Special Topics in Data Analysis and Knowledge Discovery
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 2
375-380

Interpretive subgraph generation model for knowledge graph link prediction task

Yao Junping
Yuan Cong
Li Xiaojun
Guo Yi
Wang Hao
Zhou Zhijie
Rocket Force University of Engineering, Xi'an 710025, China
DOI: 10.19734/j.issn.1001-3695.2023.06.0260

Abstract

In recent years, GNN has developed rapidly, and the performance of related models in knowledge graph link prediction tasks has been significantly improved. To explain the performance improvement, the researchers need to extract the subgraph patterns learned by the GNN. However, the interpretation accuracy of existing GNN interpreters in typical multi-relational graph data scenarios such as knowledge graphs has not been verified, and related tools have not yet been implemented, resulting in difficulty in extracting interpretation subgraphs. In response to this problem, this paper proposed a knowledge graph link prediction model that converted a multi-relational knowledge graph into a uni-relational graph. The model combined the entities in the knowledge graph into new nodes and the relationship as a new node. Node features generated a new graph with only a single relationship, and trained a denoising autoencoder on the new graph to obtain link prediction capabilities, and finally used a GNN interpreter to generate subgraph explanations. Experiments on three benchmark datasets show that the relative AUC index of the link prediction model based on single-relational transformation is significantly improved compared with GraIL without transformation. Finally, this paper selected the FB15K-237 dataset to conduct explanatory subgraph extraction experiments, and verified the effectiveness of the model in directly extracting link prediction explanations.

Foundation Support

国家自然科学基金资助项目
陕西省科技创新团队项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.06.0260
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 2
Section: Special Topics in Data Analysis and Knowledge Discovery
Pages: 375-380
Serial Number: 1001-3695(2024)02-008-0375-06

Publish History

[2023-08-21] Accepted Paper
[2024-02-05] Printed Article

Cite This Article

姚俊萍, 袁聪, 李晓军, 等. 面向知识图谱链接预测任务的解释子图生成模型 [J]. 计算机应用研究, 2024, 41 (2): 375-380. (Yao Junping, Yuan Cong, Li Xiaojun, et al. Interpretive subgraph generation model for knowledge graph link prediction task [J]. Application Research of Computers, 2024, 41 (2): 375-380. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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