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Natural Language to SQL Queries: A Review

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Bibliographic Details
Title: Natural Language to SQL Queries: A Review
Authors: Baig, Mirza Shahzaib, Imran, Azhar, Yasin , Amanullah, Butt, Abdul Haleem, Muhammad Imran Khan
Source: International Journal of Innovations in Science & Technology; Vol. 4 No. 1 (2022): Vol 4 Issue 1; 147-162 ; 2709-6130 ; 2618-1630
Publisher Information: 50SEA
Publication Year: 2022
Subject Terms: Natural Language Processing, Structured Query Language (SQL), text to relational database, Natural Language Interface for Databases (NLIDB), Intelligent Database System (IDBS), Database Management System (DBMS)
Description: The relational database is the way of maintaining, storing, and accessing structured data but in order to access the data in that database the queries need to be translated in the format of SQL queries. Using natural language rather than SQL has introduced the advancement of a new kind of handling strategy called Natural Language Interface to Database frameworks (NLIDB). NLIDB is a stage towards the turn of events of clever data set frameworks (IDBS) to upgrade the clients in performing adaptable questioning in data sets. A model that can deduce relational database queries from natural language. Advanced neural algorithms synthesize the end-to-end SQL to text relation which results in the accuracy of 80% on the publicly available datasets. In this paper, we reviewed the existing framework and compared them based on the aggregation classifier, select column pointer, and the clause pointer. Furthermore, we discussed the role of semantic parsing and neural algorithm’s contribution in predicting the aggregation, column pointer, and clause pointer. In particular, people with limited background knowledge are unable to access databases with ease. Using natural language interfaces for relational databases is the solution to make natural language to SQL queries. This paper presents a review of the existing framework to process natural language to SQL queries and we will also cover some of the speech to SQL model in discussion section, in order to understand their framework and to highlight the limitations in the existing models.
Document Type: article in journal/newspaper
File Description: application/pdf; text/xml
Language: English
Relation: https://journal.50sea.com/index.php/IJIST/article/view/145/612; https://journal.50sea.com/index.php/IJIST/article/view/145/807; https://journal.50sea.com/index.php/IJIST/article/view/145
DOI: 10.33411/ijist/2022040111
Availability: https://journal.50sea.com/index.php/IJIST/article/view/145
https://doi.org/10.33411/ijist/2022040111
Rights: Copyright (c) 2022 50Sea ; https://creativecommons.org/licenses/by/4.0
Accession Number: edsbas.2591C089
Database: BASE
Description
Abstract:The relational database is the way of maintaining, storing, and accessing structured data but in order to access the data in that database the queries need to be translated in the format of SQL queries. Using natural language rather than SQL has introduced the advancement of a new kind of handling strategy called Natural Language Interface to Database frameworks (NLIDB). NLIDB is a stage towards the turn of events of clever data set frameworks (IDBS) to upgrade the clients in performing adaptable questioning in data sets. A model that can deduce relational database queries from natural language. Advanced neural algorithms synthesize the end-to-end SQL to text relation which results in the accuracy of 80% on the publicly available datasets. In this paper, we reviewed the existing framework and compared them based on the aggregation classifier, select column pointer, and the clause pointer. Furthermore, we discussed the role of semantic parsing and neural algorithm’s contribution in predicting the aggregation, column pointer, and clause pointer. In particular, people with limited background knowledge are unable to access databases with ease. Using natural language interfaces for relational databases is the solution to make natural language to SQL queries. This paper presents a review of the existing framework to process natural language to SQL queries and we will also cover some of the speech to SQL model in discussion section, in order to understand their framework and to highlight the limitations in the existing models.
DOI:10.33411/ijist/2022040111