Adapting Network Information to Semantics for Generalizable and Plug-and-Play Multi-Scenario Network Diagnosis

2025-01-28

AI Summary

Existing network fault diagnosis methods suffer from poor generalizability and weak adaptability to diverse operational scenarios, limiting their effectiveness against heterogeneous faults. To address this, we propose NetSemantic—a plug-and-play framework for semantic-driven network diagnosis. First, we design a network symbolic representation scheme that unifies multimodal monitoring data (e.g., topology, logs, metrics) into semantically enriched textual embeddings. Second, we construct a dynamically updatable knowledge graph that ensures both timeliness and logical consistency of diagnostic knowledge. Third, we leverage large language models (LLMs) to perform cross-scenario fault attribution reasoning and automated health report generation. Experiments across multiple complex fault scenarios demonstrate that NetSemantic achieves significantly higher diagnostic accuracy, strong generalization across unseen topologies and failure modes, and lightweight deployability. This work establishes a novel, general-purpose paradigm for semantic-aware, LLM-powered intelligent network operations and maintenance.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Neuro-Symbolic Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Network fault diagnosis is a core challenge in ensuring the stability and reliability of modern network operations. Traditional approaches, limited by their training on specific performance metrics for predefined scenarios, struggle to generalize across diverse faults and anomalies in varying network environments. In recent years, large language models (LLMs) have demonstrated strong generalization capabilities across various domains. Building on this success, we propose NetSemantic, a plug-and-play intelligent network fault diagnosis framework based on LLMs. NetSemantic transforms multimodal network information into unified textual representations, enabling LLMs to perform reasoning and generate efficient fault resolutions and health assessment reports. To further enhance the logical reasoning capabilities of LLMs, we introduce a novel symbolic representation method that transforms logically strong network information into symbols. Additionally, we propose a self-adaptive data updating mechanism that dynamically incorporates network information into a knowledge graph to ensure the validity and timeliness of the knowledge base. Experimental results demonstrate that NetSemantic excels in network fault diagnosis across various complex scenarios, significantly improving diagnostic accuracy and reliability.

Problem

• Network Fault Diagnosis
• Flexibility
• Versatility

Innovation

• NetSemantic
• Large Language Models (LLMs)
• Automatic Update System