关键词: 知识图谱补全, 图神经网络, 归纳式推理, 链接预测

Abstract: Aiming at the method of knowledge map completion based on inductive relationship prediction, the existing knowledge graph completion methods based on inductive relationship prediction are limited to direct reasoning, and all entity sets must be known during training. This paper proposes a relationship prediction method based on graph neural network, extracts the local directed subgraphs of graph neural network for reasoning, introduces a node-edge bidirectional information transfer mechanism for inductive relationship reasoning, streng-thens the information communication between nodes and edges and effectively deals with the asymmetric relationship in the triplet. In view of the fact that different connection paths between entities reveal the nature of their relationship and help predictive reasoning, considering the relationship path between two entities, the relationship type suitable for inductive reasoning is used to represent the path, the edge embedding is defined attention formula, and the relationship between entities that have not been seen in the training set is predicted. Experimental results on common benchmark datasets suitable for inductive reasoning methods show that the method in this paper improves the prediction accuracy of triples compared with baseline models.

Key words: knowledge graph completion, graph neural network, inductive reasoning, link prediction