Memory-KGC: Memory-augmented Structural Learning for Knowledge Graph Completion

With the rapid development of Large Language Models (LLMs), Knowledge Graphs (KGs) have become crucial for enhancing factual reasoning in Natural Language Processing (NLP). However, the inherent incompleteness of KGs limits their effectiveness, motivating the Knowledge Graph Completion (KGC) task, which aims to infer missing relations from known facts. Existing KGC approaches often rely on local subgraphs, which restrict global structural utilization and may introduce noise. To address these limitations, we propose Memory-KGC, a memory-augmented structural learning framework for KGC. Our model introduces a trainable memory module that explicitly captures and stores the global structural knowledge of KGs, while integrating local subgraph information to refine entity matching and reduce noise. Extensive experiments on multiple benchmark datasets, including large-scale graphs with over 4.5 million entities, show that Memory-KGC significantly outperforms state-of-the-art models. Ablation studies further confirm the effectiveness of both the memory module and subgraph integration.