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Disease Related Knowledge Summarization Based on Deep Graph Search.

Wu X, Yang Z, Li Z, Lin H, Wang J - Biomed Res Int (2015)

Bottom Line: 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 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.

View Article: PubMed Central - PubMed

Affiliation: College of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China.

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.

No MeSH data available.


Related in: MedlinePlus

The framework of our method.
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Related In: Results  -  Collection


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fig1: The framework of our method.

Mentions: Our method consists of four major steps as shown in Figure 1. (1) Semantic relations are extracted from the sentence by semantic knowledge representation tool SemRep [11, 12]. (2) The relations most relevant to the seed topic are selected with the summarization algorithm based on Kullback-Leibler Divergence (KLD) [13] and Mutual Information [14, 15] (KM). (3) The hidden relations are extracted using deep search based on DFS from the directed unweighted graph of biomedical entities. (4) The weak relations are filtered out with the random walk algorithm and the final results are visualized.


Disease Related Knowledge Summarization Based on Deep Graph Search.

Wu X, Yang Z, Li Z, Lin H, Wang J - Biomed Res Int (2015)

The framework of our method.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4561941&req=5

fig1: The framework of our method.
Mentions: Our method consists of four major steps as shown in Figure 1. (1) Semantic relations are extracted from the sentence by semantic knowledge representation tool SemRep [11, 12]. (2) The relations most relevant to the seed topic are selected with the summarization algorithm based on Kullback-Leibler Divergence (KLD) [13] and Mutual Information [14, 15] (KM). (3) The hidden relations are extracted using deep search based on DFS from the directed unweighted graph of biomedical entities. (4) The weak relations are filtered out with the random walk algorithm and the final results are visualized.

Bottom Line: 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 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.

View Article: PubMed Central - PubMed

Affiliation: College of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China.

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.

No MeSH data available.


Related in: MedlinePlus