关键词: 知识图谱推理, 实体分类, 链接预测, 图卷积神经网络（GCN）, Attention机制

Abstract:

In view of the fact that the traditional neural network method can not effectively consider the correlation between entities in current Knowledge Graph Reasoning methods, a Knowledge Graph Reasoning method combining Graph Convolution Neural network（GCN） and Attention Mechanism（Attention） is proposed. This method uses Attention Mechanism to calculate the correlation between entities and their neighborhood entities in knowledge graph, and obtains the entity feature vector. All the neighborhood entity features of entities are learned by parameter sharing technology of graph convolution neural network. The entity features and relationship features are fused to get the implicit feature vectors of each entity. The results of entity classification experiment and link prediction experiment show that the accuracy of this method is further improved than that of traditional neural network method, which provides methodological support for search, question and answer, recommendation and data integration.

Key words: knowledge graph reasoning, entity classification, link prediction, Graph Convolution Neural（GCN）network, Attention mechanism