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

Abstract

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.

Assemble geo-analytical questions through a Blockly-based natural language interface

“…NLI applications such as Google search, Siri and Alexa, are widely used in daily life, which facilitate people in searching information and operating devices. In academia, NLIs to databases (NLIDBs) has been extensively studied for the last decades (Majhadi and Machkour, 2021;Nihalani et al, 2011), and NLIs to knowledge graphs start become popular with the advent of Semantic Web (Kaufmann and Bernstein, 2007). Besides, many researchers also focus on domain-tailored NLIs that can handle specialized terms and perform analytics (Yates et al, 2003;Setlur et al, 2016;Yuan et al, 2019).…”