LLM-based SPARQL Query Generation from Natural Language over Federated Knowledge Graphs

• URL: http://arxiv.org/abs/2410.06062v3
• Date: Mon, 21 Oct 2024 09:13:48 GMT
• Title: LLM-based SPARQL Query Generation from Natural Language over Federated Knowledge Graphs
• Authors: Vincent Emonet, Jerven Bolleman, Severine Duvaud, Tarcisio Mendes de Farias, Ana Claudia Sima,
• Abstract summary: We introduce a Retrieval-Augmented Generation (RAG) system for translating user questions into accurate SPARQL queries over bioinformatics knowledge graphs (KGs) To enhance accuracy and reduce hallucinations in query generation, our system utilise metadata from the KGs, including query examples and schema information, and incorporates a validation step to correct generated queries. The system is available online at chat.expasy.org.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 at chat.expasy.org.

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