Contextualized Semantic Graph Networks (CSGN)

Abstract

This paper introduces Contextualized Semantic Graph Networks (CSGN), a novel approach to knowledge representation and reasoning that addresses the limitations of traditional static knowledge graphs. The core idea is that semantic understanding fundamentally relies on a graph representation that dynamically adapts to the *context* of the information being processed. CSGN employs a graph neural network where nodes and edges are not static; rather, they evolve their attributes and relationships based on the surrounding context, thereby incorporating new information and adjusting semantic weights in real-time. This dynamic adaptation allows CSGN to handle complex, evolving data with a far greater degree of nuance and adaptability compared to static knowledge graphs. The framework is designed to improve performance in tasks requiring an understanding of interconnected information where context plays a crucial role. We demonstrate the potential of this approach through a theoretical framework and outline key design considerations.