A knowledge graph complement method based on improved graph convolution neural network

Abstract

In order to better measure the correlation between entities and solve the problem of link prediction in the knowledge graph, a knowledge graph completion method based on graph neural network and attention mechanism is proposed HRGAT. The hybrid attention mechanism is used to calculate the correlation of adjacent entities in the knowledge graph and obtain the corresponding feature entity vector. The capsule network is used as the decoder to better represent each attribute dimension of the entity, to improve the expression ability of migrated feature entity vectors, link prediction experiments were conducted in the classic datasets WN18RR and FB15K-237. The experimental results show that compared with other classical algorithms, this method has some improvement in four common evaluation indicators. It is proved that the hybrid attention mechanism can effectively improve the prediction accuracy of RGCN on the knowledge graph triplet, thus expanding the knowledge map and providing data support for downstream tasks.