Full Text PDF
Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
Vol 10 No 5 (2026): Volume 10, Issue 5, May 2026 | Pages: 260-270
International Research Journal of Innovations in Engineering and Technology
OPEN ACCESS | Research Article | Published Date: 13-05-2026
Omniverse AI addresses this by presenting a unified, intelligent platform that transforms unstructured web data into structured and queryable information. The system enables users to input a website URL, automatically extracting relevant content through advanced web-scraping algorithms. The retrieved data is securely stored in a centralized database for efficient management and analysis. An integrated AI-driven chatbot allows users to interact with the stored data using natural language queries. Instead of manually searching websites, users obtain accurate and context-aware responses generated directly from the trained model. By combining web scraping, intelligent data processing, and conversational artificial intelligence, Omniverse AI bridges the gap between raw online data and actionable knowledge. This results in an intelligent system capable of extracting and structuring web data, storing it securely for analysis, and training AI models to generate accurate insights. Additionally, an integrated AI chatbot will enable users to interact with the data through natural, conversational queries, making information retrieval more efficient and user-friendly.
Artificial Intelligence, Web Scraping, Natural Language Processing, Data Management, Information Retrieval, Chatbot System.
Varun Patel, Roshani Chaudhari, Bhavesh Y. Patil, Bhavesh R. Patil, & Rijavan A. Shaikh. (2026). Omniverse AI: The Universal Web Intelligence Platform Using AI. International Research Journal of Innovations in Engineering and Technology - IRJIET, 10(5), 260-270. Article DOI https://doi.org/10.47001/IRJIET/2026.105036
This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
V. Crescenzi, “Automatic Web Data Extraction Using DOM Tree,” 2001.
J. Srivastava, “Web Content Mining: Tools, Techniques and Applications,” 2000.
N. Kushmerick, “Information Extraction from Web Documents Using Machine Learning,” 1997.
Y. LeCun and Y. Bengio, “Deep Learning for Natural Language Processing,” 2015.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” 2018.
J. Masche and N. Le, “A Survey on Chatbot Systems,” 2017.
R. Feldman, “Text Mining and Analysis Techniques,” 2007.
“Scrapy: A Web Crawling Framework,” 2015. [Online]. Available: https://docs.scrapy.org
“Selenium for Dynamic Web Scraping,” 2018. [Online]. Available: https://www.selenium.dev
O. Kolomiyets and M.-F. Moens, “A Survey on Question Answering Systems,” 2011.
C. D. Manning, P. Raghavan, and H. Schütze, *Introduction to Information Retrieval*, Cambridge University Press, 2008.
X. Chu et al., “Machine Learning Approaches for Data Cleaning,” 2016.
J. Dean and S. Ghemawat, “Big Data Analytics for Web Data,” 2014.
P. Mika, “Knowledge Extraction from Web Data,” 2012.
D. Jurafsky and J. H. Martin, “Natural Language Understanding in AI Systems,” 2019.
S. García, J. Luengo, and F. Herrera, “Data Preprocessing Techniques in Machine Learning,” 2015.
A.Sweigart, “Web Automation using Python,” 2020.
S. Shawar and E. Atwell, “AI-based Chatbot for Information Retrieval,” 2021.
H. Chen et al., “Hybrid Models for Intelligent Data Processing,” 2022.