Prompted Meta-Learning for Few-shot Knowledge Graph Completion

Few-shot knowledge graph completion (KGC) has obtained significant attention due to its practical applications in real-world scenarios, where new knowledge often emerges with limited available data. While most existing methods for few-shot KGC have predominantly focused on leveraging relational information, rich semantics inherent in KGs have been largely overlooked. To address this gap, we propose a novel prompted meta-learning (PromptMeta) framework that seamlessly integrates meta-semantics with relational information for few-shot KGC. PrompMeta has two key innovations: (1) a meta-semantic prompt pool that captures and consolidates high-level meta-semantics, enabling effective knowledge transfer and adaptation to rare and newly emerging relations. (2) a learnable fusion prompt that dynamically combines meta-semantic information with task-specific relational information tailored to different few-shot tasks. Both components are optimized together with model parameters within a meta-learning framework. Extensive experiments on two benchmark datasets demonstrate the effectiveness of our approach.