Community answer recommendation based on heterogeneous semantic fusion

Yongliang Wu, Hu Yin, Qianqian Zhou, Jiahao Dong, Dan Wei, Dongbo Liu

Abstract: The community question-answering system has gradually replaced the search engine as the primary way for people to acquire and share knowledge. Users' interactive behavior establishes complex relationships among multiple entities in the question-answering community. With the accumulation of question-and-answer data, retrieving relevant answers to a new question is time-consuming. Some researchers have conducted related research on answer recommendation, mainly dependent on word-based question–answer literal matching, ignoring text semantics and connected entity relationships. In this paper, we propose a Community Answer Recommendation Model Based on Heterogeneous Semantic Fusion, called CARHSF, which combines text semantic and multi-aspect features to enhance answer recommendations. Firstly, we employ the PCFG to build parse trees of text and extract hierarchical phrases through the iterative traversal method. Meanwhile, we utilize phrase embedding to efficiently quantify semantic relatedness. Secondly, we construct a heterogeneous information network to fuse text semantics and entity-relationship features. Finally, we represent entity relations through the heterogeneous graph neural network and supply the recommended answers based on entity correlations. Comparative experiments on three real datasets demonstrate that CARHSF has advantages in answer recommendation tasks.