Model Graph Inductive Learning for Knowledge Graph Completion

The article introduces Model Graph Inductive Learning (MGIL), a novel framework for knowledge graph completion that enhances link prediction by constructing a model graph through clustering based on relational structures and entity types. By applying a Graph Neural Network (GNN) to this model graph, MGIL generates high-quality embeddings that capture the global structure of the knowledge graph, leading to improved stability and expressiveness compared to traditional methods. Extensive experiments show that MGIL achieves state-of-the-art performance on various inductive benchmarks, making it a significant advancement for practitioners focused on link prediction in knowledge graphs.