  Real-world Knowledge Graphs (KGs) often suffer from incompleteness, which
limits their potential performance. Knowledge Graph Completion (KGC) techniques
aim to address this issue. However, traditional KGC methods are computationally
intensive and impractical for large-scale KGs, necessitating the learning of
dense node embeddings and computing pairwise distances. Generative
transformer-based language models (e.g., T5 and recent KGT5) offer a promising
solution as they can predict the tail nodes directly. In this study, we propose
to include node neighborhoods as additional information to improve KGC methods
based on language models. We examine the effects of this imputation and show
that, on both inductive and transductive Wikidata subsets, our method
outperforms KGT5 and conventional KGC approaches. We also provide an extensive
analysis of the impact of neighborhood on model prediction and show its
importance. Furthermore, we point the way to significantly improve KGC through
more effective neighborhood selection.
