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.1Results 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.

We use the grammatical relations (GRs) described in Carroll et al. (1998) to compare a number of parsing algorithms A first ranking of the parsers is provided by comparing the extracted GRs to a gold standard GR annotation of 500 Susanne sentences: this required an implementation of GR extraction software for Penn Treebank style parsers. In addition, we perform an experiment using the extracted GRs as input to the Lappin and Leass (1994) anaphora resolution algorithm. This produces a second ranking of the parsers, and we investigate the number of errors that are caused by the incorrect GRs.

BackgroundLiterature based discovery (LBD) automatically infers missed connections between concepts in literature. It is often assumed that LBD generates more information than can be reasonably examined.MethodsWe present a detailed analysis of the quantity of hidden knowledge produced by an LBD system and the effect of various filtering approaches upon this. The investigation of filtering combined with single or multi-step linking term chains is carried out on all articles in PubMed.ResultsThe evaluation is carried out using both replication of existing discoveries, which provides justification for multi-step linking chain knowledge in specific cases, and using timeslicing, which gives a large scale measure of performance.ConclusionsWhile the quantity of hidden knowledge generated by LBD can be vast, we demonstrate that (a) intelligent filtering can greatly reduce the number of hidden knowledge pairs generated, (b) for a specific term, the number of single step connections can be manageable, and (c) in the absence of single step hidden links, considering multiple steps can provide valid links.

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BackgroundThe volume of research published in the biomedical domain has increasingly lead to researchers focussing on specific areas of interest and connections between findings being missed. Literature based discovery (LBD) attempts to address this problem by searching for previously unnoticed connections between published information (also known as “hidden knowledge”). A common approach is to identify hidden knowledge via shared linking terms. However, biomedical documents are highly ambiguous which can lead LBD systems to over generate hidden knowledge by hypothesising connections through different meanings of linking terms. Word Sense Disambiguation (WSD) aims to resolve ambiguities in text by identifying the meaning of ambiguous terms. This study explores the effect of WSD accuracy on LBD performance.MethodsAn existing LBD system is employed and four approaches to WSD of biomedical documents integrated with it. The accuracy of each WSD approach is determined by comparing its output against a standard benchmark. Evaluation of the LBD output is carried out using timeslicing approach, where hidden knowledge is generated from articles published prior to a certain cutoff date and a gold standard extracted from publications after the cutoff date.ResultsWSD accuracy varies depending on the approach used. The connection between the performance of the LBD and WSD systems are analysed to reveal a correlation between WSD accuracy and LBD performance.ConclusionThis study reveals that LBD performance is sensitive to WSD accuracy. It is therefore concluded that WSD has the potential to improve the output of LBD systems by reducing the amount of spurious hidden knowledge that is generated. It is also suggested that further improvements in WSD accuracy have the potential to improve LBD accuracy.