Knowledge graph enhanced retrieval-augmented generation for failure mode and effects analysis
arXiv:2406.18114 · doi:10.1016/j.jii.2025.100807
Abstract
Failure mode and effects analysis (FMEA) is an essential tool for mitigating potential failures, particularly during the ramp-up phases of new products. However, its effectiveness is often limited by the reasoning capabilities of the FMEA tools, which are usually tabular structured. Meanwhile, large language models (LLMs) offer novel prospects for advanced natural language processing tasks. However, LLMs face challenges in tasks that require factual knowledge, a gap that retrieval-augmented generation (RAG) approaches aim to fill. RAG retrieves information from a non-parametric data store and uses a language model to generate responses. Building on this concept, we propose to enhance the non-parametric data store with a knowledge graph (KG). By integrating a KG into the RAG framework, we aim to leverage analytical and semantic question-answering capabilities for FMEA data. This paper contributes by presenting set-theoretic standardization and a schema for FMEA data, an algorithm for creating vector embeddings from the FMEA-KG, and a KG-enhanced RAG framework. Our approach is validated through a user experience design study, and we measure the precision and performance of the context retrieval recall.
References in corpus (10)
- Efficient Estimation of Word Representations in Vector Space
- Survey of Hallucination in Natural Language Generation
- Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods
- A Survey of Large Language Models
- Unifying Large Language Models and Knowledge Graphs: A Roadmap
- Retrieval-Augmented Generation for Large Language Models: A Survey
- Text and Code Embeddings by Contrastive Pre-Training
- A Survey on Retrieval-Augmented Text Generation
- Comprehensible Artificial Intelligence on Knowledge Graphs: A survey
- Thinking, Fast and Slow: Combining Vector Spaces and Knowledge Graphs