Literature-based discovery

An example diagram of Swanson linking, usinc the ABC paradigm

Literature-based discovery (LBD), also called literature-related discovery (LRD) is a form of knowledge extraction and automated hypothesis generation that uses papers and other academic publications (the "literature") to find new relationships between existing knowledge (the "discovery"). Literature-based discovery aims to discover new knowledge by connecting information which have been explicitly stated in literature to deduce connections which have not been explicitly stated.[1]

LBD can help researchers to quickly discover and explore hypotheses as well as gain information on relevant advances inside and outside of their niches and increase interdisciplinary information sharing.[1]

The most basic and widespread type of LBD is called the ABC paradigm because it centers around three concepts called A, B and C.[2][3][4] It states that if there is a connection between A and B and one between B and C, then there is one between A and C which, if not explicitly stated, is yet to be explored.[1]

  1. ^ a b c Crichton, Gamal; Baker, Simon; Guo, Yufan; Korhonen, Anna (2020-05-15). "Neural networks for open and closed Literature-based Discovery". PLOS ONE. 15 (5): e0232891. Bibcode:2020PLoSO..1532891C. doi:10.1371/JOURNAL.PONE.0232891. PMC 7228051. PMID 32413059.  This article incorporates text available under the CC BY 4.0 license.
  2. ^ Smalheiser, Neil R; Swanson, Don R (November 1998). "Using Arrowsmith: a computer-assisted approach to formulating and assessing scientific hypotheses". Computer Methods and Programs in Biomedicine. 57 (3): 149–153. doi:10.1016/s0169-2607(98)00033-9. ISSN 0169-2607. PMID 9822851.
  3. ^ Gordon, Michael D.; Lindsay, Robert K. (February 1996). "Toward discovery support systems: A replication, re-examination, and extension of Swanson's work on literature-based discovery of a connection between Raynaud's and fish oil". Journal of the American Society for Information Science. 47 (2): 116–128. doi:10.1002/(sici)1097-4571(199602)47:2<116::aid-asi3>3.0.co;2-1. ISSN 0002-8231.
  4. ^ Cohen, Trevor; Schvaneveldt, Roger; Widdows, Dominic (April 2010). "Reflective Random Indexing and indirect inference: A scalable method for discovery of implicit connections". Journal of Biomedical Informatics. 43 (2): 240–256. doi:10.1016/j.jbi.2009.09.003. ISSN 1532-0464. PMID 19761870.