在N-ary事实上进行链接预测的图形转换器.
Bin Hu1, Xiongjie Tao2, Hui Guo3
1Faculty of Humanities and Arts, Macau University of Science and Technology, Taipa, 999078, Macau, China.
Scientific reports
|December 20, 2025
概括
本研究介绍了N-ary图形转换器 (NAGT) 模型,以改进超关系知识图中的N-ary事实链接预测. NAGT增强了结构信息的利用,以便在建议中更准确地识别关联.
科学领域:
- 人工智能的人工智能
- 数据科学数据科学数据科学
- 图形理论 图形理论
背景情况:
- 超关系知识图扩展了传统的KG与多维的辅助信息,增加表示的复杂性.
- 与二进制关系相比,N-ary事实链接预测面临着由于N-ary事实的复杂和多样化的表达形式的挑战.
研究的目的:
- 解决现有的N-ary事实表示方法中不充分利用异质图形结构信息的问题.
- 为增强的N-ary事实链接预测提出一个N-ary图形转换器 (NAGT) 模型.
主要方法:
- 开发一个N-ary图形变压器 (NAGT) 模型.
- 纳入基于N-ary结构偏差的新型注意力机制.
- 改进N-ary异质图的表示.
主要成果:
- 与现有方法相比,NAGT模型在提取结构信息方面表现优越.
- 在JF17K,Wikipeople和WD50K数据集上的实验验证证证了NAGT的有效性.
- 该模型准确地识别了推场景中的关键关联.
结论:
- 拟议的NAGT模型有效地完成了知识图,并显示了N-ary事实链接预测任务的效率和稳定性.
- NAGT增强了N-ary异质图的表示性,从而提高了预测准确性.
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