【摘要】 知识图谱补全旨在对知识图谱中隐藏实体关系进行挖掘和预测，传统方法大多利用图卷积神经网络对其进行补全。然而，传统方法大多只利用邻居节点的信息对中心节点进行更新，一定程度上忽视了关系信息。因此，提出一种改进的图卷积神经网络作为编码器，使用循环相关策略组合邻居节点和关系，并使用注意力机制区分其对中心节点的贡献程度，并以InteractE模型作为解码器，实现了端到端的网络训练。在WN18RR、FB15K-237和YAGO3-10数据集上进行实验，与目前最好模型相比，在MRR和Hits@N指标上都有一定程度的提高。更多还原

【Abstract】 Knowledge graph completion aims at mining and predicting hidden entity relationships in knowledge graph. Most traditional methods use graph convolutional neural network to complete knowledge graph. However, the traditional methods mainly use the information of neighbor nodes to update the central node, ignoring the relationship information to some extent. Therefore, an improved graph convolutional neural network is proposed as an encoder. It combined neighbor nodes and relationships by using cyclic correlation strategy, and used attention mechanism to distinguish the degree of their contribution to the central node. InteractE model was used as the decoder to achieve end-to-end network training. The experiments on WN18RR, FB15K-237 and YAGO3-10 data sets show that, compared with the current best model, the MRR and Hits@N indexes are improved to a certain extent.更多还原