Artificial Intelligence-Based Query Writer using Machine Learning
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Abstract
Writing SQL queries requires technical expertise, which limits database accessibility for non-technical users. This paper proposes an AI-based Query Writer that converts natural language inputs into structured SQL queries using Natural Language Processing (NLP) techniques. The system processes user input through text preprocessing, intent recognition, and entity extraction, followed by automated SQL query generation. A supervised learning approach is used to improve accuracy and contextual understanding of user requests. The proposed model reduces manual effort and minimizes syntax errors while interacting with relational databases. Experimental evaluation shows improved query generation accuracy and faster execution compared to traditional query writing methods. The system enhances usability, efficiency, and accessibility in database management environments. This framework can be further extended to support multilingual inputs and voice-based query systems, making intelligent database interaction more user-friendly and scalable for real-world applications.
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