Better Together: Enhancing Generative Knowledge Graph Completion with Language Models and Neighborhood Information
This work addresses the computational inefficiency of traditional knowledge graph completion methods for large-scale graphs, offering a domain-specific improvement.
The paper tackles the problem of incompleteness in knowledge graphs by enhancing generative transformer-based language models with node neighborhood information for knowledge graph completion, showing that their method outperforms existing approaches like KGT5 and conventional methods on Wikidata subsets.
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.