[1]徐贞顺,张文豪,王振彪,等.融合多信息的图卷积实体对齐方法[J].郑州大学学报(工学版),2026,47(3):108-116.[doi:10.13705/j.issn.1671-6833.2026.03.010]
　XU Zhenshun,ZHANG Wenhao,WANG Zhenbiao,et al.Multiple Information Graph Convolutional Network Entity Alignment Method[J].Journal of Zhengzhou University (Engineering Science),2026,47(3):108-116.[doi:10.13705/j.issn.1671-6833.2026.03.010]
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融合多信息的图卷积实体对齐方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
47
期数:
2026年3期
页码:
108-116
栏目:
出版日期:
2026-05-27

文章信息/Info

Title:
Multiple Information Graph Convolutional Network Entity Alignment Method
文章编号:
1671-6833(2026)03-0108-09
作者:
徐贞顺1,2, 张文豪1,2, 王振彪1,2, 唐增金1,2, 赵泽宇1,2, 苏梦瑶1,2
1.北方民族大学 计算机科学与工程学院,宁夏 银川 750021;2. 北方民族大学 图像图形智能处理国家民委重点实验室,宁夏 银川 750021
Author(s):
XU Zhenshun1,2, ZHANG Wenhao1,2, WANG Zhenbiao1,2, TANG Zengjin1,2, ZHAO Zeyu1,2, SU Mengyao1,2
1.College of Compute Science and Engineering, North Minzu University, Yinchuan 750021, China; 2.The Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan 750021, China
关键词:
知识图谱;  图卷积网络;  实体对齐;  图注意力网络;  预训练语言模型
Keywords:
knowledge graph;  graph convolutional network;  entity alignment;  graph attention network; pre-trained languages model
分类号:
TP391
DOI:
10.13705/j.issn.1671-6833.2026.03.010
文献标志码:
A
摘要:
由于不同知识图谱在实体表示、关系定义和语义结构等方面的异质性,在图谱的结构性差异和信息缺失的情况下,仅依赖图结构难以有效提高对齐质量。为此,提出了一种融合多信息的图卷积实体对齐方法。首先,改进的PageRank算法用于筛选三元组,缓解知识图谱结构差异带来的影响;其次,通过图卷积网络学习实体和属性的嵌入表示,并利用这些表示迭代更新实体间的关系;最后,基于PBAB方法整合文本描述信息,与图结构信息加权融合,从而提升实体对齐的效果。实验在DBP15K数据集上开展,评估了所提方法与基准方法在实体对齐任务中的表现。实验结果表明:所提方法在Hits@1指标上相较于最优基准方法提升了约3%,其他评价指标也均有相应的提升。
Abstract:
Due to the heterogeneity in entity representation, relationship definition, and semantic structure between different knowledge graphs, it is difficult to effectively improve alignment quality in the presence of structural differences and information loss in the graphs by relying solely on graph structure. Therefore, in this study a graph convolutional entity alignment method that integrates multiple information was proposed. Firstly, the improved PageRank algorithm was used to filter triplets and alleviate the impact of differences in knowledge graph structure. Next, we learn the embedding representations of entities and attributes were learnt through graph convolutional networks, and the relationships between entities were iteratively updated by using these representations. Finally, based on the PBAB method, text description information was integrated and weighted with graph structure information to enhance the effectiveness of entity alignment. The experimental results showed that the proposed method improved the Hits@1 metric by approximately 3% compared to the optimal baseline, with corresponding improvements observed in other evaluation metrics as well.

参考文献/References:

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更新日期/Last Update: 2026-05-27