Structuring Natural Language to Query Language: A Review
Published online first on December 1, 2020.
Corresponding author: B. Nethravathi
Abstract
SQL (Structured Query Language) is a structured language for specialized purposes used to communicate with the data stored in a database management system. It uses dynamic and sophisticated query commands for processing and controlling data in a database, which can become an obstacle for users with no previous experience. In order to address this constraint, we have analyzed the existing models in Natural Language Processing, which convert a native-language query into an SQL query. Thus, any novice user can use the SQL program and eliminate the need to generate any complex queries. This work is a detailed survey of the existing literature.
Keywords:
Structured Query Language, Natural Language Processing, QueryDownloads
References
D. Saha, A. Floratou, K. Sankaranarayanan, U. F. Minhas, A. R. Mittal, and F. Özcan, "ATHENA: an ontology-driven system for natural language querying over relational data stores," Proceedings of the VLDB Endowment, vol. 9, no. 12, pp. 1209-1220, Aug. 2016. DOI: https://doi.org/10.14778/2994509.2994536
F. Brad, R. C. A. Iacob, I. A. Hosu, and T. Rebedea, "Dataset for a Neural Natural Language Interface for Databases (NNLIDB)," in Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Taipei, Taiwan, Nov. 2017, pp. 906-914, Accessed: Nov. 28, 2020. [Online]. Available: https://www.aclweb.org/anthology/I17-1091.
L. Blunschi, C. Jossen, D. Kossman, M. Mori, and K. Stockinger, "SODA: Generating SQL for Business Users," arXiv:1207.0134 [cs], Jun. 2012, Accessed: Nov. 28, 2020. [Online]. Available: http://arxiv.org/abs/1207.0134.
G. Bhalotia, A. Hulgeri, C. Nakhe, S. Chakrabarti, and S. Sudarshan, "Keyword searching and browsing in databases using BANKS," in Proceedings 18th International Conference on Data Engineering, San Jose, CA, USA, Feb. 2002, pp. 431-440.
A.-M. Popescu, O. Etzioni, and H. Kautz, "Towards a theory of natural language interfaces to databases," in Proceedings of the 8th international conference on Intelligent user interfaces, New York, NY, USA, Jan. 2003, pp. 149-157. DOI: https://doi.org/10.1145/604045.604120
F. Basik et al., "DBPal: A Learned NL-Interface for Databases," in Proceedings of the 2018 International Conference on Management of Data, New York, NY, USA, May 2018, pp. 1765-1768. DOI: https://doi.org/10.1145/3183713.3193562
X. Xu, C. Liu, and D. Song, "SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning," arXiv:1711.04436 [cs], Nov. 2017, Accessed: Nov. 28, 2020. [Online]. Available: http://arxiv.org/abs/1711.04436.
J. Berant, A. Chou, R. Frostig, and P. Liang, "Semantic Parsing on Freebase from Question-Answer Pairs," in Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, Seattle, Washington, USA, Oct. 2013, pp. 1533-1544.
A. Kate, S. Kamble, A. Bodkhe, and M. Joshi, "Conversion of Natural Language Query to SQL Query," in 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, Mar. 2018, pp. 488-491. DOI: https://doi.org/10.1109/ICECA.2018.8474639
R. Kumar and M. Dua, "Translating controlled natural language query into SQL query using pattern matching technique," in International Conference for Convergence for Technology-2014, Pune, India, Apr. 2014, pp. 1-5. DOI: https://doi.org/10.1109/I2CT.2014.7092161
A. Iftikhar, E. Iftikhar, and M. K. Mehmood, "Domain specific query generation from natural language text," in 2016 Sixth International Conference on Innovative Computing Technology (INTECH), Dublin, Ireland, Aug. 2016, pp. 502-506. DOI: https://doi.org/10.1109/INTECH.2016.7845105
C. Baik, H. V. Jagadish, and Y. Li, "Bridging the Semantic Gap with SQL Query Logs in Natural Language Interfaces to Databases," 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 374-385, Apr. 2019. DOI: https://doi.org/10.1109/ICDE.2019.00041
N. Yaghmazadeh, Y. Wang, I. Dillig, and T. Dillig, "SQLizer: query synthesis from natural language," Proceedings of the ACM on Programming Languages, vol. 1, no. OOPSLA, p. 63:1-63:26, Oct. 2017. DOI: https://doi.org/10.1145/3133887
L. Dong and M. Lapata, "Language to Logical Form with Neural Attention," arXiv:1601.01280 [cs], Jun. 2016, Accessed: Nov. 28, 2020. [Online]. Available: http://arxiv.org/abs/1601.01280.
R. Ge and R. Mooney, "A Statistical Semantic Parser that Integrates Syntax and Semantics," in Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005), Ann Arbor, Michigan, Jun. 2005, pp. 9-16, Accessed: Nov. 28, 2020. [Online]. Available: https://www.aclweb.org/anthology/W05-0602. DOI: https://doi.org/10.3115/1706543.1706546
D. Shah and D. Vanusha, "Optimizing Natural Language Interface for Relational Database," International Journal of Engineering and Advanced Technology, vol. 8, no. 4, pp. 131-135, Apr. 2019.
C. Sun, "A Natural Language Interface for Querying Graph Databases," M.S. thesis, Massachusetts Institute of Technology, 2018.
H. Bais, M. Machkour, and L. Koutti, "A Model of a Generic Natural Language Interface for Querying Database," International Journal of Intelligent Systems and Applications, vol. 8, no. 2, pp. 35-44, Feb. 2016. DOI: https://doi.org/10.5815/ijisa.2016.02.05
J.-H. Tao, J. Huang, Y. Li, Z. Lian, and M.-Y. Niu, "Semi-supervised Ladder Networks for Speech Emotion Recognition," International Journal of Automation and Computing, vol. 16, no. 4, pp. 437-448, Aug. 2019. DOI: https://doi.org/10.1007/s11633-019-1175-x
B.-T. Zhang, X.-P. Wang, Y. Shen, and T. Lei, "Dual-modal Physiological Feature Fusion-based Sleep Recognition Using CFS and RF Algorithm," International Journal of Automation and Computing, vol. 16, no. 3, pp. 286-296, Jun. 2019. DOI: https://doi.org/10.1007/s11633-019-1171-1
N. R. Nayak, P. K. Dash, and R. Bisoi, "A Hybrid Time Frequency Response and Fuzzy Decision Tree for Non-stationary Signal Analysis and Pattern Recognition," International Journal of Automation and Computing, vol. 16, no. 3, pp. 398-412, Jun. 2019. DOI: https://doi.org/10.1007/s11633-018-1113-3
H. Sasaki, S. Yamamoto, A. Agchbayar, and Ν. Nkhbayasgalan, "Extracting Problem Linkages to Improve Knowledge Exchange between Science and Technology Domains using an Attention-based Language Model," Engineering, Technology & Applied Science Research, vol. 10, no. 4, pp. 5903-5913, Aug. 2020. DOI: https://doi.org/10.48084/etasr.3598
S. Khalid and S. Wu, "Supporting Scholarly Search by Query Expansion and Citation Analysis," Engineering, Technology & Applied Science Research, vol. 10, no. 4, pp. 6102-6108, Aug. 2020. DOI: https://doi.org/10.48084/etasr.3655
M. Alsuwaiket, A. H. Blasi, and K. Altarawneh, "Refining Student Marks based on Enrolled Modules' Assessment Methods using Data Mining Techniques," Engineering, Technology & Applied Science Research, vol. 10, no. 1, pp. 5205-5210, Feb. 2020. DOI: https://doi.org/10.48084/etasr.3284
S. R. Basha and J. K. Rani, "A Comparative Approach of Dimensionality Reduction Techniques in Text Classification," Engineering, Technology & Applied Science Research, vol. 9, no. 6, pp. 4974-4979, Dec. 2019. DOI: https://doi.org/10.48084/etasr.3146
Downloads
How to Cite
License
Copyright (c) 2020 Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
