梳理神经符号AI中表示空间差异，揭示性能瓶颈根源
Bridging the Gap: Representation Spaces in Neuro-Symbolic AI
- 构建四层分类框架，系统分析表示空间类型与信息模态
- 归纳5类表示空间、4种符号逻辑与3种协同策略
- 基于46篇研究实证分析，指明跨模态融合的关键挑战
神经符号AI通过结合神经网络与符号学习的优势，提升模型整体性能。然而，两者在数据处理方式上的差异，尤其是表示方法的不同，常成为制约性能的关键因素。为此，我们基于2013年以来的191项研究，构建了四层分类框架：第一层定义五类表示空间，第二层聚焦五类可表征的信息模态，第三层描述四种符号逻辑方法，第四层提出三种神经网络与符号学习的协作策略。此外，对其中46项研究进行了深入分析，揭示表示空间不匹配对融合效果的影响。
原文摘要 · Abstract (English)
Neuro-symbolic AI is an effective method for improving the overall performance of AI models by combining the advantages of neural networks and symbolic learning. However, there are differences between the two in terms of how they process data, primarily because they often use different data representation methods, which is often an important factor limiting the overall performance of the two. From this perspective, we analyzed 191 studies from 2013 by constructing a four-level classification framework. The first level defines five types of representation spaces, and the second level focuses on five types of information modalities that the representation space can represent. Then, the third level describes four symbolic logic methods. Finally, the fourth-level categories propose three collaboration strategies between neural networks and symbolic learning. Furthermore, we conducted a detailed analysis of 46 research based on their representation space.
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