The rapid development of next-generation networking technologies underscores their transformative role in revolutionizing modern communication systems, enabling faster, more reliable, and highly interconnected solutions. However, such development has also brought challenges to network optimizations. Thanks to the emergence of Large Language Models (LLMs) in recent years, tools including Retrieval Augmented Generation (RAG) have been developed and applied in various fields including networking, and have shown their effectiveness. Taking one step further, the integration of knowledge graphs into RAG frameworks further enhanced the performance of RAG in networking applications such as Intent-Driven Networks (IDNs) and spectrum knowledge maps by providing more contextually relevant responses through more accurate retrieval of related network information. This paper introduces the RAG framework that integrates knowledge graphs in its database and explores such framework's application in networking. We begin by exploring RAG's applications in networking and the limitations of conventional RAG and present the advantages that knowledge graphs' structured knowledge representation brings to the retrieval and generation processes. Next, we propose a detailed GraphRAG-based framework for networking, including a step-by-step tutorial on its construction. Our evaluation through a case study on channel gain prediction demonstrates GraphRAG's enhanced capability in generating accurate, contextually rich responses, surpassing traditional RAG models. Finally, we discuss key future directions for applying knowledge-graphs-empowered RAG frameworks in networking, including robust updates, mitigation of hallucination, and enhanced security measures for networking applications.

翻译：下一代网络技术的快速发展突显了其在革新现代通信系统中的变革性作用，实现了更快速、更可靠和高度互联的解决方案。然而，这种发展也为网络优化带来了挑战。得益于近年来大语言模型（LLMs）的出现，包括检索增强生成（RAG）在内的工具已被开发并应用于包括网络在内的各个领域，并显示出其有效性。更进一步，将知识图谱集成到RAG框架中，通过更准确地检索相关网络信息，提供更具上下文相关性的响应，从而进一步提升了RAG在意图驱动网络（IDNs）和频谱知识图谱等网络应用中的性能。本文介绍了在数据库中集成知识图谱的RAG框架，并探讨了该框架在网络中的应用。我们首先探讨了RAG在网络中的应用以及传统RAG的局限性，并阐述了知识图谱的结构化知识表示如何为检索和生成过程带来优势。接着，我们提出了一个详细的基于GraphRAG的网络框架，包括其构建的逐步教程。我们通过对信道增益预测的案例研究进行评估，证明了GraphRAG在生成准确、上下文丰富的响应方面具有更强的能力，超越了传统的RAG模型。最后，我们讨论了知识图谱赋能的RAG框架在网络中应用的未来关键方向，包括稳健更新、幻觉缓解以及针对网络应用增强的安全措施。