Two-view Graph Neural Networks for Knowledge Graph Completion

In this paper, we introduce a novel GNN-based knowledge graph embedding model, named WGE, to capture entity-focused graph structure and relation-focused graph structure. In particular, given the knowledge graph, WGE builds a single undirected entity-focused graph that views entities as nodes. In addition, WGE also constructs another single undirected graph from relation-focused constraints, which views entities and relations as nodes. WGE then proposes a new architecture of utilizing two vanilla GNNs directly on these two single graphs to better update vector representations of entities and relations, followed by a weighted score function to return the triple scores. Experimental results show that WGE obtains state-of-the-art performances on three new and challenging benchmark datasets CoDEx for knowledge graph completion.