Frameworks for Querying Databases Using Natural Language: A Literature Review – NLP-to-DB Querying Frameworks

Syed Ahmad Chan Bukhari, Hafsa Shareef Dar, M. Ikramullah Lali, Fazel Keshtkar, Khalid Mahmood Malik and Seifedine Kadry
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Syed Ahmad Chan Bukhari: Division of Computer Science, Mathematics and Science, Collins College of Professional Studies, St. John's University, USA
Hafsa Shareef Dar: University of Gujrat, Pakistan
M. Ikramullah Lali: University of Education Lahore, Pakistan
Fazel Keshtkar: Division of Computer Science, Mathematics and Science, Collins College of Professional Studies, St. John's University, USA
Khalid Mahmood Malik: Computer Science and Engineering Department, Oakland University, USA
Seifedine Kadry: Faculty of Applied Computing and Technology, Noroff University College, Kristiansand, Norway

International Journal of Data Warehousing and Mining (IJDWM), 2021, vol. 17, issue 2, 21-38

Abstract: A natural language interface is useful for a wide range of users to retrieve their desired information from databases without requiring prior knowledge of database query language such as SQL. The advent of user-friendly technologies, such as speech-enabled interfaces, have revived the use of natural language technology for querying databases; however, the most relevant and last work presenting state of the art was published back in 2013 and does not encompass several advancements. In this paper, the authors have reviewed 47 frameworks that have been developed during the last decade and categorized the SQL and NoSQL-based frameworks. Furthermore, the analysis of these frameworks is presented on the basis of criteria such as supporting language, scheme of heuristic rules, interoperability support, scope of the dataset, and overall performance score. The study concludes that the majority of frameworks focus on translating natural language queries to SQL and translates English language text to queries.

Date: 2021
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