ISQNL: interpretable SQL query synthesizer from natural language input
Authors: 
Shubham Milind Phal, Yatish H R, Tanmay Sanjay Hukkeri, Abhiram Natarajan, Prathika Gonchigar, Deepamala NAuthors Info & Claims
Shubham Milind Phal, Yatish H R, Tanmay Sanjay Hukkeri, Abhiram Natarajan, Prathika Gonchigar, Deepamala NAuthors Info & ClaimsAISS '19: Proceedings of the 1st International Conference on Advanced Information Science and System
Article No.: 1, Pages 1 - 6
Abstract
Databases serve as the forefront for most systems today. Structured query language (SQL) is used to access and manipulate the data stored in a relational database. However, most end users have limited knowledge of SQL and thus face difficulties in accessing such systems. In this paper we describe a novel system (ISQNL) to convert a query provided in Natural Language (English) to an SQL query. By applying several natural language processing techniques ISQNL achieves this conversion without the need for any elaborate schema specific training/modification during setup and is robust enough to handle dynamically changing database states or database schema. ISQNL has demonstrated remarkable accuracy in SQL query synthesis when tested on large sets of natural language input. This paper discusses the methodology and key challenges involved in building ISQNL.
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Index Terms
- ISQNL: interpretable SQL query synthesizer from natural language input
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Published In
November 2019
253 pages
ISBN:9781450372916
DOI:10.1145/3373477
- Conference Chairs:
- Alexander Balinsky,
- Ah-Hwee Tan,
- Wernhuar Tarng
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Published: 15 January 2020
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AISS 2019
AISS 2019: 2019 International Conference on Advanced Information Science and System
November 15 - 17, 2019
Singapore, Singapore
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AISS '19 Paper Acceptance Rate 41 of 95 submissions, 43%;
Overall Acceptance Rate 41 of 95 submissions, 43%
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