title: Exploring relation types for literature-based discovery.
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.
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Computer Science, The University of Sheffield 211 Portobello, Sheffield S1 4DP, UK j.preiss@sheffield.ac.uk.
authors.forename: Judita
authors.initials: J
authors.lastname: Preiss
authors.affiliation: Department of Computer Science, The University of Sheffield 211 Portobello, Sheffield S1 4DP, UK.
authors.forename: Mark
authors.initials: M
authors.lastname: Stevenson
authors.affiliation: Department of Computer Science, The University of Sheffield 211 Portobello, Sheffield S1 4DP, UK.
authors.forename: Robert
authors.initials: R
authors.lastname: Gaizauskas
mesh_terms: D016247:Information Storage and Retrieval / Q000379:methods*; D008037:Linguistics; D009323:Natural Language Processing
keywords: knowledge discovery; literature based discovery; natural language processing; text mining