Attention-Based Relational Learning Model for Few-Shot Knowledge Graph Completion
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Knowledge graphs are crucial for numerous applications, but their frequent incompleteness limits their utility. Few-shot knowledge graph completion (FKGC) addresses this by learning to infer new facts from only a handful of examples. However, existing FKGC methods are highly vulnerable to noisy or inconsistent reference examples, which can severely degrade model performance. To overcome this critical challenge, we introduce ATMR, an attention-based meta-relational learning framework. ATMR incorporates a novel attention mechanism that strategically identifies and upweights the most informative reference triples while diminishing the influence of potential noise. This allows for the construction of more robust and accurate relation representations. Rigorous experiments on two public datasets demonstrate that ATMR consistently outperforms baselines. Notably, it achieves an 8.5% improvement in the Hits@10 metric for 5-shot completion on the NELL-One dataset, validating its superior ability to handle noise in few-shot scenarios.
Abstract
Knowledge graphs are crucial for numerous applications, but their frequent incompleteness limits their utility. Few-shot knowledge graph completion (FKGC) addresses this by learning to infer new facts from only a handful of examples. However, existing FKGC methods are highly vulnerable to noisy or inconsistent reference examples, which can severely degrade model performance. To overcome this critical challenge, we introduce ATMR, an attention-based meta-relational learning framework. ATMR incorporates a novel attention mechanism that strategically identifies and upweights the most informative reference triples while diminishing the influence of potential noise. This allows for the construction of more robust and accurate relation representations. Rigorous experiments on two public datasets demonstrate that ATMR consistently outperforms baselines. Notably, it achieves an 8.5% improvement in the Hits@10 metric for 5-shot completion on the NELL-One dataset, validating its superior ability to handle noise in few-shot scenarios.