An Arabic natural language interface for querying relational databases based on natural language processing and graph theory methods

Bais, Hanane; Machkour, Mustapha; Koutti, Lahcen

doi:10.1504/ijris.2018.092221

Cited by 6 publications

(3 citation statements)

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“…It should also come with a voice interface to help uneducated people. [25] utilized a graph theory models for developing an Arabic natural interface for an effective information extraction. Generally, the natural language queries have the disadvantage of not providing guidance on accessing the requested data.…”

Section: A Query Based Aggregationmentioning

confidence: 99%

“…• Such keywords are simpler to find in the input sentence that is provided. Bais et al [25] Natural language interface for databases --• Databases only accept structurally formed logical combinations for search queries. • Users don't need to comprehend the database structure because the interface allows them to extract data from databases using Arabic.…”

Section: Herbert Et Al [19]mentioning

confidence: 99%

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A Detailed Study on Aggregation Methods used in Natural Language Interface to Databases (NLIDB)

Kolarkar,

Kumar

2023

IJRITCC

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Historically, databases have been the most crucial issue in the study of information systems, and they constitute an essential part of all information management systems. Since, it complicated due to restricting the number of potential users, particularly non-expert database users who must comprehend the database structure to submit such queries. Natural language interface (NLI), the simplest method to retrieve information, is one possibility for interacting with the database. The transformation of a natural language query into a Structured Query (SQL) in a database is known as a "Natural Language Interface to Database" (NLIDB). This study uses NLIDB to handle the works performed under various aggregations with aggregation functions, a grouping phrase, and a possessing clause. This study carefully examines the numerous systematic aggregation approaches utilized in the NLIDB. This review provides extensive information about the many methods, including query-based, pattern-based, general, keyword-based NLIDB, and grammar-based systems, to extract data for a dissertation from a generic module for use in such systems that support query execution utilizing aggregations.

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Section: A Query Based Aggregationmentioning

confidence: 99%

Section: Herbert Et Al [19]mentioning

confidence: 99%

A Detailed Study on Aggregation Methods used in Natural Language Interface to Databases (NLIDB)

Kolarkar,

Kumar

2023

IJRITCC

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“…This paper is proposing a solution to solve both the language nuance [3] [6] and performance issues with the use of shallow parsing [7], which does not require an understanding of language nuances. The shallow parsing approach being proposed by this paper is the use of keywords [8] to identify characteristics that are important for the search.…”

Section: Introductionmentioning

confidence: 99%

A Shallow Parsing Approach to Natural Language Queries of a Database

Skeggs¹,

Lauria²

2019

JSEA

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The performance and reliability of converting natural language into structured query language can be problematic in handling nuances that are prevalent in natural language. Relational databases are not designed to understand language nuance, therefore the question why we must handle nuance has to be asked. This paper is looking at an alternative solution for the conversion of a Natural Language Query into a Structured Query Language (SQL) capable of being used to search a relational database. The process uses the natural language concept, Part of Speech to identify words that can be used to identify database tables and table columns. The use of Open NLP based grammar files, as well as additional configuration files, assist in the translation from natural language to query language. Having identified which tables and which columns contain the pertinent data the next step is to create the SQL statement.

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The history and recent advances of Natural Language Interfaces for Databases Querying

Majhadi

Machkour

2021

E3S Web Conf.

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Databases have been always the most important topic in the study of information systems, and an indispensable tool in all information management systems. However, the extraction of information stored in these databases is generally carried out using queries expressed in a computer language, such as SQL (Structured Query Language). This generally has the effect of limiting the number of potential users, in particular non-expert database users who must know the database structure to write such requests. One solution to this problem is to use Natural Language Interface (NLI), to communicate with the database, which is the easiest way to get information. So, the appearance of Natural Language Interfaces for Databases (NLIDB) is becoming a real need and an ambitious goal to translate the user’s query given in Natural Language (NL) into the corresponding one in Database Query Language (DBQL). This article provides an overview of the state of the art of Natural Language Interfaces as well as their architecture. Also, it summarizes the main recent advances on the task of Natural Language Interfaces for databases.

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An Arabic natural language interface for querying relational databases based on natural language processing and graph theory methods

Cited by 6 publications

References 0 publications

A Detailed Study on Aggregation Methods used in Natural Language Interface to Databases (NLIDB)

A Detailed Study on Aggregation Methods used in Natural Language Interface to Databases (NLIDB)

A Shallow Parsing Approach to Natural Language Queries of a Database

The history and recent advances of Natural Language Interfaces for Databases Querying

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