通过异质超图进行一类边缘分类,用于因果发现.
Marcos Paulo Silva Gôlo1, Ricardo Marcondes Marcacini2
1Institute of Mathematical and Computer Sciences, University of Sao Paulo, São Carlos, São Paulo, Brazil. marcosgolo@usp.br.
Scientific reports
|November 20, 2025
概括
我们介绍eCHOLGA,一种用于从事件对中发现因果关系的新方法. 它使用异构的超图和语言模型来提高对复杂事件相互依赖和因果关系的理解.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 图形神经网络的神经网络
背景情况:
- 从事件对中发现因果关系对于理解复杂系统至关重要.
- 大型语言模型 (LLM) 在事件语义方面表现出色,但在全球事件结构方面扎.
- 现有的基于图形的方法缺乏关系表达力,并可能导致脱节的结构.
研究的目的:
- 提出一种新的方法,eCHOLGA (边缘分类通过异质一类图形自编码器),用于从事件对中有效的因果发现.
- 为了利用异构的超图和LLM语义特征来增强因果关系建模.
- 改进拓连接,并使用图形神经网络 (GNN) 实现信息边缘表示.
主要方法:
- eCHOLGA使用异质超图,将关系转换为节点,并结合了额外的节点/边缘类型.
- 来自LLM的语义特征被整合到图形结构中,以实现更丰富的事件和关系表示.
- 采用一类学习策略,只需要积极的因果实例进行培训,减少标签的努力.
主要成果:
- 与最先进的方法相比,eCHOLGA在因果发现任务中表现优越.
- 该方法增强了拓连接性,使GNN能够学习更有信息的边缘表示.
- 实验结果验证了整合LLM特征和异质超图的有效性.
结论:
- 通过有效地建模复杂的相互依存关系,eCHOLGA为事件对中的因果发现提供了一个有希望的方法.
- 该方法提高了因果推理,可解释性,并减少了对广泛标记数据的需求.
- 这项工作通过为因果推理提供更具表达性和连接性的基于图形的框架来推进该领域.
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