Bridging the Semantic Gap:Automated SQL Query Generation from Natural Language

Natural language processing and computer science researchers are exploring the conversion of natural language queries into SQL to bridge the gap between humans and machines. This process involves parsing the query, determining the database schema, creating a corresponding SQL query, and running the SQL query against the database. Researchers have explored methods like rule-based systems, machine learning algorithms, and neural networks. The conversion has several uses, including making databases more accessible, enabling non-technical users to extract information, and combining Natural Language Processing (NLP) with other disciplines like data analytics and business intelligence. The first stage involves using NLP approaches like tokenization, part-of-speech tagging, and named entity recognition to deconstruct the query into its components. The second stage involves translating the query's keywords into relevant SQL keywords and clauses. This technology has real-world applications in business intelligence, data analytics, and customer service, reducing the need for specialized training and technical competence.