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This research aims to improve the accuracy of SPARQL query generation from natural language questions using existing schemas.

April 13, 2026BioinformaticsOpen Access

Accurate SPARQL Generation via In-Context Learning and Schema-based Query Construction

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This research aims to improve the accuracy of SPARQL query generation from natural language questions using existing schemas.
Developed a framework that combines LLM for word extraction and schema-based query construction.
The LLM extracts relevant variables and parameters from user queries based on a predefined schema.
Utilized a proof-of-concept chatbot system to facilitate RDF database querying via natural language.
The proposed method outperforms traditional LLM-based approaches in generating coherent SPARQL queries.
Experimental comparisons showed high similarity between the generated queries and those written by experts.
The chatbot system effectively demonstrates practical utility in querying biological databases.

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Novel framework generates accurate SPARQL queries in biological databases, enhancing accessibility for users.

Nagazumi et al. (2026) studied this question.

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