The management of cellular networks and services has evolved due to the rapidly changing demands and complexity of service modeling and management. 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 in cellular networks. This fusion of IBN with KGs facilitates an intelligent, flexible, and resilient service orchestration process. It can anticipate and mitigate issues that impact network performance or service delivery. We propose an intent completion approach using knowledge graph learning and a mapping model capable of inferring and validating the service intents in the network. Subsequently, these service intents are deployed using available network resources in a simulated fifth generation (5G) non-standalone (NSA). 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 distance-based 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 intent arrival times.