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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).
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Received: 28 April 2013
Published: 24 July 2013
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[1] 赵辉,刘怀亮,范云杰. 复杂网络理论在中文文本特征选择中的应用研究[J]. 现代图书情报技术,2012(9):23-28.(Zhao Hui, Liu Huailiang, Fan Yunjie. Study on the Application of Complex Network Theory in Chinese Text Feature Selection[J].New Technology of Library and Information Service,2012(9):23-28.) [2] Erkan G, Radev D R. LexRank: Graph-based Lexical Centrality as Salience in Text Summarization[J]. Journal of Artificial Intelligence Research,2004,22(1):457-479. [3] Zhang X, Cheng G,Qu Y Z. Ontology Summarization Based on RDF Sentence Graph[C].In: Proceedings of the 16th International Conference on World Wide Web. 2007:707-716. [4] Unified Medical Language System (UMLS)[EB/OL].[2013-03-11]. http://www.nlm.nih.gov/research/umls/. [5] Aronson A R, Lang F M. An Overview of MetaMap: Historical Perspective and Recent Advances [J]. Journal of the American Medical Informatics Association, 2010,17(3):229-236. [6] Kilicoglu H, Fiszman M, Rodriguez A, et al. Semantic MEDLINE: A Web Application to Manage the Results of PubMed Searches[C].In: Proceedings of the 3rd International Symposium on Semantic Mining in Biomedicine. 2008:69-76. [7] Fiszman M, Demner-Fushman D, Kilicoglu H, et al. Automatic Summarization of MEDLINE Citations for Evidence-based Medical Treatment: A Topic-oriented Evaluation[J]. Journal of Biomedical Informatics,2009,42(5):801-813. [8] Workman E T, Hurdle J F. Dynamic Summarization of Bibliographic-based Data[J]. BMC Medical Informatics & Decision Making, 2011,11(6). doi:10.1186/1472-6947-11-6. [9] 商玥,王鸿飞,杨志豪. 利用语义关系抽取生成生物医学文摘的算法[J]. 计算机科学与探索, 2011,5(11):1027-1035.(Shang Yue, Wang Hongfei, Yang Zhihao. Automatic Summarization Algorithm for Biomedical Literature Based on Semantic Relation Extraction[J]. Journal of Frontiers of Computer Science & Technology, 2011,5(11):1027-1035.) [10] Zhang H, Fiszman M, Shin D, et al. Degree Centrality for Semantic Abstraction Summarization of Theraputic Studies[J]. Journal of Biomedical Informatics,2011,44(5):830-838. [11] de Nooy W, Mrvar A, Batagelj V.Appendix 1: Getting Started with Pajek[A].//Exploratory Social Network Analysis with Pajek[M].New York:Cambridge University Press,2010. [12] Freeman L C. Centrality in Social Networks: Conceptual Clarification[J]. Social Networks, 1979,1(3):215-239. [13] 高小强,赵星,陶乃航. 网络中心度用于期刊引文评价的有效性研究[J]. 大学图书馆学报,2009,27(5):61-64.(Gao Xiaoqiang, Zhao Xing, Tao Naihang. Validity of Journals Citation Evaluation with Centrality Indexes of Networks[J].Journal of Academic Libraries, 2009,27(5):61-64.) [14] McCray A T, Burgun A, Bodenreider O. Aggregating UMLS Semantic Types for Reducing Conceptual Complexity[J].Studies in Health Technology and Informatics,2001,84(1):216-220. |
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