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This article presents a novel Text-to-SQL strategy that utilizes large language models (LLMs) and knowledge graphs to improve SQL query generation from natural language questions, particularly for real-world databases. The proposed method addresses challenges such as vocabulary mismatch, ambiguity, schema complexity, and data semantics complexity by employing a dynamic few-shot example technique and a database keyword search tool. Experiments demonstrate that this strategy outperforms existing state-of-the-

Author

iqra.des

Language

EN