{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T13:45:00Z","timestamp":1760708700050,"version":"build-2065373602"},"reference-count":34,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2014,5,2]],"date-time":"2014-05-02T00:00:00Z","timestamp":1398988800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Asso for Info Science &amp;amp; Tech"],"published-print":{"date-parts":[[2015,1]]},"abstract":"<jats:p>With the ubiquity of the Internet and the rapid development of Web 2.0 technology, social question and answering (<jats:styled-content style=\"fixed-case\">SQA<\/jats:styled-content>) websites have become popular knowledge\u2010sharing platforms. As the number of posted questions and answers (<jats:styled-content style=\"fixed-case\">QAs<\/jats:styled-content>) continues to increase rapidly, the massive amount of question\u2010answer knowledge is causing information overload. The problem is compounded by the growing number of redundant <jats:styled-content style=\"fixed-case\">QAs<\/jats:styled-content>. <jats:styled-content style=\"fixed-case\">SQA<\/jats:styled-content> 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 <jats:styled-content style=\"fixed-case\">QAs<\/jats:styled-content> (<jats:styled-content style=\"fixed-case\">CQAs<\/jats:styled-content>) of a target <jats:styled-content style=\"fixed-case\">QA<\/jats:styled-content>. We define two types of complementarity: partial complementarity and extended complementarity. First, we utilize a classification\u2010based approach to predict complementary relationships between <jats:styled-content style=\"fixed-case\">QAs<\/jats:styled-content> based on three measures: question similarity, answer novelty, and answer correlation. Then we construct a <jats:styled-content style=\"fixed-case\">CQA<\/jats:styled-content> network based on the derived complementary relationships. In addition, we introduce a <jats:styled-content style=\"fixed-case\">CQA<\/jats:styled-content> network analysis technique that searches the <jats:styled-content style=\"fixed-case\">QA<\/jats:styled-content> network to find direct and indirect <jats:styled-content style=\"fixed-case\">CQAs<\/jats:styled-content> of the target <jats:styled-content style=\"fixed-case\">QA<\/jats:styled-content>. The results of experiments conducted on the data collected from Yahoo! Answers <jats:styled-content style=\"fixed-case\">T<\/jats:styled-content>aiwan show that the proposed approach can more effectively identify <jats:styled-content style=\"fixed-case\">CQAs<\/jats:styled-content> than can the conventional similarity\u2010based method. Case and user study results also validate the helpfulness and the effectiveness of our approach.<\/jats:p>","DOI":"10.1002\/asi.23155","type":"journal-article","created":{"date-parts":[[2014,5,2]],"date-time":"2014-05-02T12:04:55Z","timestamp":1399032295000},"page":"99-116","source":"Crossref","is-referenced-by-count":7,"title":["Complementary QA network analysis for QA retrieval in social question\u2010answering websites"],"prefix":"10.1002","volume":"66","author":[{"given":"Duen\u2010Ren","family":"Liu","sequence":"first","affiliation":[{"name":"Institute of Information Management National Chiao Tung University  1001 Ta\u2010Hseuh Road Hsinchu 300 Taiwan"}]},{"given":"Yu\u2010Hsuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Information Management National Chiao Tung University  1001 Ta\u2010Hseuh Road Hsinchu 300 Taiwan"}]},{"given":"Minxin","family":"Shen","sequence":"additional","affiliation":[{"name":"Institute of Information Management National Chiao Tung University  1001 Ta\u2010Hseuh Road Hsinchu 300 Taiwan"}]},{"given":"Pei\u2010Jung","family":"Lu","sequence":"additional","affiliation":[{"name":"Institute of Information Management National Chiao Tung University  1001 Ta\u2010Hseuh Road Hsinchu 300 Taiwan"}]}],"member":"311","published-online":{"date-parts":[[2014,5,2]]},"reference":[{"volume-title":"Modern information retrieval","year":"1999","author":"Baeza\u2010Yates R.","key":"e_1_2_8_2_1"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1367497.1367561"},{"key":"e_1_2_8_4_1","first-page":"1107","volume-title":"Proceedings of the Association for Computing Machinery (ACM) Symposium on Applied Computing","author":"Blooma M.J.","year":"2008"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/130385.130401"},{"key":"e_1_2_8_6_1","first-page":"866","volume-title":"Proceedings of the 14th Association for Computing Machinery Special Interest Group on Knowledge Discovery and Data Mining (ACM KDD) International Conference on Knowledge Discovery and Data Mining","author":"Bouguessa M.","year":"2008"},{"key":"e_1_2_8_7_1","first-page":"265","volume-title":"Proceedings of the 18th Association for Computing Machinery (ACM) Conference on Information and Knowledge Management","author":"Cao X.","year":"2009"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/asi.21529"},{"key":"e_1_2_8_9_1","first-page":"8:1","volume-title":"Proceedings of the Workshop on Massive Data Analytics on the Cloud","author":"Chiang M.\u2010F.","year":"2010"},{"key":"e_1_2_8_10_1","unstructured":"Chinese Knowledge and Information Processing (CKIP). 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