Article Abstract

	Title:	Mining novel connections from online biomedical text databases using semantic query expansion and semantic-relationship pruning
	Author:	Xiaohua Hu, Xuheng Xu
	Address:	College of Information Science and Technology, Drexel University, Philadelphia, PA 19036, USA. ' College of Information Science and Technology, Drexel University, Philadelphia, PA 19036, USA
	Journal:	International Journal of Web and Grid Services 2005 - Vol. 1, No.2  pp. 222 - 239
	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.
	Keywords:	information extraction; text mining; query generation; information retrieval; biomedical databases; online databases; full text databases; semantic query expansion; semantic-relationship pruning; biomedical literature; biomedical ontologies; information retrieval; search terms; semantic network.
	DOI:	10.1504/IJWGS.2005.008321
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