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BioMed Research International
Volume 2015 (2015), Article ID 428195, 11 pages
http://dx.doi.org/10.1155/2015/428195
Research Article

Disease Related Knowledge Summarization Based on Deep Graph Search

College of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China

Received 24 December 2014; Revised 29 March 2015; Accepted 25 April 2015

Academic Editor: Hong-Jie Dai

Copyright © 2015 Xiaofang Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The volume of published biomedical literature on disease related knowledge is expanding rapidly. Traditional information retrieval (IR) techniques, when applied to large databases such as PubMed, often return large, unmanageable lists of citations that do not fulfill the searcher’s information needs. In this paper, we present an approach to automatically construct disease related knowledge summarization from biomedical literature. In this approach, firstly Kullback-Leibler Divergence combined with mutual information metric is used to extract disease salient information. Then deep search based on depth first search (DFS) is applied to find hidden (indirect) relations between biomedical entities. Finally random walk algorithm is exploited to filter out the weak relations. The experimental results show that our approach achieves a precision of 60% and a recall of 61% on salient information extraction for Carcinoma of bladder and outperforms the method of Combo.