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.

翻译：以透明且用户友好的方式解释机器学习结果，仍是可解释人工智能领域的一项挑战。本文提出一种方法，通过引入知识图谱增强机器学习模型的可解释性。我们将领域特定数据与机器学习结果及其对应解释进行关联存储，在领域知识与机器学习见解之间建立结构化连接。为使这些见解易于用户理解，我们设计了一种选择性检索方法：从知识图谱中提取相关三元组，并由大型语言模型进行处理，生成用户友好的机器学习结果解释。我们采用XAI问题库，在制造环境中评估了该方法。除标准问题外，我们引入了更复杂、更具针对性的问题，以突出本方法的优势。我们评估了33个问题，通过准确性与一致性等定量指标，以及清晰度与有用性等定性指标分析回答。我们的贡献兼具理论与实用价值：从理论角度，我们提出了一种使大型语言模型动态访问知识图谱以提升机器学习结果可解释性的创新方法；从实用角度，我们提供了实证证据，表明此类解释可在真实制造环境中成功应用，从而支持制造流程中更优的决策制定。