An Embedding Model for Knowledge Graph Completion Based on Graph Sub-Hop Convolutional Network

Abstract

The research on knowledge graph completion based on representation learning is increasingly dependent on the node structural feature in the graph. However, a large number of nodes have few immediate neighbors, resulting in the node features unable to be fully expressed. Hence, multi-hop structure features are crucial to the representation learning of nodes. GCN (Graph Convolutional Network) is a graph embedding model that can introduce the multi-hop structure. However, the multi-hop information transmitted between GCN layers suffers a lot of losses. This would lead to the insufficient mining of the node structure features and semantic feature association among entities, further reducing the efficiency of graph knowledge completion. A gate-controlled graph sub-hop convolutional network model for knowledge graph completion is proposed to fill these research gaps. Firstly, a graph sub-hop convolutional network based on matrix representation is designed, which can transmit multi-hop neighbor features directly to the encoded node vector to avoid a large loss of features during multi-hop transmission. On this basis, the implicit multi-hop relations are explicitly embedded into the model based on the TransE. In the process of each hop convolution, aiming at the accumulation of noise redundancy caused by the increase of the receptive field, a sub-hop gate mechanism strategy is proposed to filter information. Finally, the linear model is used to decode the encoded nodes and then complete the knowledge graph. We carried out experimental comparison and analysis on WN18RR, FB15k-237, UMLS, and KINSHIP datasets. The results show that the embedding method based on the sub-hop structural information fusion can greatly improve the results of link prediction.