Zobraziť v EDS

An SQL query generator for cross-domain human language based questions based on NLP model.

Uložené v:
Podrobná bibliografia
Názov: An SQL query generator for cross-domain human language based questions based on NLP model.
Autori: Naik, B. Balaji, Reddy, T. Jaya Venkata Rama, karthik, K. Rohith Venkata, Kuila, Pratyay
Zdroj: Multimedia Tools & Applications; Jan2024, Vol. 83 Issue 4, p11861-11884, 24p
Predmety: SQL, HUMAN beings
Abstrakt: The amount of data generated in the modern world is so great that data lakes are now being used to store data. However, relational databases are currently the primary repository for the world's data. However, it is very time-consuming for a user to type each query every time, especially the queries that include complex keywords. Our proposed approach uses the interaction history by altering the preceding projected query to improve the generation quality, based on the finding that successive human language queries are frequently lin- guistically dependent, and their equivalent SQL queries overlap. This paper focuses on text-to-SQL conversion for cross-domain datasets. Our approach reuses results produced at the token level and considers SQL statements as sequences. Finally, we evaluate our approach on different datasets like the Sparc, Spider, and CoSQL datasets. It compared our proposed approach with existing famous algorithms like Seq2seq, and added attention and copying to the seq2seq model, SQLNet model, and TypeSQL model in terms of accuracy and F1 score. [ABSTRACT FROM AUTHOR]
Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Databáza: Complementary Index
Popis
Abstrakt:The amount of data generated in the modern world is so great that data lakes are now being used to store data. However, relational databases are currently the primary repository for the world's data. However, it is very time-consuming for a user to type each query every time, especially the queries that include complex keywords. Our proposed approach uses the interaction history by altering the preceding projected query to improve the generation quality, based on the finding that successive human language queries are frequently lin- guistically dependent, and their equivalent SQL queries overlap. This paper focuses on text-to-SQL conversion for cross-domain datasets. Our approach reuses results produced at the token level and considers SQL statements as sequences. Finally, we evaluate our approach on different datasets like the Sparc, Spider, and CoSQL datasets. It compared our proposed approach with existing famous algorithms like Seq2seq, and added attention and copying to the seq2seq model, SQLNet model, and TypeSQL model in terms of accuracy and F1 score. [ABSTRACT FROM AUTHOR]
ISSN:13807501
DOI:10.1007/s11042-023-15731-0