AI-Powered NLP Query Engine for Intelligent Data Retrieval

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@article{1716940,
      author = {Edwin Vettikattil, Prajwal Dikshit, Rashmi Pathak},
      title = {AI-Powered NLP Query Engine for Intelligent Data Retrieval},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {10},
      pages = {3343-3350},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1716940.pdf},
      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.},
      month = {April},
      doi = {https://doi.org/10.64388/IREV9I10-1716940}
  }