# Augmented Knowledge Graph Querying leveraging LLMs

arXiv: https://arxiv.org/abs/2502.01298 · HTML: https://mlsanity.com/paper/2502.01298/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2025.

## Built on by (most cited first, 5 of 5)

- [Leveraging Knowledge Graphs and LLM Reasoning to Identify Operational Bottlenecks for Warehouse Planning Assistance](https://mlsanity.com/paper/2507.17273/) (2025)
- [GraphSeek: Next-Generation Graph Analytics with LLMs](https://mlsanity.com/paper/2602.11052/) (2026)
- [The Role of Visualization in LLM-Assisted Knowledge Graph Systems: Effects on User Trust, Exploration, and Workflows](https://mlsanity.com/paper/2505.21512/) (2025)
- [Intelligent Human-Machine Partnership for Manufacturing: Enhancing Warehouse Planning through Simulation-Driven Knowledge Graphs and LLM Collaboration](https://mlsanity.com/paper/2512.18265/) (2025)
- [Natural Language Access to Domain-Specific Metadata: A Reusable Framework for LLM Query Generation](https://mlsanity.com/paper/2607.18029/) (2026)

## Abstract (arXiv)

Adopting Knowledge Graphs (KGs) as a structured, semantic-oriented, data representation model has significantly improved data integration, reasoning, and querying capabilities across different domains. This is especially true in modern scenarios such as Industry 5.0, in which the integration of data produced by humans, smart devices, and production processes plays a crucial role. However, the management, retrieval, and visualization of data from a KG using formal query languages can be difficult for non-expert users due to their technical complexity, thus limiting their usage inside industrial environments. For this reason, we introduce SparqLLM, a framework that utilizes a Retrieval-Augmented Generation (RAG) solution, to enhance the querying of Knowledge Graphs (KGs). SparqLLM executes the Extract, Transform, and Load (ETL) pipeline to construct KGs from raw data. It also features a natural language interface powered by Large Language Models (LLMs) to enable automatic SPARQL query generation. By integrating template-based methods as retrieved-context for the LLM, SparqLLM enhances query reliability and reduces semantic errors, ensuring more accurate and efficient KG interactions. Moreover, to improve usability, the system incorporates a dynamic visualization dashboard that adapts to the structure of the retrieved data, presenting the query results in an intuitive format. Rigorous experimental evaluations demonstrate that SparqLLM achieves high query accuracy, improved robustness, and user-friendly interaction with KGs, establishing it as a scalable solution to access semantic data.