Interpretable answer retrieval based on heterogeneous network embedding.

• Multi-entity fusion and low interpretability of answers are urgent issues • We employ heterogeneous graphs for entity fusion of community question answering • The heterogeneous graph neural network is used for entity relation embedding • Meta-paths represent user intention and are pioneered for answer interpretability • Experiments on real datasets verify the interpretable question-answering performance Community question answering is a rising technology based on users' autonomous interactive behaviors, such as posting their issues, answering questions based on their experience, and commenting on existing questions. As a result of its use of natural language for communication and stimulation of user interest in information sharing, it has increasingly taken the place of other channels as the main way that people learn new things. Multi-type entity characteristics fusion and poor answer interpretability are the two major concerns that currently plague community answer prediction research. The Interpretable Answer Retrieval Method Based on Heterogeneous Network Embedding (IARHNE) is what we present in this work. It combines complex entity features and generates interpretable predicted answers. In order to incorporate the interactions of several kinds of individuals in answer social retrieval, we first build a heterogeneity graph. In order to acquire entity embeddings, we secondly use the heterogeneous graph neural network. We then adopt the vector distance to convert the entity matching problem in the heterogeneous information network into a homogeneous node similarity job. Finally, using entity correlation to predict answers, we provide a list of answers to the new query and interpret them using meta-paths. Comparative studies using three authentic datasets demonstrate the benefits of IARHNE for interpretative question-answering research. [ABSTRACT FROM AUTHOR]