Quadripartite graph-based clustering of questions

In a Community Question Answering (CQA) service, each user interaction is different and since there are a variety of complex questions, identijjdng similar questions for reusing answers is difficult. This is mainly because of lexical mismatch problem. This research 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 outpeiformed other baseline clustering techniques in identifYing similar questions in CQA corpora. We believe that these findings can serve to guide future developments in the reuse of similar question in CQA services.