Disease (PD), Multiple Sclerosis (MS), and Water Purification (WP)) is summarized.
Keywords:Literature-Based Discovery  Text Mining  Information Retrieval  Clustering  Semantic Filters
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