  We introduce a novel embedding model, named NoGE, 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, NoGE constructs a single graph considering entities and relations as
individual nodes. NoGE then computes weights for edges among nodes based on the
co-occurrence of entities and relations. Next, NoGE proposes Dual Quaternion
Graph Neural Networks (DualQGNN) and utilizes DualQGNN to update vector
representations for entity and relation nodes. NoGE then adopts a score
function to produce the triple scores. Comprehensive experimental results show
that NoGE obtains state-of-the-art results on three new and difficult benchmark
datasets CoDEx for knowledge graph completion.

Computation and Language;Machine Learning