We introduce a Retrieval-Augmented Generation (RAG) system for translating user questions into accurate federated SPARQL queries over bioinformatics knowledge graphs (KGs) leveraging Large Language Models (LLMs). To enhance accuracy and reduce hallucinations in query generation, our system utilises metadata from the KGs, including query examples and schema information, and incorporates a validation step to correct generated queries. The system is available online atthis http URL.
View on arXiv@article{emonet2025_2410.06062,
title={ LLM-based SPARQL Query Generation from Natural Language over Federated Knowledge Graphs },
author={ Vincent Emonet and Jerven Bolleman and Severine Duvaud and Tarcisio Mendes de Farias and Ana Claudia Sima },
journal={arXiv preprint arXiv:2410.06062},
year={ 2025 }
}