Question to Query: Converting Human Language to DBMS Query
Yashvi Thakkar1, Faiz Palwala2, Utsav Vyas3, Krati Agarwal4, Rajesh Kannan Regunathan5
1Yashvi Thakkar, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore.
2Faiz Palwala, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore.
3Utsav Vyas, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore.
4Krati Agarwal, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore.
5Rajesh Kannan Regunathan, School of Computer Science and Engineering Vellore Institute of Technology, Vellore.
Manuscript received on November 14, 2019. | Revised Manuscript received on December 22, 2019. | Manuscript published on December 30, 2019. | PP: 1371-1377 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B2635129219/2020©BEIESP | DOI: 10.35940/ijeat.B2635.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper a method has been proposed keeping in the mind the need for systems that could generate structured queries from normal language keeping in mind that the user has no prior knowledge of database query language. A novel method which aims at aiding analyst who aren’t well versed with codes, but need quantitative outputs to analyze, predict and alert the business or market. A python model is used, which aims at converting any sentence typed in English to a query provided that such tables and database is present for query processing. Tree tagging is used here to relate words typed in to SQL query syntax. Any sentence typed in by analyst, it further annotated by parts of speech and lemmas. A list of generic words and stop words is used while parsing the input the sentence and tagging it. Query is generated by simultaneously removing the stop words, mapping the keywords with the one’s used in structured query language. The generated query comes out in form of a JSON file.
Keywords: Complex SQL generation, Natural Language Processing, Query parsing, Structured query language, Tree tagging.