Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective
Luís C. Lamb, Artur d’Avila Garcez, Marco Gori, Marcelo O.R. Prates, Pedro H.C. Avelar, Moshe Y. Vardi
Abstract
Neural-symbolic computing has now become the subject of interest of both academic and industry research laboratories. Graph Neural Networks (GNNs) 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 their relationship to current developments in neural-symbolic computing.
BibTeX
@inproceedings{ijcai2020p679,
title = {Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective},
author = {Lamb, Luís C. and Garcez, Artur d’Avila and Gori, Marco and Prates, Marcelo O.R. and Avelar, Pedro H.C. and Vardi, Moshe Y.},
booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
Artificial Intelligence, {IJCAI-20}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Christian Bessiere},
pages = {4877--4884},
year = {2020},
month = {7},
note = {Survey track},
doi = {10.24963/ijcai.2020/679},
url = {https://doi.org/10.24963/ijcai.2020/679},
}