Graph attention network with edge weights for question answering over knowledge graph

Abstract

Question Answering over the Knowledge Graphs (KGQA) has attracted extensive attention. The graph neural network can represent the dependent information of the KG, so it is well applied to the KGQA. But most of the KGQA approaches based on graph neural networks model question sentences and candidate answer entities separately. And the influences among questions, relations, and structure are not fully utilized when learning entity representations. To solve these problems, it is proposed that a question answering method based on graph attention network with edge weight to enhance the question relevance of entity representation. For the relationship in the extracted candidate answer subgraph, the Roberta is used to calculate the question’s semantic similarity to be the edge weight. A graph attention network is relied on to fuse the pre-trained entity embeddings and edge weight information for node updates to obtain candidate answer representations. The experimental results show that our proposed model has certain advantages compared with some other benchmark methods.