Semantic Based Novelty Approach for Natural Language to SQL Conversion

Abstract

Databases are essential for storing and managing information in modern applications, organizations, and institutions. However, accessing data from relational databases typically requires knowledge of Structured Query Language (SQL), which many users do not possess. Formulating accurate SQL queries also demands an understanding of database schemas, table structures, and syntax rules. Natural Language to SQL (NL2SQL) systems aim to overcome this limitation by enabling users to interact with databases using everyday language (Affolter, 2019). Despite significant research in Natural Language Interface to Databases (NLIDB), existing systems still struggle with ambiguity, synonym variation, and complex query structures such as aggregation functions and joins (Li & Jagadish & Yu et al.). Therefore, a semantic-based novelty approach is needed to improve accuracy and usability.