Current Volume 9
Abstract
With the rapid growth of digital information, efficient retrieval of relevant data has become increasingly important. Traditional database systems rely on structured query languages such as SQL to retrieve data. However, many users lack the technical knowledge required to write complex database queries. Natural Language Processing (NLP) and Artificial Intelligence (AI) provide a promising solution by enabling users to interact with databases using natural language queries. This research paper proposes an AI-powered NLP query engine that allows users to retrieve information using simple natural language inputs. The system processes user queries through NLP techniques such as tokenization, intent detection, and semantic analysis. Based on the interpreted query, the system either converts the query into SQL for structured databases or performs semantic search for unstructured data. This approach simplifies data retrieval and improves accessibility for non-technical users. The proposed system demonstrates how AI-driven query processing can enhance user interaction with databases and improve the efficiency of information retrieval systems.
Keywords
Artificial Intelligence, Natural Language Processing, Semantic Search, Query Processing, Text-to-SQL, Data Retrieval Systems.
Citations
IRE Journals:
Edwin Vettikattil, Prajwal Dikshit, Rashmi Pathak "AI-Powered NLP Query Engine for Intelligent Data Retrieval" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 3343-3350 https://doi.org/10.64388/IREV9I10-1716940
IEEE:
Edwin Vettikattil, Prajwal Dikshit, Rashmi Pathak
"AI-Powered NLP Query Engine for Intelligent Data Retrieval" Iconic Research And Engineering Journals, 9(10) https://doi.org/10.64388/IREV9I10-1716940
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
Important Links
For Authors
Volume 8:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 7:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 6:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 5:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 4:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 3:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12