A Meta-Learning-Based Joint Two-View Framework for Inductive Knowledge Graph Completion

Inductive knowledge graph completion (KGC) aims at predicting triples involving new entities or relations not present during training. Recently proposed methods have achieved good performance in predicting triples involving only unseen entities, which either utilize the enclosing subgraph reasoning or learn transferable structural patterns by sampling local subgraphs. However, existing methods predominantly focus on modeling entities based on neighboring relations within independent subgraphs, posing challenges in handling sparse knowledge graphs and leading to the loss of global semantic information. In this paper, we introduce MeJo, a meta-learning-based joint two-view framework. MeJo incorporates the ontology view to provide rich, transferable type information for entity representation. The two-view interaction connects each independent subgraph, enabling the model to learn global contextual information. The model is trained to capture transferable structure knowledge from the instance view and comprehensive semantic information from the ontology view, incorporating hierarchy-aware encoding for ontologies with hierarchical structures. Furthermore, our approach can extend to handling both unseen entities and unseen relations simultaneously during the test. Extensive experimental analysis reveals that MeJo excels beyond current state-of-the-art approaches in both effectiveness and generalizability across prevalent benchmark datasets.