Formation of SQL from Natural Language Query using NLP

Uma, M. K.; Sneha, V.; Sneha, G; Bhuvana, J; Bharathi, B.

doi:10.1109/iccids.2019.8862080

Cited by 32 publications

(6 citation statements)

References 4 publications

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“…While discussing in context of computational complexity is less for syntax SQL-Net as it used syntax-based decision trees as compared to the others but it only achieves the overall accuracy of 12.4% [28]. If we consider the computational complexity along with the performance the IR-Net with BERT model seem to perform good with over 50% accuracy but lesser computational complexity [29]. While looking at the extensions in the existed model, Arpit Narechania et al [49] discussed the important of importance of natural language to structured queries mentioned the Debug-It-Yourself (DIY) approach based on state of art model NL2SQL in which they provided user with a sandbox where user have the provision to interact with three elements those are the mapping of the generated query on the basis of given question, small subset of underlying database and ensembled modal explanation of generated query, their discussion is the user interaction with the sandbox seems to efficient for control environment testing but not for actually deploying the models in professional environment where the feasibility and accessibility come into play.…”

Section: Figure 8 Flowchart Of Rat-sql [7] Analysis and Discussionmentioning

confidence: 99%

Natural Language to SQL Queries: A Review

Baig

Imran

Yasin

et al. 2022

IJIST

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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.

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Section: Figure 8 Flowchart Of Rat-sql [7] Analysis and Discussionmentioning

confidence: 99%

Natural Language to SQL Queries: A Review

Baig

Imran

Yasin

et al. 2022

IJIST

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“…al. [7] used the following techniques to extract information from natural language. First, they extracted 'attribute' using Parts Of Speech (POS) tags.…”

Section: B Natural Language Query To Sqlmentioning

confidence: 99%

NLI-GSC: A Natural Language Interface for Generating SourceCode

Ansari¹,

Vora²

2022

IJACSA

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There are many different programming languages and each programming language has its own structure or way of writing the code, it becomes difficult to learn and frequently switch between different programming languages. Due to this reason, a person working with multiple programming languages needs to look at documentations frequently which costs time and effort. In the past few years, there have been significant increase in the amount of papers published on this topic, each providing a unique solution to this problem. Many of these papers are based on applying NLP concepts in unique configuration to get the desired results. Some have used AI along with NLP to train the system to generate source-code in specific language, and some have trained the AI directly without pre-processing the dataset with NLP. All of these papers face two problems: a lack of proper dataset for this particular application and each paper can convent natural language into only one specified programming language source-code. This proposed system shows that a language independent solution is a feasible alternate for writing source-code without having full knowledge about a programming language. The proposed system uses Natural Language Processing to convert Natural Language into programming language-independent pseudo code using custom Named Entity Recognition and save it in XML (eXtensible Markup Language) format which is an intermediate step. Then, using traditional programming, this system converts the generated pseudo code into programming language-dependent source-code. In this paper, another novel method has been proposed to create dataset from scratch using predefined structure that is filled with predefined keywords creating unique combination of training dataset.

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“…The systems reviewed in this paper proposed algorithms to handle English language queries made by a user to obtain an SQL query built using a number of techniques [3,12,13]. The NLIDB-OLAP novel architecture proposed in this paper built upon three main pillars.…”

Section: Introductionmentioning

confidence: 99%

A Natural Language Interface to Relational Databases Using an Online Analytic Processing Hypercube

Hazboun

Owda

2021

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Structured Query Language (SQL) is commonly used in Relational Database Management Systems (RDBMS) and is currently one of the most popular data definition and manipulation languages. Its core functionality is implemented, with only some minor variations, throughout all RDBMS products. It is an effective tool in the process of managing and querying data in relational databases. This paper describes a method to effectively automate the conversion of a data query from a Natural Language Query (NLQ) to Structured Query Language (SQL) with Online Analytical Processing (OLAP) cube data warehouse objects. To obtain or manipulate the data from relational databases, the user must be familiar with SQL and must also write an appropriate and valid SQL statement. However, users who are not familiar with SQL are unable to obtain relevant data through relational databases. To address this, we propose a Natural Language Processing (NLP) model to convert an NLQ into an SQL query. This allows novice users to obtain the required data without having to know any complicated SQL details. The model is also capable of handling complex queries using the OLAP cube technique, which allows data to be pre-calculated and stored in a multi-dimensional and ready-to-use format. A multi-dimensional cube (hypercube) is used to connect with the NLP interface, thereby eliminating long-running data queries and enabling self-service business intelligence. The study demonstrated how the use of hypercube technology helps to increase the system response speed and the ability to process very complex query sentences. The system achieved impressive performance in terms of NLP and the accuracy of generating different query sentences. Using OLAP hypercube technology, the study achieved distinguished results compared to previous studies in terms of the speed of the response of the model to NLQ analysis, the generation of complex SQL statements, and the dynamic display of the results. As a plan for future work, it is recommended to use infinite-dimension (n-D) cubes instead of 4-D cubes to enable ingesting as much data as possible in a single object and to facilitate the execution of query statements that may be too complex in query interfaces running in a data warehouse. The study demonstrated how the use of hypercube technology helps to increase system response speed and process very complex query sentences.

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Formation of SQL from Natural Language Query using NLP

Cited by 32 publications

References 4 publications

Natural Language to SQL Queries: A Review

Natural Language to SQL Queries: A Review

NLI-GSC: A Natural Language Interface for Generating SourceCode

A Natural Language Interface to Relational Databases Using an Online Analytic Processing Hypercube

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