Deep graph search based disease related knowledge summarization from biomedical literature

In this paper, we present an approach to automatically construct disease related knowledge summarization from biomedical literature. In this approach, first 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 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, and outperforms the method of Combo. In addition, the method of deep search obtains more hidden relations than the original correlation extraction methods.