An intelligent automatic query generation interface for relational databases using deep learning technique
Abstract
In the emerging information retrieval world, the selection of keywords and the generation of queries are very critical for the efficient retrieval. The most new-generations database applications request keen interface to upgrade productive connections among various databases and the clients. Majority of the accessible interfaces for databases should be intelligent and also should understand natural language expressions. The paper deals with, the mapping of natural language queries which is in spoken form, and converting into words forming the foundation of SQL. We give a general framework for a smart database interface which could be linked to any database. One of the most striking features of this interface is domain-independence. The smart interface employs speech recognition techniques to convert spoken language input into text. Then a semantic matching technique is employed in converting natural language query to SQL words, complemented by using dictionary and a set of production rules. The dictionary consists of semantics sets for columns and tables. The identified type of query, query-word tuple is memorized and sent through a convolution neural network for better construction. This work tries to use natural language processing techniques, parsing and POS tagging to identify semantic structures of inputs. The natural language techniques are used to increase the production of unambiguous queries and speed in detecting and identifying the important parts in a query.
References
[1]
Anderson, D., & Hills, M. (2017, February). Query construction patterns in PHP. In: IEEE 24th international conference on software analysis, evolution and reengineering (SANER), 2017 (pp. 452–456). IEEE.
[2]
Brodie, M. L. (1988). Future intelligent information systems: AI and database technologies working together. In: Readings in artificial intelligence and databases (pp. 623–641).
[3]
Celko, J., Burleson, D. K., & Cook, J. P. (2015). Advanced SQL database programmer handbook. Kittrell: Rampant Tech.
[4]
Chan, T. H., Ho, S. W., & Yamamoto, H. (2015, June). Private information retrieval for coded storage. In: IEEE international symposium on information theory (ISIT), 2015 (pp. 2842–2846). IEEE.
[5]
Charniak, E., & Johnson, M. (2005, June). Coarse-to-fine n-best parsing and MaxEnt discriminative reranking. In: Proceedings of the 43rd annual meeting on association for computational linguistics (pp. 173–180). Association for Computational Linguistics.
[6]
Demidova, E., Zhou, X., & Nejdl, W. (2012). A probabilistic scheme for keyword-based incremental query construction. IEEE Transactions on Knowledge and Data Engineering, 24(3), 426.
[7]
Greene, B. B., & Rubin, G. M. (1971). Automatic grammatical tagging of English. Technical report, Department of Linguistics, Brown University, Providence, Rhode Island.
[8]
Guo, T., Dong, J., Li, H., & Gao, Y. (2017, March). Simple convolutional neural network on image classification. In: IEEE 2nd international conference on big data analysis (ICBDA) (pp. 721–724).
[9]
Gupta, S., Mohanta, S., Chakraborty, M., & Ghosh, S. (2017, August). Quantum machine learning-using quantum computation in artificial intelligence and deep neural networks: Quantum computation and machine learning in artificial intelligence. In: 8th annual industrial automation and electromechanical engineering conference (IEMECON), 2017 (pp. 268–274). IEEE.
[10]
Iftikhar, A., Iftikhar, E., & Mehmood, M. K., (2016, August). Domain specific query generation from natural language text. In: Sixth international conference on innovative computing technology (INTECH), 2016 (pp. 502–506). IEEE.
[11]
Kasmaji, A. N. S., & Purwarianti, A. (2015, August). Employing natural language processing to analyse grammatical error in a simple Japanese sentence. In: international conference on electrical engineering and informatics (ICEEI), 2015 (pp. 82–86). IEEE.
[12]
Kupiec, J. (1992). Robust part-of-speech tagging using a hidden Markov model. Computer Speech and Language, 6(3), 225–242.
[13]
Ma, Z. (2007). Intelligent databases: Technologies and applications. Pennsylvania: IGI Global.
[14]
McKay, D. P., Finin, T., & O’Hare, A. (1990, August). The intelligent database interface: Integrating AI and database systems. In: Proceedings of the 1990 national conference on artificial intelligence.
[15]
Nguyen, D. Q., & Verspoor, K. (2018). An improved neural network model for joint POS tagging and dependency parsing. arXiv preprint arXiv:1807.03955.
[16]
Nguyen, D. Q., & Verspoor, K. (2019). From POS tagging to dependency parsing for biomedical event extraction. BMC Bioinformatics, 20(1), 72.
[17]
Rabiner, L. R., & Juang, B. H. (1986). An introduction to hidden Markov models. IEEE ASSP Magazine, 3(1), 4–6.
[18]
Ruparel, B. (1990). Designing and implementing intelligent database applications-a case study, Bethesda, MD. In: Proceedings [1990] IEEE conference on managing expert system programs and projects (pp. 223–229).
[19]
Sangeetha, J., & Jothilakshmi, S. (2017). Speech translation system for English to dravidian languages. Applied Intelligence, 46(3), 534–550.
[20]
Sontakke, A. R., & Pimpalkar, A. (2014). A rule based graphical user interface to relational database using NLP. Interaction, 5, 6.
[21]
Trinkunas, J., & Vasilecas, O. (2009). Ontology transformation: From requirements to conceptual model. Scientific Papers, University of Latvia, Computer Science and Information Technologies, 751, 52–64.
[22]
Wolfram, D. (2013, October). Applications of SQL for informetric data processing. In: Proceedings of the annual conference of CAIS/Actes du congrès annuel de l’ACSI.
[23]
Yadav, P. K., & Rizvi, S. A. M. (2016). Ghaziabad, Randomized algorithm for optimal query plan generation for distributed environments. IEEE.
[24]
Yang, L., Zhang, M., Liu, Y., Sun, M., Yu, N., & Fu, G. (2018). Joint POS tagging and dependence parsing with transition-based neural networks. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), 26(8), 1352–1358.
[25]
Zhu, R., Mao, X. J., Zhu, Q. H., Li, N., & Yang, Y. B. (2016, September). Text detection based on convolutional neural networks with spatial pyramid pooling. In: ICIP (pp. 1032–1036).
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Published: 01 September 2019
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