Bridging the Gap: Representation Spaces in Neuro-Symbolic AI
This work addresses the challenge of integrating neural networks and symbolic learning for AI researchers, but it appears incremental as it focuses on classification and analysis rather than novel solutions.
The paper tackles the problem of differing data representation methods limiting performance in neuro-symbolic AI by analyzing 191 studies from 2013 using a four-level classification framework, including representation spaces, information modalities, symbolic logic methods, and collaboration strategies, with detailed analysis of 46 studies based on representation space.
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.