Management of networks and services has evolved due to the rapidly changing demands and complexity of the service modeling and design process. This paper uses intent-based networking (IBN) as a solution and couples it with contextual information from knowledge graphs (KGs) of network and service components to achieve the objective of service orchestration. This fusion of IBN with KGs facilitates an intelligent, flexible, and resilient service orchestration process capable of anticipating and mitigating issues that impact network performance or service delivery. We propose a knowledge graph learning approach for training an intent translation and a mapping model capable of inferring and validating the service intents in the network. Afterward, these service intents are deployed using available network resources in a simulated 5G nonstandalone network. The compliance of the deployed intents is monitored, and mutual optimization against their required service key performance indicators is performed using Simultaneous Perturbation Stochastic Approximation (SPSA) and Multiple Gradient Descent Algorithm (MGDA). The simulation results cover various scenarios to discover the performance of the proposed intent processing pipeline for different service requirements and compliance states. The numerical results show that the knowledge graph with Gaussian embedding (KG2E) model outperforms other distancebased embedding models for the proposed service KG. Moreover, the optimal deployment and compliance of mission-critical (MC) intents is ensured for greater than 90% of the independent simulation runs with varying points of intent deployment.