arXiv 2411.195392024-11-29cs.AIcs.CLcs.IR

基于图检索增强生成（Graph RAG）的汽车故障分析知识管理

Knowledge Management for Automobile Failure Analysis Using Graph RAG

The University of Tokyo（东京大学）；Hokkaido University（北海道大学）；Isuzu Motors Limited（五十铃汽车公司）

Yuta Ojima, Hiroki Sakaji, Tadashi Nakamura, Hiroaki Sakata, Kazuya Seki, Yuu Teshigawara, Masami Yamashita, Kazuhiro Aoyama

备注：: 7 pages, 6 figures, to be published in 2024 IEEE International Conference on Bid Data (BigData)

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AI总结：

针对汽车故障分析知识传递难题，提出结合LLMs和KGs的Graph RAG知识管理系统，优化现有知识图谱的Graph RAG pipeline，在原始问答数据集上ROUGE F1分数较现有方法平均提升157.6%。

摘要：

本文提出了一种结合大型语言模型（LLMs）和知识图谱（KGs）的检索增强生成（RAG）技术，用于汽车故障分析的知识管理系统。在汽车行业，将故障分析知识从经验丰富的工程师传递给年轻工程师的需求日益增长。然而，故障事件是连锁反应现象，初学者难以分析。知识图谱可描述语义关系和结构化信息，能有效表示故障事件，因其具备表示组件间关系的能力，但知识图谱包含大量信息，年轻工程师难以从中提取和理解子图。另一方面，结合LLMs和KGs的Graph RAG在知识管理中的应用受到越来越多关注，但使用现有汽车故障知识图谱的现有Graph RAG框架时，由于难以生成针对非LLM构建的知识图谱数据库的可执行查询，会出现若干问题。为此，我们专注于优化现有知识图谱的Graph RAG pipeline。使用原始问答数据集，所提方法生成句子的ROUGE F1分数较现有方法平均提升157.6%，突显了该方法在汽车故障分析中的有效性。

英文摘要：

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.

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