Prompted Meta-Learning for Few-shot Knowledge Graph Completion

• URL: http://arxiv.org/abs/2505.05684v1
• Date: Thu, 08 May 2025 22:59:42 GMT
• Title: Prompted Meta-Learning for Few-shot Knowledge Graph Completion
• Authors: Han Wu, Jie Yin,
• Abstract summary: Few-shot knowledge graph completion (KGC) has obtained significant attention due to its practical applications in real-world scenarios.<n>We propose a novel prompted meta-learning framework that seamlessly integrates meta-semantics with relational information for few-shot KGC.<n>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.
• Score: 11.880512693272367
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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