  This paper presents a novel approach to network management by integrating
intent-based networking (IBN) with knowledge graphs (KGs), 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. We utilize knowledge graph embedding (KGE)
to acquire context information from the network and service providers. The KGE
model is trained using a custom KG and Gaussian embedding model and maps
intents to services via service prediction and intent validation processes. The
proposed intent lifecycle enables intent translation and assurance by only
deploying validated intents according to network and resource availability. We
evaluate the trained model for its efficiency in service mapping and intent
validation tasks using simulated environments and extensive experiments. The
service prediction and intent verification accuracy greater than 80 percent is
achieved for the trained KGE model on a custom service orchestration intent
knowledge graph (IKG) based on TMForum's intent common model.
