Integrating expert profile, reputation and link analysis for expert finding in question-answering websites

• Published in: Information processing & management Vol. 49; no. 1; pp. 312 - 329
• Main Authors: Liu, Duen-Ren, Chen, Yu-Hsuan, Kao, Wei-Chen, Wang, Hsiu-Wen
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.01.2013; Elsevier; Elsevier Science Ltd
• Subjects: Authority; Categories; Community; Data processing; Exact sciences and technology; Expert finding; Expert systems; Experts; Information and communication sciences; Information processing and retrieval; Information retrieval systems. Information and document management system; Information retrieval. Man machine relationship; Information science. Documentation; Knowledge; Link analysis; Links; Management; Methods; Platforms; Question answering; Question answering systems; Reputations; Research process. Evaluation; Sciences and techniques of general use; Studies; Subject fields; User reputation; Web sites; Websites; Yahoo! Answer Taiwan; Taiwan; User reputation; Question answering; Expert finding; Link analysis; Community; Yahoo! Answer Taiwan; Expert; Web site
• ISSN: 0306-4573; 1873-5371
• DOI: 10.1016/j.ipm.2012.07.002

► Proposing a hybrid approach to find experts in question-answering websites. ► User knowledge profile and reputation contribute more than user authority. ► Considering the relevance of past questions to target question led to better quality. Question answering websites are becoming an ever more popular knowledge sharing platform. On such websites, people may ask any type of question and then wait for someone else to answer the question. However, in this manner, askers may not obtain correct answers from appropriate experts. Recently, various approaches have been proposed to automatically find experts in question answering websites. In this paper, we propose a novel hybrid approach to effectively find experts for the category of the target question in question answering websites. Our approach considers user subject relevance, user reputation and authority of a category in finding experts. A user’s subject relevance denotes the relevance of a user’s domain knowledge to the target question. A user’s reputation is derived from the user’s historical question-answering records, while user authority is derived from link analysis. Moreover, our proposed approach has been extended to develop a question dependent approach that considers the relevance of historical questions to the target question in deriving user domain knowledge, reputation and authority. We used a dataset obtained from Yahoo! Answer Taiwan to evaluate our approach. Our experiment results show that our proposed methods outperform other conventional methods.