| Summary: | 碩士 === 元智大學 === 資訊管理學系 === 101 === With the rapid explosion of the Internet technology and related applications, the volume of information available online grows dramatically. The situation we face changed. As John Naisbitt said, “We are drowning in information but starved for knowledge.” Thus, the development of effective and efficient knowledge discovery techniques to retrieve useful knowledge from huge dataset becomes an essential issue. The maturation of Web 2.0 applications has provided us opportunities and tools to extend existing knowledge discovery techniques. Community-driven question answering (CQA) website is a typical example which helps users to obtain useful information (i.e., answer to a specific question) in a more rapid and convenient way. However, the quality of answers varies extensively from professional to dilettante and brings another problem of determining the best answer from various candidates. Therefore, this study focuses on establishing an automatic method to predict the best answers in CQA environment. We extract comprehensive set of features and classify them into three categories, namely answer-related (A), question-related (Q), and user-related (U) variables. On the basis these three types of features, we proposed four prediction models, i.e., A, AQ, AU, and AQU. Moreover, several state-of-the-art single-classifier and multi-classifier induction techniques are examined to evaluate their performance on the best answer prediction issue in CQA websites. A dataset collected from Yahoo! Knowledge+ is employed for evaluation purpose. Some interesting and promising results are obtained from our empirical evaluation.
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