Link Prediction in Knowledge Hypergraph Combining Attention and Convolution Network

Abstract

Knowledge hypergraphs (KHG) are knowledge graph of hypergraph structure. KHG link prediction aims to predict the missing relations through the known entities and relations. However, HypE, the existing optimal KHG link prediction method based on embedding model considers the location information when embedding entities, but ignores the differences in the contributions of different entities when embedding relations. And the information of the entity convolution vector is insufficient. Relation embeddings consider the entity contribution and supply the information of entity embedding, which can greatly improve the prediction ability of model. Therefore, link prediction based on attention and convolution network (LPACN) is proposed. The improved attention mechanism is applied to merging entity attention information into relation embeddings. And the number information of neighboring entities in the same tuple is integrated into the convolution network, which further supplies the information of entity convolution embedding. For the gradient vanishing problem of LPACN, the improved ResidualNet is integrated into LPACN, and the multilayer perceptron (MLP) is used to improve the nonlinear learning ability of model. The improved algorithm LPACN+ is obtained. Extensive experiments on real datasets validate that LPACN is better than the Baselines.

Keywords

• knowledge hypergraph; link prediction; embedding model; attention mechanism