scholarly journals Natural Language Statement to SQL Query Translator

2013 ◽  
Vol 82 (5) ◽  
pp. 18-22 ◽  
Author(s):  
Pranali P.Chaudhari
Keyword(s):  
Natural Language ◽  
Sql Query ◽  
Natural Language Statement

scholarly journals nQuery - A Natural Language Statement to SQL Query Generator

2017 ◽  
Author(s):  
Nandan Sukthankar ◽  
Sanket Maharnawar ◽  
Pranay Deshmukh ◽  
Yashodhara Haribhakta ◽  
Vibhavari Kamble
Keyword(s):  
Natural Language ◽  
Sql Query ◽  
Natural Language Statement

scholarly journals Automated SQL Query Generator by Understanding a Natural Language Statement

2018 ◽  
Vol 7 (3) ◽  
pp. 01-11 ◽  
Author(s):  
Amit Pagrut ◽  
Ishant Pakmode ◽  
Shambhoo Kariya ◽  
Vibhavari Kamble ◽  
Yashodhara Haribhakta
Keyword(s):  
Natural Language ◽  
Sql Query ◽  
Natural Language Statement

scholarly journals Natural Language to SQL query Generation

2021 ◽  
Vol 9 (VI) ◽  
pp. 5069-5072
Author(s):  
Kiran Raj R
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
English Language ◽  
Regular Expression ◽  
Parts Of Speech ◽  
Query Generation ◽  
Sql Query ◽  
Speech Tagging ◽  
The Web

Today, everyone has a personal device to access the web. Every user tries to access the knowledge that they require through internet. Most of the knowledge is within the sort of a database. A user with limited knowledge of database will have difficulty in accessing the data in the database. Hence, there’s a requirement for a system that permits the users to access the knowledge within the database. The proposed method is to develop a system where the input be a natural language and receive an SQL query which is used to access the database and retrieve the information with ease. Tokenization, parts-of-speech tagging, lemmatization, parsing and mapping are the steps involved in the process. The project proposed would give a view of using of Natural Language Processing (NLP) and mapping the query in accordance with regular expression in English language to SQL.

Cognitive Models Underlying Students' Formulation of Simple Linear Equations

1993 ◽  
Vol 24 (3) ◽  
pp. 217-232 ◽  
Author(s):  
Mollie MacGregor ◽  
Kaye Stacey
Keyword(s):  
Natural Language ◽  
Linear Equations ◽  
Cognitive Model ◽  
Multiple Choice ◽  
Response Format ◽  
Algebraic Equations ◽  
Free Response ◽  
Multiple Choice Item ◽  
Reversal Error ◽  
Natural Language Statement

Data are presented to show that errors in formulating algebraic equations are not primarily due to syntactic translation, as has been assumed in the literature. Furthermore, it is shown that the reversal error is common even when none of the previously published causes of the error is applicable. A new explanation is required and is proposed in this paper. An examination of students' errors leads us to suggest that students generally construct from the natural language statement a cognitive model of compared unequal quantities. They formulate equations by trying to represent the model directly or by drawing information from it. This hypothesis is supported by research on the comprehension of relationships by linguists, pyscholinguists and psychologists. Data were collected from 281 students in grade 9 in free response format and from 1048 students in grades 8, 9, and 10 who completed a multiple-choice item.

scholarly journals Hindi Language Interface to Database

2020 ◽  
Vol 32 ◽  
pp. 01007
Author(s):  
Rachana Dubey ◽  
Tejal Kawale ◽  
Twisha Choudhary ◽  
Vaibhav Narawade
Keyword(s):  
Natural Language ◽  
Data Storage ◽  
Query Language ◽  
Database Systems ◽  
Sources Of Information ◽  
Common People ◽  
Storage And Retrieval ◽  
Natural Language Query ◽  
Sql Query ◽  
Information Database

In our everyday lives we require information to accomplish daily tasks. Database is one of the most important sources of information. Database systems have been widely used in data storage and retrieval. However, to extract information from databases, we need to have some knowledge of database languages like SQL. But SQL has predefined structures and format, so it is hard for the non-expert users to formulate the desired query. To override this complexity, we have turned to natural language to retrieve information from database, which can be an ideal channel between a non-technical user and the application. But the application cannot understand natural language so an interface is required. This interface is capable of converting the user’s natural language query to an equivalent database language query. In this paper, we address the system architecture for translating a Hindi sentence in the form of an audio to an equivalent SQL query. The users don’t need to learn any formal query language; hence it’s easy to use for common people.

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