Enhancing Manufacturing Knowledge Access with LLMs and Context-aware Prompting

2025-07-30

AI Summary

Non-expert users struggle to formulate SPARQL queries over industrial knowledge graphs (KGs) such as the Bosch Line Information System KG and the I40 Core Information Model. Method: We propose a context-aware prompting framework that leverages large language models (LLMs) as natural language-to-SPARQL translators, tightly integrating KG ontology structure for contextual enhancement. Our structured prompts explicitly encode domain semantics and schema constraints to guide LLMs in generating precise, valid SPARQL queries while mitigating hallucination. Contribution/Results: Experiments demonstrate that our method achieves high-quality SPARQL generation using only lightweight schema context—significantly lowering the usability barrier for non-experts. It improves query accuracy and completeness, enabling effective knowledge retrieval and intelligent decision-making in manufacturing scenarios.

Technology Category

• Data Mining & Knowledge Management: Intelligent Query Processing
• Knowledge Representation and Reasoning: Description Logics
• Natural Language Processing: Question Answering

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Knowledge graphs (KGs) have transformed data management within the manufacturing industry, offering effective means for integrating disparate data sources through shared and structured conceptual schemas. However, harnessing the power of KGs can be daunting for non-experts, as it often requires formulating complex SPARQL queries to retrieve specific information. With the advent of Large Language Models (LLMs), there is a growing potential to automatically translate natural language queries into the SPARQL format, thus bridging the gap between user-friendly interfaces and the sophisticated architecture of KGs. The challenge remains in adequately informing LLMs about the relevant context and structure of domain-specific KGs, e.g., in manufacturing, to improve the accuracy of generated queries. In this paper, we evaluate multiple strategies that use LLMs as mediators to facilitate information retrieval from KGs. We focus on the manufacturing domain, particularly on the Bosch Line Information System KG and the I40 Core Information Model. In our evaluation, we compare various approaches for feeding relevant context from the KG to the LLM and analyze their proficiency in transforming real-world questions into SPARQL queries. Our findings show that LLMs can significantly improve their performance on generating correct and complete queries when provided only the adequate context of the KG schema. Such context-aware prompting techniques help LLMs to focus on the relevant parts of the ontology and reduce the risk of hallucination. We anticipate that the proposed techniques help LLMs to democratize access to complex data repositories and empower informed decision-making in manufacturing settings.

Problem

• Bridging natural language queries to SPARQL for manufacturing KGs
• Improving LLM accuracy with context-aware KG schema information
• Democratizing access to complex manufacturing data repositories

Innovation

• LLMs translate natural language to SPARQL queries
• Context-aware prompting improves KG query accuracy
• Manufacturing KG schema enhances LLM performance