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.

翻译：本文提出了一种将基于意图的网络（IBN）与知识图谱（KG）相结合的新型网络管理方法，构建了更直观高效的服务编排流水线。通过利用知识图谱将高层业务意图映射到网络配置，该系统能够动态适应网络变化和服务需求，确保最优性能和资源分配。我们采用知识图谱嵌入（KGE）从网络和服务提供商处获取上下文信息。该KGE模型基于定制知识图谱和高斯嵌入模型进行训练，通过服务预测和意图验证过程将意图映射到服务。所提出的意图生命周期通过仅根据网络和资源可用性部署已验证的意图，实现了意图转换和保障。我们利用仿真环境和大量实验评估了训练模型在服务映射和意图验证任务中的效率。基于TMForum意图通用模型构建的定制服务编排意图知识图谱（IKG）上，训练后的KGE模型实现了超过80%的服务预测与意图验证准确率。