Formation of SQL from Natural Language Query using NLP

M. Uma, V. Sneha, G. Sneha, J. Bhuvana, B. Bharathi
{"title":"Formation of SQL from Natural Language Query using NLP","authors":"M. Uma, V. Sneha, G. Sneha, J. Bhuvana, B. Bharathi","doi":"10.1109/ICCIDS.2019.8862080","DOIUrl":null,"url":null,"abstract":"Today, everyone has their own personal devices that connects to the internet. Every user tries to get the information that they require through internet. Most of the information is in the form of a database. A user who wants to access a database but having limited or no knowledge of database languages faces a challenging and difficult situation. Hence, there is a need for a system that enables the users to access the information in the database. This paper aims to develop such a system using NLP by giving structured natural language question as input and receiving SQL query as the output, to access the related information from the railways reservation database with ease. The steps involved in this process are tokenization, lemmatization, parts of speech tagging, parsing and mapping. The dataset used for the proposed system has a set of 2880 structured natural language queries on train fare and seats available. We have achieved 98.89 per cent accuracy. The paper would give an overall view of the usage of Natural Language Processing (NLP) and use of regular expressions to map the query in English language to SQL.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCIDS.2019.8862080","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 18

Abstract

Today, everyone has their own personal devices that connects to the internet. Every user tries to get the information that they require through internet. Most of the information is in the form of a database. A user who wants to access a database but having limited or no knowledge of database languages faces a challenging and difficult situation. Hence, there is a need for a system that enables the users to access the information in the database. This paper aims to develop such a system using NLP by giving structured natural language question as input and receiving SQL query as the output, to access the related information from the railways reservation database with ease. The steps involved in this process are tokenization, lemmatization, parts of speech tagging, parsing and mapping. The dataset used for the proposed system has a set of 2880 structured natural language queries on train fare and seats available. We have achieved 98.89 per cent accuracy. The paper would give an overall view of the usage of Natural Language Processing (NLP) and use of regular expressions to map the query in English language to SQL.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
使用NLP从自然语言查询生成SQL
今天,每个人都有自己连接到互联网的个人设备。每个用户都试图通过互联网获得他们需要的信息。大多数信息以数据库的形式存在。想要访问数据库但对数据库语言了解有限或完全不了解的用户将面临一种具有挑战性和困难的情况。因此,需要一个使用户能够访问数据库中的信息的系统。本文旨在利用自然语言的结构化问题作为输入,SQL查询作为输出,利用自然语言的自然语言结构化问题作为输入,利用自然语言的自然语言结构化问题作为输出,方便地从铁路订票数据库中获取相关信息。在这个过程中涉及的步骤是标记化、词法化、词性标注、解析和映射。该系统使用的数据集包含2880个结构化的自然语言查询,涉及火车票价和可用座位。我们达到了98.89%的准确率。本文将全面介绍使用自然语言处理(NLP)和使用正则表达式将英语查询映射到SQL。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Region Based Convolutional Neural Network for Human-Elephant Conflict Management System A Comparison of Regression Models for Prediction of Graduate Admissions Feature selection with LASSO and VSURF to model mechanical properties for investment casting Med-Recommender System for Predictive Analysis of Hospitals and Doctors Analysis of Facial Landmark Features to determine the best subset for finding Face Orientation
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1