Concept Extraction and Clustering for Search Result Organization and Virtual Community Construction

Computer Science and Information Systems

Hao-Ren Ke4¹  Chia-Ning Chang²  Yi-Hsiang Nien³  Shihn-Yuarn Chen⁴ 

[1]Corresponding Author, Professor and Deputy Library Director, Graduate Institute of Library and Information Studies, National Taiwan Normal University

[2]Master, Dept. of Computer Science, National Chiao Tung University

[3]Master, Institute of Information Management, National Chiao Tung University

[4]PhD Candidate, Dept. of Computer Science, National Chiao Tung University

关键词: information retrieval;    concept extraction;    document clustering;    virtual community;    social network analysis;    bibliographic;   

DOI  :  10.2298/CSIS101124020C

学科分类：社会科学、人文和艺术（综合）

来源: Computer Science and Information Systems

【 摘 要 】

This study proposes a concept extraction and clustering method, which improves Topic Keyword Clustering by using Log Likelihood Ratio for semantic correlation and Bisection K-Means for document clustering. Two value-added services are proposed to show how this approach can benefit information retrieval (IR) systems. The first service focuses on the organization and visual presentation of search results by clustering and bibliographic coupling. The second one aims at constructing virtual research communities and recommending significant papers to researchers. In addition to the two services, this study conducts quantitative and qualitative evaluations to show the feasibility of the proposed method; moreover, comparison with the previous approach is also performed.The experimental results show that the accuracy of the proposed method for search result organization reaches 80%, outperforming Topic Keyword Clustering. Both the precision and recall of virtual community construction are higher than 70%, and the accuracy of paper recommendation is almost 90%.

【 授权许可】

CC BY-NC-ND