基于图神经网络的符号逻辑推理

Abstract

This paper explores the application of Graph Neural Networks (GNNs) to symbolic logic reasoning. Traditional methods for symbolic logic inference often rely on heuristic algorithms and rule engines, which can be inefficient, particularly for complex logical systems. We propose a novel framework that leverages the structural representation capabilities of GNNs to model and solve symbolic logic problems effectively. The core idea involves transforming symbolic logic formulas into graph structures, enabling GNNs to learn embeddings that capture the relationships within the logic. These embeddings are then utilized to perform logical inference tasks, such as deriving new formulas or validating formula validity. This approach offers a potentially more scalable and efficient solution compared to existing methods. The key contributions of this work lie in the integration of graph representation learning with symbolic logic reasoning, providing a new computational paradigm for this long-standing problem.