Quantifying and filtering knowledge generated by literature based discovery

BMC Bioinformatics

Research

Judita Preiss¹  Mark Stevenson¹ 

[1] Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello, Sheffield, UK;

关键词: Data mining;    Literature based discovery in the biomedical domain;    Biomedical text;   

DOI  :  10.1186/s12859-017-1641-9

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s) 2017