这篇论文讲怎么用RAG和LLM把技术文档自动变成知识图谱，用在反应堆诊断上，跑得还稳，搞可靠性的可以看看。
该研究提出利用检索增强生成和大语言模型，从系统描述中自动构建动态主逻辑模型，并表示为知识图谱（KG-DML）。方法沿DML层级进行定向检索，保留功能依赖和显式逻辑关系，支持故障向上传播和向下依赖追踪。验证应用于一座退役沸水反应堆的低压冷却剂注入系统，重复运行结果一致。多级评估覆盖各层精度、召回率、逻辑门一致性与结构完整性，证明自动化构建可将技术文档转为可用于诊断和可靠性分析的可执行功能模型。
Constructing Dynamic Master Logic Models as Knowledge Graphs for Complex System Diagnostics Using Retrieval-Augmented Large Language Models
Dynamic Master Logic (DML) provides a hierarchical framework for representing system behavior by linking functional objectives to underlying structural elements. However, DML construction typically relies on expert interpretation of technical documentation, limiting scalability for complex systems. This study presents a framework for automated construction of DML models from system descriptions and their representation as Knowledge Graphs (KG-DML), using Retrieval-Augmented Generation and Large Language Models as enabling tools. Building on prior work with small-scale systems, the framework extends automated KG-DML construction and evaluation to substantially larger and more complex systems. Model construction proceeds across the DML hierarchy using targeted retrieval while preserving functional dependencies and explicit logical relationships. The resulting KG-DML supports diagnostic reasoning, safety assessment, upward failure propagation, and downward dependency tracing. A multi-level validation methodology evaluates layer-specific precision and recall, logical gate consistency, and overall structural integrity. Application to the Low-Pressure Coolant Injection system of a decommissioned Boiling Water Reactor demonstrates consistent reconstruction across repeated runs. The results show that automated KG-DML construction can transform technical documentation into executable functional models for diagnostic and reliability analysis.