Knowledge graph-based entity alignment with unified representation for auditing

Zhou You-hua

doi:10.1007/s40747-025-01843-7

2025-03-28

DOI：10.1007/s40747-025-01843-7

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摘要

En 中文

审计工作借助审计知识图谱得以简化，而构建审计知识图谱的最大挑战在于实体对齐。实体对齐涉及将具有相同现实世界身份的实体对进行关联，旨在整合不同知识图谱中的异构知识。然而，大多数现有研究未能有效将属性和关系表示整合到统一的实体对齐框架中，而这对于在审计知识图谱中精确关联实体至关重要。本研究提出了一种基于知识图谱的实体对齐方法，该方法融合多属性和加权关系（KG-Marfia），用于智能审计。我们提出的KG-Marfia首先通过解决属性和关系的失衡问题提取实体表示，然后设计了一个堆叠图卷积网络作为编码器，以融合属性和关系信息，学习实体的统一表示。具体而言，我们采用基于SVM的分类器执行智能审计中的对齐任务。在两个公开数据集以及三个审计数据集上进行的实验表明，我们的KG-Marfia在性能上优于当前最先进的实体对齐方法。

Keyword:

Entity alignment

Knowledge graph

Intelligent auditing

Graph convolutional network

期刊

Complex and Intelligent Systems

IF:

4.6

论文数:

2.1K

被引数:

6.6K

机构

Guangdong Univ Foreign Studies

学者数:

119

论文数: 87

被引数: 21

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