Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective
It provides a comprehensive overview for researchers interested in integrating GNNs with neural-symbolic approaches to enhance AI interpretability.
This paper surveys the use of Graph Neural Networks (GNNs) as a model for neural-symbolic computing, addressing the need for improved explainability and trust in AI systems by reviewing applications in domains like combinatorial optimization and relational reasoning.
Neural-symbolic computing has now become the subject of interest of both academic and industry research laboratories. Graph Neural Networks (GNN) have been widely used in relational and symbolic domains, with widespread application of GNNs in combinatorial optimization, constraint satisfaction, relational reasoning and other scientific domains. The need for improved explainability, interpretability and trust of AI systems in general demands principled methodologies, as suggested by neural-symbolic computing. In this paper, we review the state-of-the-art on the use of GNNs as a model of neural-symbolic computing. This includes the application of GNNs in several domains as well as its relationship to current developments in neural-symbolic computing.