Natural Language Query to SQL Conversion Using Machine Learning Approach

Natural Language Processing is a computer science and artificial intelligence topic concerned with computer-human language interactions and how computers are designed for processing and exploring a variety of natural language data, in particular. The Structured Query Language for non-expert users is usually challenging database storage, they may not know the database structure. For database applications to improve the interaction between database and user, a new intelligent interface is therefore necessary. The concept of utilizing a natural language instead of a structured query language has led to the creation of the natural language interface to database systems as a new form of the processing procedure. This research aims to build a query generating process using an algorithm for machine learning to represent information according to the user's demands for answering the query and obtaining information. For the conversion of Natural Language Query into Structured Query, we utilized a lowercase conversion, removing escaped words, tokenization, PoS tagging, word similarity, Jaro-Winkler matching algorithm, and the method Naive Bayes.