Literature-related discovery (LRD) is the linking of two or more literature concepts that have heretofore not been linked (i.e., disjoint), in order to produce novel, interesting, plausible, and intelligible knowledge (i.e., potential discovery). The open discovery systems (ODS) component of LRD starts with a problem to be solved, and generates solutions to that problem through potential discovery.
We have been using ODS LRD to identify potential treatments or preventative actions for challenging medical problems, among myriad other applications. Multiple Sclerosis (MS) is a progressive neurodegenerative disorder (typically preceded by periods of remission and relapse), affecting mainly people in their early-mid life
We selected the subject of MS because of its global prevalence, and its apparent intractability to all treatments except for palliative remediation mainly through drugs or surgery. Our first goal was to identify non-drug nonsurgical treatments that would 1) prevent the occurrence, or 2) reduce the progression rate, or 3) stop the progression, or 4) maybe even reverse the progression, of MS. Our second goal was to demonstrate that we could again solve an ODS problem (using LRD) with no prior knowledge of any results or prior work (unlike the case of the RP problem). We used the MeSH taxonomy of MEDLINE to restrict potential discoveries to selected semantic classes, and to identify potential discoveries efficiently. Our third goal was to generate large amounts of potential discovery in a short period of time. The discovery generation methodology has been developed to the point where ODS LRD problems can be solved with no results or knowledge of any prior work.
Full-text available at LBDiscv MS 2008
Authors: Ronald N. Kostoff, Michael B. Briggs, Terence J. Lyons
Organizations: Office of Naval Research, Air Force Office of Scientific Research,
Source: Technological Forecasting & Social Change