知识图嵌入模型基于尖端的神经类图注意力网络关系预测
1School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China.
International journal of neural systems
|November 11, 2025
概括
本研究介绍了GEGS,这是一种用于知识图 (KG) 嵌入的新型框架,通过整合尖端神经P (SNP) 机制来增强关系预测. 在KG完成任务方面,GEGS取得了最先进的结果.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 自然语言处理自然语言处理.
背景情况:
- 知识图 (KG) 对NLP至关重要,但存在不完整性,限制了它们的实用性.
- 预测KG中缺失关系是一个重大的研究挑战.
研究的目的:
- 提出GEGS,一个新的KG嵌入框架,以提高关系预测中的可扩展性和表达性.
- 为了解决不完整的知识图的局限性.
主要方法:
- GEGS集成了GAT-SNP (带有尖端神经P机制的图表注意网络),以捕捉复杂的关系结构.
- 纳入了一个BiLSTM-SNP组件,以减轻远程和顺序路径特征中的信息丢失.
主要成果:
- 在基准数据集 (Kinship, FB15k-237, WN18RR) 的链接预测任务中,GEGS取得了卓越的表现.
- 该模型在多个评估指标中展示了最先进的结果,包括Hits@10和MRR.
结论:
- 通过改善关系预测,GEGS为知识图完成提供了有效的解决方案.
- 该框架的增强可扩展性和表达性为大规模的知识库应用铺平了道路.
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