计算机工程2026，Vol.52Issue(5)：139-149,11.DOI:10.19678/j.issn.1000-3428.0070058
面向多知识图谱融合的实体对齐优化方法
Entity Alignment Optimization Method for Multiple Knowledge Graphs Fusion
摘要
Abstract
Entity Alignment(EA)is a key step in the fusion of Knowledge Graphs(KGs).Existing EA methods only consider EA between two KGs,whereas many scenarios require the EA across multiple KGs.Existing methods transform the EA tasks of multiple KGs to several pairwise EA tasks,while ignoring the inherent connections and constraints among equivalent entities across all KGs.To address this problem,based on an analysis of existing EA optimization methods,Entity Alignment Optimization for Multiple knowledge Graphs Fusion(MGEAO)is proposed by leveraging the transitivity constraints of equivalent entities across multiple KGs.A general framework for EA optimization across multiple KGs is proposed by combining it with existing pairwise EA methods.First,the pre-alignment matrix for each KG pair is computed on the basis of entity embeddings.The matrix is then corrected to obtain the final alignment through multiple KGs alignment optimization,which integrates Bidirectional Normalization(BN),Deferred Acceptance Algorithm(DAA),Relation-Entity Aware adjustment(REA)and Transitivity Constrained Optimization(TCO).Experiments on the DBP15K,FB15K,and YAGO15K datasets indicate that the performance is significantly improved in relation to that of baseline EA models,i.e.,Hits@1 and Hits@10 are improved by up to 18.8 and 18.05 percentage points,respectively.关键词
实体对齐/知识融合/知识图谱/传递性约束/结构信息Key words
Entity Alignment(EA)/knowledge fusion/Knowledge Graph(KG)/transitivity constraint/structural information分类
信息技术与安全科学引用本文复制引用
王硕,李克,李泽霖..面向多知识图谱融合的实体对齐优化方法[J].计算机工程,2026,52(5):139-149,11.基金项目
国家自然科学基金(61972040). （61972040）
