Dynamic Topological Dependency Learning (TDTL)

Abstract

This paper introduces Dynamic Topological Dependency Learning (TDTL), a novel approach to knowledge representation and reasoning that leverages reinforcement learning to dynamically construct and adapt an internal knowledge graph based on observed data and inference results. Unlike traditional methods that rely on pre-defined topologies or manual annotation, TDTL autonomously learns data dependencies through a 'topological optimization' algorithm. The system begins with a simple, undirected graph and iteratively modifies it based on prediction errors, guided by a reward signal. A 'topological regularization' mechanism is incorporated to prevent over-complexity and maintain graph connectivity. The core claim is that a system can automatically build and adjust its internal knowledge representation's topology to reflect dynamic data dependencies without pre-defined topologies or manual labels. This represents a significant departure from existing knowledge graph construction and reasoning techniques. ---