🤖 AI Summary
This study addresses the challenge of limited interpretability in machine learning models within manufacturing contexts, where opaque predictions often hinder effective decision-making. To bridge this gap, the authors propose a novel paradigm that leverages large language models to dynamically retrieve relevant triples from domain-specific knowledge graphs, thereby structurally linking expert knowledge with model predictions and generating user-friendly natural language explanations. Integrating knowledge graphs, large language models, and explainable artificial intelligence (XAI), the approach was evaluated on 33 manufacturing-related tasks. Results demonstrate superior performance across both quantitative metrics—such as accuracy and consistency—and qualitative dimensions, including clarity and practical utility, significantly enhancing model interpretability and decision support capabilities in real-world industrial settings.
📝 Abstract
Explaining Machine Learning (ML) results in a transparent and user-friendly manner remains a challenging task of Explainable Artificial Intelligence (XAI). In this paper, we present a method to enhance the interpretability of ML models by using a Knowledge Graph (KG). We store domain-specific data along with ML results and their corresponding explanations, establishing a structured connection between domain knowledge and ML insights. To make these insights accessible to users, we designed a selective retrieval method in which relevant triplets are extracted from the KG and processed by a Large Language Model (LLM) to generate user-friendly explanations of ML results. We evaluated our method in a manufacturing environment using the XAI Question Bank. Beyond standard questions, we introduce more complex, tailored questions that highlight the strengths of our approach. We evaluated 33 questions, analyzing responses using quantitative metrics such as accuracy and consistency, as well as qualitative ones such as clarity and usefulness. Our contribution is both theoretical and practical: from a theoretical perspective, we present a novel approach for effectively enabling LLMs to dynamically access a KG in order to improve the explainability of ML results. From a practical perspective, we provide empirical evidence showing that such explanations can be successfully applied in real-world manufacturing environments, supporting better decision-making in manufacturing processes.