Clustering Similar Questions In Social Question Answering Services

Social Question Answering (SQA) services are defined as dedicated platforms for users to respond to other users’ questions, resulting in the building of a community where users share and interactively give ratings to questions and answers (Liu et al., 2008). SQA services are emerging as a valuable information resource that is rich not only in the expertise of the user community but also their interactions and insights in the form of user comments and ratings. In SQA services each user interaction is different and since there are a variety of complex questions, identifying similar questions for re-using answers is difficult. Scholarly inquiries have yet to dovetail into a composite research stream in identifying similar questions by harnessing the information richness in SQA services. This paper aims to develop a quadripartite graph-based clustering (QGC) approach by harnessing relationship of a question with common answers and associated users. It was found that QGC approach outperformed other baseline clustering techniques in identifying similar questions in SQA corpora. We believe that these findings can serve to guide future developments in the reuse of similar question in SQA service