摘要：

实体对齐是对不同来源的知识图谱进行知识融合时的一个关键步骤, 现有方法通常只考虑两两图谱间的实体对齐, 而很多实际应用场景中需要对多个图谱进行融合。在解决多图谱实体对齐问题时, 现有的方法往往只能将其转化为多个图谱对的对齐任务, 忽略了多个图谱的等价实体间内在联系和约束, 从而影响了最终的对齐性能。针对上述问题, 在系统分析现有实体对齐优化方法的基础上, 利用等价实体在多个图谱间的传递性约束特征, 提出一种面向多知识图谱融合的实体对齐优化方法(MGEAO), 并通过与现有的两图谱间实体对齐方法相结合, 提出一种通用的多图谱实体对齐优化框架。首先根据每个知识图谱中各实体的嵌入表示计算得到各图谱对的实体预对齐矩阵, 然后经过多图谱间对齐优化方法修正预对齐矩阵得到最终结果。其中对齐优化方法融合了双向归一化(BN)、延迟接受算法(DAA)、关系实体感知调整算法(REA)和传递性约束优化算法(TCO)。在DBP15K、FB15K和YAGO15K等多个数据集上的实验表明, 和不采用多图谱间对齐优化的基线模型相比, 该优化方法性能显著提升, 其中Hits@1和Hits@10指标分别最大可提升18.8和18.05百分点。

关键词: 实体对齐, 知识融合, 知识图谱, 传递性约束, 结构信息

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.

Key words: Entity Alignment (EA), knowledge fusion, Knowledge Graph (KG), transitivity constraint, structural information