Neuro-Symbolic Reasoning with Graph Neural Networks and Rule Extraction

Abstract

This paper presents a novel framework for neuro-symbolic reasoning, addressing the longstanding challenge of integrating neural networks and symbolic reasoning. The core idea is to leverage the strengths of both approaches by employing a Graph Neural Network (GNN) to represent knowledge and perform initial reasoning steps, followed by a rule extraction algorithm that automatically learns logical rules from the GNN's learned representations. This approach avoids the need for manual rule engineering and allows the system to adapt and refine its reasoning capabilities over time. We demonstrate the effectiveness of this framework through a theoretical analysis and provide a detailed description of the components and their interactions. The key contributions of this work include a unified architecture for neuro-symbolic reasoning, a method for extracting logical rules from GNN representations, and a mechanism for iteratively refining both the GNN and the learned rules. The system's ability to learn complex relationships and generate logical rules represents a significant step towards more robust and explainable AI systems.