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Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

Postal Subscription Code 80-970

2018 Impact Factor: 1.129

Front. Comput. Sci.    2016, Vol. 10 Issue (1) : 136-146    https://doi.org/10.1007/s11704-015-4548-5
RESEARCH ARTICLE
Topic hierarchy construction from heterogeneous evidence
Han XUE1,2,Bing QIN1,Ting LIU1,*(),Shen LIU1
1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
2. Harbin Engineering University Library, Harbin Engineering University, Harbin 150001, China
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Abstract

Existing studies on hierarchy constructionmainly focus on text corpora and indiscriminately mix numerous topics,thus increasing the possibility of knowledge acquisition bottlenecks and misconceptions. To address these problems and provide a comprehensive and in-depth representation of domain specific topics, we propose a novel topic hierarchy construction method with real-time update. This method combines heterogeneous evidence from multiple sources including folksonomy and encyclopedia, separately in both initial topic hierarchy construction and topic hierarchy improvement.Results of comprehensive experiments indicate that the proposed method significantly outperforms state-of-theart methods (t-test, p-value<0.000 1); recall has particularly improved by 20.4% to 38.7%.

Keywords hierarchy construction      Chinese topic hierarchy      folksonomy      heterogeneous evidence      hierarchy update     
Corresponding Author(s): Ting LIU   
Just Accepted Date: 04 June 2015   Issue Date: 06 January 2016
 Cite this article:   
Han XUE,Bing QIN,Ting LIU, et al. Topic hierarchy construction from heterogeneous evidence[J]. Front. Comput. Sci., 2016, 10(1): 136-146.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-015-4548-5
https://academic.hep.com.cn/fcs/EN/Y2016/V10/I1/136
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[1] Supplementary Material-Highlights in 3-page ppt Download
[1] Xiaohui GUO, Chunming HU, Richong ZHANG, Jinpeng HUAI. A probabilistic framework of preference discovery from folksonomy corpus[J]. Front. Comput. Sci., 2017, 11(6): 1075-1084.
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