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

Autor:	Emonet, Vincent, Bolleman, Jerven, Duvaud, Severine, de Farias, Tarcisio Mendes, Sima, Ana Claudia
Rok vydání:	2024
Předmět:	Computer Science - Databases Computer Science - Artificial Intelligence Computer Science - Information Retrieval
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.06062 Zobrazit plný text záznamu View this record from Arxiv