A novel community answer matching approach based on phrase fusion heterogeneous information network

Abstract

Community Question Answering (CQA) allows users to ask or answer questions in a social way, so it is becoming the primary means for people acquiring knowledge. However, the asker must wait until a satisfactory answer appears, which reduces user activity. In this paper, we propose an innovative answering method that matches the most relevant answers for the new issue automatically. Firstly, we utilize phrases to represent the semantic of the posts (answers/questions) and construct a Phrase Fusion Heterogeneous Information Network, called PFHIN, to represent complex entity relationships in CQA. So, the answer selection is regarded as the related entity retrieval task. Then, we define the distance between entities in PFHIN, which is independent of the meta path. Finally, the Type-constrained Top-k Similarity Entity Finding Algorithm (TTSEF) is proposed for finding the nearest entities according to the known start entity and end-entity type, which can match the most relevant answers automatically.To the best of our knowledge, it is the first work to define the phrase information network for answer selection and provide a novel idea for the heterogeneous information network fusion. Experimental results on three large-scale datasets (Stack Overflow, Super User, and Mathematics) from Stack Exchange demonstrate that our proposed approaches significantly outperform the state-of-the-art answer retrieval methods. Moreover, we conduct an in-depth analysis of the meta path to the optimal answer and reveal the critical role of phrases in community answer matching.