SKIP:一种基于原型的可扩展的知识图表表示学习方法
IEEE transactions on neural networks and learning systems
|November 26, 2025
概括
SKIP是一种基于的知识图表表示学习 (KGRL) 新方法,通过使用原型信息选择具有代表性的实体来提高效率. 这种方法提高了性能,同时降低了大型知识图的计算成本和模型参数.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 知识图表表示学习 (KGRL) 对于将模型应用于大型现实世界知识图表 (KG) 至关重要.
- 基于的方法旨在通过编码具有有限集的实体来降低KGRL中的计算成本和参数.
- 现有的选策略往往是基本的,可能导致KGRL性能不足.
研究的目的:
- 引入SKIP,一个可扩展的基于的KGRL方法,增强实体选.
- 利用原型信息来识别更具代表性和有效性的实体.
- 在大型知识图上提高KGRL模型的效率和性能.
主要方法:
- SKIP采用两步过程:预训练模型以使用拓和文本KG信息编码实体.
- 一个原型学习模块 (PLM) 提取实体原型以指导信息性实体的采样.
- 该方法侧重于选择包含有价值的原型信息的代表性实体.
主要成果:
- 在各种下游任务和KG尺度上,SKIP表现出卓越的性能和有效性.
- 在OGB WikiKG 2数据集上,SKIP实现了可比性能,运行时间大幅缩短 (约1. 21.28%) 和模型参数 (大约. 21.43%).这是一个很好的例子.
- 结果强调SKIP与基线方法相比,提高了可扩展性和效率.
结论:
- SKIP为基于的KGRL提供了一个可扩展和有效的解决方案,优于现有的方法.
- 在SKIP中以原型驱动的选导致性能提高和减少资源需求.
- 对于将KGRL应用到大型,现实世界的知识图表来说,SKIP是一个有前途的进步.
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