Neuro-Symbolic Reasoning via Dynamic Knowledge Graph Construction

Abstract

This paper introduces a novel neuro-symbolic reasoning framework centered around the dynamic construction of a knowledge graph. The core challenge in integrating neural networks and symbolic reasoning lies in the inherent differences in their representations and the difficulty in seamless translation between them. Our approach addresses this by establishing a continuous feedback loop. A neural network generates initial hypotheses, which are then used to construct a knowledge graph. Symbolic rules are then applied to refine and constrain this graph, ensuring consistency and logical validity. This dynamic process allows for a more robust and interpretable knowledge representation, moving beyond the limitations of static knowledge graphs. We detail the architecture, the ruleset, and the interaction mechanisms, emphasizing the system's ability to adapt and learn from both neural and symbolic sources. The system's performance is evaluated through a series of reasoning tasks, demonstrating its effectiveness in complex scenarios.