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New Technology of Library and Information Service  2013, Vol. Issue (6): 30-35    DOI: 10.11925/infotech.1003-3513.2013.06.05
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Comparative Analysis of Centrality Indices in Extracting Concepts from Semantic Predication Network——Based on Disease Treatment Research
Zhang Han, Liu Shuangmei
Department of Medical Informatics, China Medical University, Shenyang 110001, China
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Abstract  The aim of the study is to compare the validity of four node centrality indices in extracting crucial nodes from semantic predication network. Depending on Unified Medical Language System (UMLS) and SemRep, this paper first constructs a semantic predication network for biomedical literature, in which nodes represent UMLS concepts and edges semantic relations between nodes. Relying on the semantic type of the concepts and the semantic relations, schemas related to disease treatment are defined and used to extract disease treatment related predications. Then four centrality indices including degree centrality, betweenness centrality, closeness centrality and eigenvector centrality are used to extract crucial concepts related to four aspects of disease treatment (therapeutic drugs, therapeutic procedures, body location of the disease and disease comorbidities). The extracted concepts are compared to a reference standard produced by domain experts. The results show that centrality combined with semantic schema can effectively extract crucial nodes of the users interest. Among four centrality indices, degree centrality performs best (F-score is 0.72) and eigenvector centrality performs secondly best (F-score is 0.66).
Key wordsInformation extraction      Semantic predication network      Semantic schema      Node centrality     
Received: 28 April 2013      Published: 24 July 2013
:  TP391.1  

Cite this article:

Zhang Han, Liu Shuangmei. Comparative Analysis of Centrality Indices in Extracting Concepts from Semantic Predication Network——Based on Disease Treatment Research. New Technology of Library and Information Service, 2013, (6): 30-35.

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