Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective

• URL: http://arxiv.org/abs/2003.00330v7
• Date: Sat, 12 Jun 2021 23:05:33 GMT
• Title: Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective
• Authors: Luis C. Lamb, Artur Garcez, Marco Gori, Marcelo Prates, Pedro Avelar, Moshe Vardi
• Abstract summary: 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.
• Score: 8.047921724008278
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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