ISQNL: Interpretable SQL Query Synthesizer from Natural Language Input

被引：0

作者：

Phal, Shubham Milind ^{[1
]}

Yatish, H. R. ^{[1
]}

Hukkeri, Tanmay Sanjay ^{[1
]}

Natarajan, Abhiram ^{[1
]}

Gonchigar, Prathika ^{[1
]}

Deepamala, N. ^{[1
]}

机构：

[1] RV Coll Engn, Comp Sci & Engn, Bangalore, Karnataka, India

来源：

PROCEEDINGS OF THE 1ST INTERNATIONAL CONFERENCE ON ADVANCED INFORMATION SCIENCE AND SYSTEM, AISS 2019 | 2019年

关键词：

SQL query; Natural Language query; Natural language processing; Tokenization parts of speech tagging; Wordnets; N Grams; Intent Recognition; Entity Recognition;

D O I：

10.1145/3373477.3373478

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页数：6

共 19 条

[1] Aung Sint, 2018, Advances in Science, Technology and Engineering Systems Journal, V3, P218, DOI [10.25046/aj030126, DOI 10.25046/AJ030126]
[2] Carpio Martin, 2018, Studies in Computational Intelligence
[3] Chen HY, 2018, 2018 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP 2018), P4834
[4] Chesworth D, 2016, 2016 IEEE SYSTEMS AND INFORMATION ENGINEERING DESIGN SYMPOSIUM (SIEDS), P233, DOI 10.1109/SIEDS.2016.7489305
[5] Giordani A, 2008, LECT NOTES COMPUT SC, V5039, P367
[6] Intent-Aware Semantic Query Annotation
Glater, Rafael
Santos, Rodrygo L. T.
Ziviani, Nivio
[J]. SIGIR'17: PROCEEDINGS OF THE 40TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, 2017, : 485 - 494
[7] Grefenstette Gregory, 2015, INRIASAC, P19, DOI [10.18653/v1/S15-2152, DOI 10.18653/V1/S15-2152]
[8] Gupta A, 2018, AAAI CONF ARTIF INTE, P5149
[9] Heimark Karl Johan V, 2013, International Journal of Computer Applications, P0975
[10] Natural language aggregate query over RDF data
Hu, Xin
Dang, Depeng
Yao, Yingting
Ye, Luting
[J]. INFORMATION SCIENCES, 2018, 454 : 363 - 381

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