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Knowledge graph completion (KGC) aims to discover missing relations of queryentities. Current text-based models utilize the entity name and description toinfer the tail entity given the head entity and a certain relation. Existingapproaches also consider the neighborhood of the head entity. However, thesemethods tend to model the neighborhood using a flat structure and are onlyrestricted to 1-hop neighbors. In this work, we propose a nodeneighborhood-enhanced framework for knowledge graph completion. It models thehead entity neighborhood from multiple hops using graph neural networks toenrich the head node information. Moreover, we introduce an additional edgelink prediction task to improve KGC. Evaluation on two public datasets showsthat this framework is simple yet effective. The case study also shows that themodel is able to predict explainable predictions.

NNKGC: Improving Knowledge Graph Completion with Node Neighborhoods