Volume 66, Issue 1 p. 99-116
RESEARCH ARTICLE

Complementary QA network analysis for QA retrieval in social question-answering websites

Duen-Ren Liu

Duen-Ren Liu

Institute of Information Management, National Chiao Tung University, 1001 Ta-Hseuh Road, Hsinchu, 300 Taiwan

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Yu-Hsuan Chen

Yu-Hsuan Chen

Institute of Information Management, National Chiao Tung University, 1001 Ta-Hseuh Road, Hsinchu, 300 Taiwan

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Minxin Shen

Minxin Shen

Institute of Information Management, National Chiao Tung University, 1001 Ta-Hseuh Road, Hsinchu, 300 Taiwan

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Pei-Jung Lu

Pei-Jung Lu

Institute of Information Management, National Chiao Tung University, 1001 Ta-Hseuh Road, Hsinchu, 300 Taiwan

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First published: 02 May 2014
Citations: 7

Abstract

With the ubiquity of the Internet and the rapid development of Web 2.0 technology, social question and answering (SQA) websites have become popular knowledge-sharing platforms. As the number of posted questions and answers (QAs) continues to increase rapidly, the massive amount of question-answer knowledge is causing information overload. The problem is compounded by the growing number of redundant QAs. SQA websites such as Yahoo! Answers are open platforms where users can freely ask or answer questions. Users also may wish to learn more about the information provided in an answer so they can use related keywords in the answer to search for extended, complementary information. In this article, we propose a novel approach to identify complementary QAs (CQAs) of a target QA. We define two types of complementarity: partial complementarity and extended complementarity. First, we utilize a classification-based approach to predict complementary relationships between QAs based on three measures: question similarity, answer novelty, and answer correlation. Then we construct a CQA network based on the derived complementary relationships. In addition, we introduce a CQA network analysis technique that searches the QA network to find direct and indirect CQAs of the target QA. The results of experiments conducted on the data collected from Yahoo! Answers Taiwan show that the proposed approach can more effectively identify CQAs than can the conventional similarity-based method. Case and user study results also validate the helpfulness and the effectiveness of our approach.