Few-shot Knowledge Graph Completion Based on Multi-head Attention Matching

Few-shot knowledge graph completion (FKGC) aims to infer missing triples within long-tail relations by leveraging a limited number of reference instances. Existing FKGC models struggle to effectively distinguish informative neighbors from noisy ones during the aggregation of neighborhood information for central entities. Moreover, in the matching and prediction phase, they typically rely solely on entity pair similarity, which often leads to biased predictions when the reference triples are unevenly distributed. To address these challenges, MhAMM, a novel FKGC model, is proposed based on multi-head attention matching. In the neighborhood aggregation stage, MhAMM introduces a multi-head attention mechanism tailored to the sparsity characteristics of FKGC tasks, which effectively amplifies the attention weights of informative neighbors while suppressing the influence of noisy ones, thereby improving the encoding quality of central entities. In the matching stage, a multidimensional matching network is designed, which integrates both the entity pair similarity score and a triple plausibility score computed via a fully connected neural network. These two complementary scores jointly enhance the overall matching performance. Extensive experiments on public datasets demonstrate that MhAMM consistently achieves significant improvements across multiple evaluation metrics, verifying the effectiveness and robustness of the proposed model.