An algorithm to transform natural language into SQL queries for relational databases

Intelligent interface, to enhance efficient interactions between user and databases, is the need of the database applications. Databases must be intelligent enough to make the accessibility faster. However, not every user familiar with the Structured Query Language (SQL) queries as they may not aware of structure of the database and they thus require to learn SQL. So, non-expert users need a system to interact with relational databases in their natural language such as English. For this, Database Management System (DBMS) must have an ability to understand Natural Language (NL). In this research, an intelligent interface is developed using semantic matching technique which translates natural language query to SQL using set of production rules and data dictionary. The data dictionary consists of semantics sets for relations and attributes. A series of steps like lower case conversion, tokenization, speech tagging, database element and SQL element extraction is used to convert Natural Language Query (NLQ) to SQL Query. The transformed query is executed and the results are obtained by the user. Intelligent Interface is the need of database applications to enhance efficient interaction between user and DBMS.