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

Shihn-Yuarn Chen¹, Chia-Ning Chang², Yi-Hsiang Nien³ and Hao-Ren Ke4⁴

1. PhD Candidate, Dept. of Computer Science, National Chiao Tung University
   Hsinchu, 300, Taiwan
   sychen@cs.nctu.edu.tw
2. Master, Dept. of Computer Science, National Chiao Tung University
   Hsinchu, 300, Taiwan
   chianing.chang@gmail.com
3. Master, Institute of Information Management, National Chiao Tung University
   Hsinchu, 300, Taiwan
   hugo3318@gmail.com
4. Corresponding Author, Professor and Deputy Library Director, Graduate Institute of Library and Information Studies, National Taiwan Normal University
   Taipei, 106, Taiwan
   clavenke@ntnu.edu.tw

Abstract

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%.

Key words

information retrieval, concept extraction, document clustering, virtual community, social network analysis, bibliographic

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS101124020C

Publication information

Volume 9, Issue 1 (January 2012)
Year of Publication: 2012
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

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How to cite

Chen, S., Chang, C., Nien, Y., Ke4, H.: Concept Extraction and Clustering for Search Result Organization and Virtual Community Construction. Computer Science and Information Systems, Vol. 9, No. 1, 323-355. (2012), https://doi.org/10.2298/CSIS101124020C