We present an effective GNN-based knowledge graph embedding model, named WGE,
to capture entity- and relation-focused graph structures. 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 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
competitive baselines, obtaining state-of-the-art performances on seven
benchmark datasets for knowledge graph completion.Comment: 13 pages; 3 tables; 3 figure