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.

中文翻译：

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基于异构网络嵌入的可解释答案检索

社区问答是一种基于用户自主交互行为的新兴技术，例如发布问题、根据经验回答问题、评论现有问题等。由于其利用自然语言进行交流并激发用户信息共享的兴趣，它越来越取代其他渠道成为人们学习新事物的主要方式。多类型实体特征融合和答案可解释性差是目前困扰社区答案预测研究的两大问题。我们在这项工作中提出了基于异构网络嵌入的可解释答案检索方法（IARHNE）。它结合了复杂的实体特征并生成可解释的预测答案。为了将多种个体的相互作用纳入答案社会检索中，我们首先构建一个异质性图。为了获得实体嵌入，我们其次使用异构图神经网络。然后，我们采用向量距离将异构信息网络中的实体匹配问题转换为同构节点相似性作业。最后，使用实体相关性来预测答案，我们提供新查询的答案列表并使用元路径解释它们。使用三个真实数据集的比较研究证明了 IARHNE 对于解释性问答研究的好处。