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Real-world Knowledge Graphs (KGs) often suffer from incompleteness, whichlimits their potential performance. Knowledge Graph Completion (KGC) techniquesaim to address this issue. However, traditional KGC methods are computationallyintensive and impractical for large-scale KGs, necessitating the learning ofdense node embeddings and computing pairwise distances. Generativetransformer-based language models (e.g., T5 and recent KGT5) offer a promisingsolution as they can predict the tail nodes directly. In this study, we proposeto include node neighborhoods as additional information to improve KGC methodsbased on language models. We examine the effects of this imputation and showthat, on both inductive and transductive Wikidata subsets, our methodoutperforms KGT5 and conventional KGC approaches. We also provide an extensiveanalysis of the impact of neighborhood on model prediction and show itsimportance. Furthermore, we point the way to significantly improve KGC throughmore effective neighborhood selection.

Better Together: Enhancing Generative Knowledge Graph Completion with Language Models and Neighborhood Information