Coupling Relation Strength with Graph Convolutional Networks for Knowledge Graph Completion

Abstract

In the link prediction task of knowledge graph completion, Graph Neural Network (GNN)-based knowledge graph completion models have been shown by previous studies to produce large improvements in prediction results. However, many of the previous efforts were limited to aggregating the information given by neighboring nodes and did not take advantage of the information provided by the edges represented by relations. To address the problem, Coupling Relation Strength with Graph Convolutional Networks (RS-GCN) is proposed, which is a model with an encoder-decoder framework to realize the embedding of entities and relations in the vector space. On the encoder side, RS-GCN captures graph structure and neighborhood information while aggregating the information given by neighboring nodes. On the decoder side, RotatE is utilized to model and infer various relational patterns. The models are evaluated on standard FB15k, WN18, FB15k-237 and WN18RR datasets, and the experiments show that RS-GCN achieves better results than the current state-of-the-art classical models on the above knowledge graph datasets.