Abstract
This paper is to provide a solution for building Natural Language Interface to Database (NLIDB) using Metadata configuration approach. Nowadays, using systems like Natural Language Interface to Databases aim to provide an effortless way to access enterprise’s database system by ensuring the user need not learn any formal technical languages. Traditionally, NLIDB systems were considered as a dead-end to researchers who used to face immense challenges in building such systems which can easily translate human language to computational linguistics. And the major challenges in Natural language Query processing are endless, including the problems related to inherent ambiguity which a natural language possesses such as interpreting the query correctly, removal of various ambiguity, and mapping to the appropriate context. In this paper, we’ll propose our methodology for NLIDB analytics allowing a computer user having non-technical background to access enterprise database easily. This analysis provides a solid approach to solve relational database queries using NLP techniques through metadata configurations and thereby addressing all the challenges mentioned above.
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Karthik, M.N., Makkar, G. (2021). NLIDB Systems for Enterprise Databases: A Metadata Based Approach. In: Sharma, N., Chakrabarti, A., Balas, V., Martinovic, J. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 1174. Springer, Singapore. https://doi.org/10.1007/978-981-15-5616-6_17
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DOI: https://doi.org/10.1007/978-981-15-5616-6_17
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