  Knowledge graph completion (KGC) aims to discover missing relations of query
entities. Current text-based models utilize the entity name and description to
infer the tail entity given the head entity and a certain relation. Existing
approaches also consider the neighborhood of the head entity. However, these
methods tend to model the neighborhood using a flat structure and are only
restricted to 1-hop neighbors. In this work, we propose a node
neighborhood-enhanced framework for knowledge graph completion. It models the
head entity neighborhood from multiple hops using graph neural networks to
enrich the head node information. Moreover, we introduce an additional edge
link prediction task to improve KGC. Evaluation on two public datasets shows
that this framework is simple yet effective. The case study also shows that the
model is able to predict explainable predictions.
