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

Luis Lamb 1 Artur Garcez 2 Marco Gori 2, 3 Marcelo Prates 1 Pedro Avelar 1 Moshe Vardi 4
3 MAASAI - Modèles et algorithmes pour l’intelligence artificielle
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems, UNS - Université Nice Sophia Antipolis (... - 2019), JAD - Laboratoire Jean Alexandre Dieudonné
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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Conference papers
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https://hal.archives-ouvertes.fr/hal-02878531
Contributor : Marco Gori <>
Submitted on : Tuesday, June 23, 2020 - 10:50:36 AM
Last modification on : Thursday, January 21, 2021 - 2:32:02 PM

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  • HAL Id : hal-02878531, version 1
  • ARXIV : 2003.00330

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Luis Lamb, Artur Garcez, Marco Gori, Marcelo Prates, Pedro Avelar, et al.. Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective. IJCAI-PRICAI 2020 - 29th International Joint Conference on Artificial Intelligence - Pacific Rim International Conference on Artificial Intelligence, Jul 2020, Yokohama, Japan. ⟨hal-02878531⟩

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