Neuro-Symbolic Dynamic Knowledge Graph Evolution

Abstract

This paper proposes a novel framework for constructing and evolving a neuro-symbolic knowledge graph (NSKG) capable of adapting to dynamic changes in information. The core idea is to combine the strengths of deep learning and knowledge graphs, leveraging graph neural networks (GNNs) to learn relationships within the graph and incorporating a reward mechanism to guide optimization towards higher graph quality. Furthermore, the system dynamically integrates knowledge from external sources, allowing for incremental updates and corrections. The resulting NSKG can autonomously learn and evolve, addressing the limitations of static knowledge graphs commonly used in intelligent applications. The key contributions lie in the automated adaptation process and the ability to represent and reason with evolving knowledge. We present a formal model of the system and outline the key components, focusing on the learning process and the dynamic evolution strategy. The system's performance is assessed through a simulated environment, demonstrating its ability to maintain knowledge consistency and accuracy as new information emerges. The overall goal is to create a robust and intelligent knowledge representation system suitable for real-world applications requiring dynamic reasoning.