通过图形数据学习和推理
1Department of Computer Science, Aalborg University, Aalborg, Denmark.
Frontiers in artificial intelligence
|September 7, 2023
概括
本综述绘制了人工智能领域的图形学习和推理,统一不同的方法,如图形神经网络和逻辑推理在一个共同的模型概念下. 它为分析和整合各种图形数据方法提供了一个框架.
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:
- 图形是人工智能,逻辑和统计学习中的基本结构.
- 图形表示学习和图形神经网络是快速发展的领域.
- 有多种方法可以用图形数据进行推理和学习.
研究的目的:
- 为学习和借助图形推理提供统一的概念框架.
- 为了绘制基于图形的AI方法的多样化景观.
- 促进不同建模范式的理论分析和整合.
主要方法:
- 引入了适用于各种框架 (例如知识库,GNN,SVM) 的一般语义模型概念.
- 调查了模型规范的常见策略 (概率因子化,特征构造).
- 开发了一个推理任务的分类法,并通过最大概率原则来表达学习.
主要成果:
- 在逻辑演,节点嵌入和其他图形学习技术中建立了统一的视角.
- 为各种图形建模方法提供了一个共同的语义基础.
- 创建了推理任务的分类学和跨框架学习的原则.
结论:
- 该综述为了解图形数据方法的优点和局限性提供了一个连贯的框架.
- 拟议的框架有助于结合和整合不同的建模范式.
- 这项工作作为进一步基于图形的AI理论进步的基础.
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