  We introduce a novel embedding model, named NoKE, which aims to integrate
co-occurrence among entities and relations into graph neural networks to
improve knowledge graph completion (i.e., link prediction). Given a knowledge
graph, NoKE constructs a single graph considering entities and relations as
individual nodes. NoKE then computes weights for edges among nodes based on the
co-occurrence of entities and relations. Next, NoKE utilizes vanilla GNNs to
update vector representations for entity and relation nodes and then adopts a
score function to produce the triple scores. Comprehensive experimental results
show that our NoKE obtains state-of-the-art results on three new, challenging,
and difficult benchmark datasets CoDEx for knowledge graph completion,
demonstrating the power of its simplicity and effectiveness.
