Knowledge Graph Embedding in Intent-Based Networking

This paper presents a novel approach to network management by integrating intent-based networking (IBN) with knowledge graphs (KGs); thus, creating a more intuitive and efficient pipeline for service orchestration. By mapping high-level business intents onto network configurations using KGs, the system dynamically adapts to network changes and service demands, ensuring optimal performance and resource allocation. This integration facilitates a deeper understanding of network states and dependencies, enabling predictive adjustments and real-time troubleshooting. We utilize knowledge graph embedding (KGE) to acquire context information from the network and service providers. The trained KGE model maps intents to services via service prediction and intent validation processes in the proposed intent processing pipeline. We evaluate the trained model for its efficiency in the service mapping and intent validation tasks using simulated environments and extensive experiments. The service prediction and intent verification accuracy ≥80% is achieved for the trained KGE model on a custom service orchestration intent knowledge graph (IKG) based on TMForum’s intent common model.