在缺乏监督下对知识图的调整:一个具有积极交叉视图对比学习的一般框架
IEEE transactions on neural networks and learning systems
|October 17, 2023
概括
本研究引入了知识图对齐 (KGA) 的新框架,该框架同时匹配实体和关系. 它通过使用关系增强的活跃实例选择和使用有限的标记数据进行交叉视图对比学习来提高KG覆盖率.
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
- 知识表示 知识表示
背景情况:
- 知识图 (KG) 对于数据集成至关重要,但很少完成.
- 知识图对齐 (KGA) 增强了KG覆盖范围,但现有的方法往往忽视了关系,需要大量的标记数据.
研究的目的:
- 提出KG中同时对实体和关系进行对齐的一般框架.
- 解决缺乏监督和忽视传统KGA中关系的局限性.
主要方法:
- 开发了一个具有两个核心组件的框架:关系增强的活跃实例选择 (RAS) 和交叉视图对比学习 (CCL).
- RAS指导使用关系信息进行标记的有价值实例的选择.
- CCL利用不同视角的对比学习来放大有限的监督信号.
主要成果:
- 拟议的框架在稀缺的监督下,在各种KG对中持续改善KG对齐性能.
- 无论是RAS和CCL组件都会有助于提高对齐准确度.
- 该框架不依赖于模型,可以适应现有的实体和关系对齐技术.
结论:
- 新的框架有效地增强了KG覆盖范围,同时对实体和关系进行调整.
- 这种方法显著提高了对齐性能,即使有有限的标记数据.
- 这项工作为实际的KG调整挑战提供了可行的解决方案.
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