  We present an effective graph neural network (GNN)-based knowledge graph
embedding model, which we name WGE, to capture entity- and relation-focused
graph structures. Given a knowledge graph, WGE builds a single undirected
entity-focused graph that views entities as nodes. WGE also constructs another
single undirected graph from relation-focused constraints, which views entities
and relations as nodes. WGE then proposes a GNN-based architecture to better
learn vector representations of entities and relations from these two single
entity- and relation-focused graphs. WGE feeds the learned entity and relation
representations into a weighted score function to return the triple scores for
knowledge graph completion. Experimental results show that WGE outperforms
strong baselines on seven benchmark datasets for knowledge graph completion.
