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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Enhancement of Natural Language to SQL Query Conversion using Machine Learning Techniques

Author 1: Akshar Prasad
Author 2: Sourabh S Badhya
Author 3: Yashwanth YS
Author 4: Shetty Rohan
Author 5: Shobha G
Author 6: Deepamala N

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2020.0111260

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 12, 2020.

  • Abstract and Keywords
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Abstract: In the age of information explosion, there is a huge data that is stored in the form of database and accessed using various querying languages. The major challenges faced by a user accessing this data is to learn the querying language and understand the various syntax associated with it. Query given in the form of Natural Language helps any naïve user to access database without learning the query languages. The current process of conversion of Natural Language to SQL Query using a rule-based algorithm is riddled with challenges -- identification of partial or implied data values and identification of descriptive values being the predominant ones. This paper discusses the use of a synchronous combination of a hybrid Machine Learning model, Elastic Search and WordNet to overcome the above-mentioned challenges. An embedding layer followed by a Long Short-Term Memory model is used to identify partial or implied data values, while Elastic Search has been used to identify descriptive data values (values which have lengthy data values and may contain descriptions). This architecture enables conversion systems to achieve robustness and high accuracies, by extracting meta data from the natural language query. The system gives an accuracy of 91.7% when tested on the IMDb database and 94.0% accuracy when tested on Company Sales database.

Keywords: Machine learning; natural language to SQL query; long short-term memory; embedding layer; elastic search; hybrid architecture

Akshar Prasad, Sourabh S Badhya, Yashwanth YS, Shetty Rohan, Shobha G and Deepamala N, “Enhancement of Natural Language to SQL Query Conversion using Machine Learning Techniques” International Journal of Advanced Computer Science and Applications(IJACSA), 11(12), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0111260

@article{Prasad2020,
title = {Enhancement of Natural Language to SQL Query Conversion using Machine Learning Techniques},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0111260},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0111260},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {12},
author = {Akshar Prasad and Sourabh S Badhya and Yashwanth YS and Shetty Rohan and Shobha G and Deepamala N}
}


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