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LLM-based SPARQL Query Generation from Natural Language over Federated Knowledge Graphs
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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.
Forward citations
Cited by 3 Pith papers
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MetaboT: An LLM-based Multi-Agent Frameworkfor Interactive Analysis of Mass SpectrometryMetabolomics Knowledge Graphs
A multi-agent LLM system converts natural-language metabolomics questions into SPARQL queries over the ENPKG knowledge graph, reaching 83.67% accuracy with GPT-4o versus 8.16% for the standalone model.
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SPARQL Query Generation with LLMs: Measuring the Impact of Training Data Memorization and Knowledge Injection
A controlled prompting protocol shows open-weight LLMs rely heavily on memorized Wikidata entities when writing SPARQL queries, with accuracy collapsing on a rarely used benchmark.
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Knowledge Conceptualization Impacts RAG Efficacy
An empirical study showing that both schema complexity and representation format affect how well GPT-4o generates SPARQL queries from competency questions, with mixed results across two knowledge graph families.
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