Mining novel connections from online biomedical text databases using semantic query expansion and semantic-relationship pruning
by Xiaohua Hu, Xuheng Xu
International Journal of Web and Grid Services (IJWGS), Vol. 1, No. 2, 2005

Abstract: This paper proposes a semantic-based approach for mining novel connections from biomedical literature. The method takes advantage of the biomedical ontologies, MeSH and UMLS, as the source of semantic knowledge. A prototype system, Biomedical Semantic-based Knowledge Discovery System (Bio-SbKDS), is designed to uncover novel hypotheses/connections hidden in biomedical literature through semantic query expansion and semantic-relationship pruning. Bio-SbKDS can automatically generate relevant search terms to retrieve the semantic-relevant articles from the online biomedical text databases. Using the semantic types and semantic relations of the biomedical concepts, Bio-SbKDS can identify the relevant concepts collected from Medline and generate the novel hypothesis between these concepts. Bio-SbKDS successfully replicates Dr. Swanson's two famous discoveries: Raynaud disease/fish oil and migraine/magnesium. Compared with previous approaches, our methods search much less articles, generate much less but more relevant novel hypotheses and require much less human intervention in the discovery procedure.

Online publication date: Fri, 02-Dec-2005

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