Neuro-Symbolic Reasoning with Attentive Graph Neural Networks and Rule Extraction

Abstract

This paper presents a novel approach to neuro-symbolic reasoning by integrating attentive graph neural networks (GNNs) with rule extraction techniques. The core idea is to leverage the pattern recognition capabilities of GNNs for initial data processing while simultaneously extracting symbolic rules that encapsulate domain knowledge. The attentive mechanism within the GNN allows for selective focus on relevant graph features, improving both the accuracy and interpretability of the learned representations. These representations are then used as the basis for logical reasoning and decision-making, providing a more robust and explainable AI system compared to purely neural or symbolic approaches. We demonstrate the effectiveness of this hybrid system through a detailed explanation of the architecture and its theoretical underpinnings. The key contribution lies in the synergistic combination of graph representation learning and symbolic knowledge discovery, resulting in a system capable of handling complex reasoning tasks.