Passage retrieval based hidden knowledge discovery from biomedical literature

摘要

Department of Computer Science and Engineering, Dalian University of Technology, Dalian 116023, China;Department of Computer Science and Engineering, Dalian University of Technology, Dalian 116023, China;Department of Computer Science and Engineering, Dalian University of Technology, Dalian 116023, China;%Biomedical literature is growing at a double-exponential pace and automatic extraction of the implicit biological relationship from biomedical literature contributes to building the biomedical hypothesis that can be explored further experimentally. This paper presents a passage retrieval based method which can explore the hidden connection from MEDLINE records. In this method, the MeSH concepts are retrieved from the sentence-level windows and are therefore more relevant with the starting term. This method is tested on three classical implicit connections: Alzheimer's disease and indomethacin, Migraine and Magnesium, Schizophrenia and Calcium-independent phospholipase A2 in the open discovery. In our experiments, three computational methods for scoring and ranking the MeSH terms are explored: z-score, TFIDF (Term Frequency Inverse Document Frequency) and PMI (pointwise mutual information). Experimental results show this method can significantly improve the hidden knowledge discovery performance.