Authors : Manikkaarachchi R. N

Volume/Issue : Volume 9 - 2024, Issue 11 - November

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Google Scholar : https://tinyurl.com/97cf8hny

Scribd : https://tinyurl.com/y397d8ra

DOI : https://doi.org/10.38124/ijisrt/IJISRT24NOV873

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : Natural Language Interfaces (NLIs) have significantly improved accessibility to database systems by allowing users to interact using natural language queries rather than complex query languages. This paper examines recent advancements in NLP and machine learning that enhance NLI functionality for database systems, discusses current methodologies and technologies, addresses the major challenges these systems face, and proposes future research directions. NLIs have broad applications, including business intelligence and customer service, where simplifying database access can streamline operations and support data-driven decision-making.

Keywords : Natural Language Interface (NLI), Deep Learning, Contextual Understanding, Conversational AI, Structured Query Language (SQL), ML (Machine Learning)

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