This paper presents a knowledge management system for automobile failure analysis using retrieval-augmented generation (RAG) with large language models (LLMs) and knowledge graphs (KGs). In the automotive industry, there is a growing demand for knowledge transfer of failure analysis from experienced engineers to young engineers. However, failure events are phenomena that occur in a chain reaction, making them difficult for beginners to analyze them. While knowledge graphs, which can describe semantic relationships and structure information is effective in representing failure events, due to their capability of representing the relationships between components, there is much information in KGs, so it is challenging for young engineers to extract and understand sub-graphs from the KG. On the other hand, there is increasing interest in the use of Graph RAG, a type of RAG that combines LLMs and KGs for knowledge management. However, when using the current Graph RAG framework with an existing knowledge graph for automobile failures, several issues arise because it is difficult to generate executable queries for a knowledge graph database which is not constructed by LLMs. To address this, we focused on optimizing the Graph RAG pipeline for existing knowledge graphs. Using an original Q&A dataset, the ROUGE F1 score of the sentences generated by the proposed method showed an average improvement of 157.6% compared to the current method. This highlights the effectiveness of the proposed method for automobile failure analysis.

翻译：本文提出了一种利用检索增强生成（RAG）技术结合大语言模型（LLMs）与知识图谱（KGs）的汽车故障分析知识管理系统。在汽车行业中，将资深工程师的故障分析经验知识传递给年轻工程师的需求日益增长。然而，故障事件通常以连锁反应的现象发生，这使得初学者难以进行分析。尽管能够描述语义关系和结构化信息的知识图谱能有效表征故障事件——得益于其表示部件间关系的能力——但知识图谱中信息量庞大，年轻工程师从中提取和理解子图具有挑战性。另一方面，结合LLMs与KGs进行知识管理的图检索增强生成（Graph RAG）技术正受到越来越多的关注。然而，当使用当前的Graph RAG框架与现有的汽车故障知识图谱时，由于难以为非LLMs构建的知识图谱数据库生成可执行查询，会引发若干问题。为此，我们专注于针对现有知识图谱优化Graph RAG流程。基于自建的问答数据集，所提方法生成语句的ROUGE F1分数相较于现有方法平均提升了157.6%，这凸显了该方法在汽车故障分析中的有效性。