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

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arxiv 2003.00330 v7 pith:AKTMNOZE submitted 2020-02-29 cs.AI cs.CLcs.LGcs.LO

Luis C. Lamb , Artur Garcez , Marco Gori , Marcelo Prates , Pedro Avelar , Moshe Vardi This is my paper · ORCID
classification cs.AIcs.CLcs.LGcs.LO
keywords computingneural-symbolicdomainsgnnsapplicationgraphnetworksneural
verification ladder T0 review T1 audit T2 compute T3 formal
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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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  1. Neurosymbolic Graph Enrichment for Grounded World Models

    cs.AI 2024-11 conditional novelty 6.0 of 10

    A neurosymbolic pipeline converts images into enriched knowledge graphs by combining LLM descriptions, AMR parsing, and LLM-generated implicit knowledge heuristics.

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