# Enhancing Manufacturing Knowledge Access with LLMs and Context-aware Prompting

arXiv: https://arxiv.org/abs/2507.22619 · HTML: https://mlsanity.com/paper/2507.22619/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 30 Jul 2025.

## Builds on (most cited first, 9 of 9)

- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [Talk like a Graph: Encoding Graphs for Large Language Models](https://mlsanity.com/paper/2310.04560/) (2023)
- [LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities](https://mlsanity.com/paper/2305.13168/) (2023)
- [SPARQL Generation: an analysis on fine-tuning OpenLLaMA for Question Answering over a Life Science Knowledge Graph](https://mlsanity.com/paper/2402.04627/) (2024)
- [SPARQL as a Foreign Language](https://mlsanity.com/paper/1708.07624/) (2017)
- [Leveraging LLMs in Scholarly Knowledge Graph Question Answering](https://mlsanity.com/paper/2311.09841/) (2023)
- [Semantic Parsing Natural Language into SPARQL: Improving Target Language Representation with Neural Attention](https://mlsanity.com/paper/1803.04329/) (2018)
- [Knowledge Graph Question Answering for Materials Science (KGQA4MAT): Developing Natural Language Interface for Metal-Organic Frameworks Knowledge Graph (MOF-KG) Using LLM](https://mlsanity.com/paper/2309.11361/) (2023)
- [Ontology Verbalization using Semantic-Refinement](https://mlsanity.com/paper/1610.09964/) (2016)

## Built on by (most cited first, 2 of 2)

- [OntoExtend: A Framework for Requirement-driven and Scalable Ontology Extension with LLMs](https://mlsanity.com/paper/2607.17963/) (2026)
- [Training Data Attribution for Image Generation using Ontology-Aligned Knowledge Graphs](https://mlsanity.com/paper/2512.02713/) (2025)

## Abstract (arXiv)

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.