关键词: 知识图谱补全, 成对关系向量, 卷积神经网络, 链接预测

Abstract: Knowledge Graph Completion aims to solve the incompleteness problem of the knowledge graph by inferring the missing triples based on the triples already known in the knowledge graph. Most of the existing methods directly fuse entities and relations to extract features, ignoring the common features and specific attributes between the triples. To this end, the paper proposes ConvPair, a convolutional neural network model based on pairwise relation vectors, to improve the link prediction performance of Knowledge Graph completion through pairwise relation modelling, feature fusion optimization, and relation-aware convolutional operations. The key idea of the ConvPair model is to split the relation r into a head relation rh and a tail relation rt, and rh is fused with the head entity h, and then a commonality feature encoding module to obtain the common feature entity f. Next, f is fused with rt to predict the tail entity t through the specific attribute extraction module. In terms of entity-relationship fusion, a checkerboard reshaping operation is performed on the vector representations of h and rh to fully fuse the features; at the same time, the vector representations of f and rt are directly spliced together, preserving the ternary translation property in the specific attribute extraction. Finally, a relation-aware convolutional kernel is designed to effectively handle complex relations and extract relation-specific features. Experimental results show that ConvPair outperforms existing state-of-the-art models on datasets such as FB15k-237, WN18RR and YAGO3-10, demonstrating excellent performance and generalizability.

Key words: knowledge graph completion, paired relation vectors, convolutional neural network, link prediction