关键词: 知识图谱, 知识图谱补全, 链接预测, 胶囊网络

Abstract:

The purpose of knowledge graph completion is to find the missing links in triples and solve the problem of sparse data in knowledge graph. This paper proposes a knowledge graph embedding method based on capsule network, which can model relational triples（head entities, relations, tail entities）. Firstly, the triple is represented as a 3-column matrix, which is convolved with multiple filters to produce different feature maps. Secondly, these feature maps are reconstructed into corresponding capsules, each capsule is a group of neurons, and a smaller size capsule is generated by the weighted product, and then a continuous vector is generated. Finally, the vector and the weight vector are subjected to a dot product operation to obtain a corresponding score, and the results of all the score summations are used to determine the correctness of the given triple. The experimental results show that compared with other models, the proposed method effectively improves the prediction accuracy of the triples, and the knowledge graph completion is better.

Key words: knowledge graph, knowledge graph completion, link prediction, capsule network