广东工业大学学报2026，Vol.43Issue(3)：34-46,13.DOI:10.12052/gdutxb.250098
基于多头注意力匹配的小样本知识图谱补全模型
Few-shot Knowledge Graph Completion Based on Multi-head Attention Matching
摘要
Abstract
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.关键词
知识图谱/知识图谱补全/小样本学习/注意力机制/链路预测Key words
knowledge graph/knowledge graph completion/few-shot learning/attention mechanism/link prediction分类
信息技术与安全科学引用本文复制引用
姜文超,吴方越..基于多头注意力匹配的小样本知识图谱补全模型[J].广东工业大学学报,2026,43(3):34-46,13.基金项目
国家自然科学基金资助重点项目(62237001) （62237001）
广东省自然科学基金资助面上项目(2024A1515011502) （2024A1515011502）
