Exploring relation types for literature-based discovery

Preiss, J; Stevenson, M; Gaizauskas, R

Abstract

Objective Literature-based discovery (LBD) aims to identify “hidden knowledge” in the medical literature by: (1) analyzing documents to identify
pairs of explicitly related concepts (terms), then (2) hypothesizing novel relations between pairs of unrelated concepts that are implicitly related via
a shared concept to which both are explicitly related. Many LBD approaches use simple techniques to identify semantically weak relations between
concepts, for example, document co-occurrence. These generate huge numbers of hypotheses, difficult for humans to assess. More complex techniques rely on linguistic analysis, for example, shallow parsing, to identify semantically stronger relations. Such approaches generate fewer hypotheses, but may miss hidden knowledge. The authors investigate this trade-off in detail, comparing techniques for identifying related concepts
to discover which are most suitable for LBD.
Materials and methods A generic LBD system that can utilize a range of relation types was developed. Experiments were carried out comparing a
number of techniques for identifying relations. Two approaches were used for evaluation: replication of existing discoveries and the “time slicing”
approach.1
Results Previous LBD discoveries could be replicated using relations based either on document co-occurrence or linguistic analysis. Using relations
based on linguistic analysis generated many fewer hypotheses, but a significantly greater proportion of them were candidates for hidden
knowledge.
Discussion and Conclusion The use of linguistic analysis-based relations improves accuracy of LBD without overly damaging coverage. LBD systems often generate huge numbers of hypotheses, which are infeasible to manually review. Improving their accuracy has the potential to make
these systems significantly more usable

Citation

Preiss, J., Stevenson, M., & Gaizauskas, R. (2015). Exploring relation types for literature-based discovery. Journal of the American Medical Informatics Association, 22(5), 987-992. https://doi.org/10.1093/jamia/ocv002

• Journal Article Type: Article
• Acceptance Date: Dec 26, 2014
• Online Publication Date: May 12, 2015
• Publication Date: May 12, 2015
• Deposit Date: Nov 11, 2020
• Publicly Available Date: Nov 11, 2020
• Journal: Journal of the American Medical Informatics Association
• Print ISSN: 1067-5027
• Electronic ISSN: 1527-974X
• Publisher: Oxford University Press
• Volume: 22
• Issue: 5
• Pages: 987-992
• DOI: https://doi.org/10.1093/jamia/ocv002
• Publisher URL: https://doi.org/10.1093/jamia/ocv002
• Related Public URLs: http://jamia.oxfordjournals.org/
• Additional Information: Funders : Engineering and Physical Sciences Research Council (EPSRC); Grant Number: EP/J008427/1