Dynamic Topological Semantic Network Inference

Abstract

This paper proposes a novel approach to system management leveraging dynamic topological information for intelligent inference. The core idea is to construct and maintain a semantic network that reflects the evolving relationships between system components, considering factors like network latency, device load, and communication costs. This network is continuously updated using a reinforcement learning-based probabilistic model, integrated with causal inference and knowledge graphs. The model learns and adapts to changes in the system topology, enabling real-time state prediction, anomaly detection, and optimized resource allocation. The system's performance is evaluated through simulation, demonstrating the effectiveness of the proposed methodology. The key contribution lies in treating topology as a dynamic element within the semantic reasoning process, moving beyond static parameter assumptions. The system utilizes a core claim of dynamic topology information to build and update a semantic network. The core mechanism involves a reinforcement learning-based probabilistic model combined with causal inference and knowledge graphs.