AI Knowledge Sharing Web Portal
DOI:
https://doi.org/10.15662/IJARCST.2026.0903004Keywords:
Artificial Intelligence, Natural Language Processing (NLP), Knowledge Sharing, Question Answering System, Web Portal, AI Evaluation, Data Analytics, Chatbot SystemAbstract
The AI-Powered Knowledge Sharing Web Portal is designed to provide an intelligent platform for effective knowledge exchange and query resolution. Traditional knowledge-sharing systems often lack efficiency, accuracy, and real-time interaction. To overcome these limitations, the proposed system integrates Artificial Intelligence to enhance the overall user experience and improve the quality of responses. The system allows users to register, log in, ask questions, and share answers in a collaborative environment. The Question & Answer Management module handles the storage and retrieval of user queries and responses. The AI Evaluation module plays a key role by analyzing multiple answers and selecting the most relevant and accurate response based on parameters such as relevance, clarity, and correctness. Additionally, the Admin and Analytics module enables system monitoring, user management, and content moderation, ensuring data integrity and system reliability. The platform is designed with a scalable architecture that supports real-time interaction and secure data handling. Overall, the proposed system provides an efficient, reliable, and user-friendly solution for knowledge sharing by combining AI-based automation, intelligent evaluation, and collaborative learning features. It is suitable for educational and professional environments where quick and accurate information exchange is essential.
References
1. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Pearson Education, 2020. [Online]. Available: https://aima.cs.berkeley.edu/
2. D. Jurafsky and J. H. Martin, Speech and Language Processing. Pearson, 2021. [Online]. Available: https://web.stanford.edu/~jurafsky/slp3/
3. C. D. Manning, P. Raghavan, and H. Schütze, Introduction to Information Retrieval. Cambridge University Press, 2008. [Online]. Available: https://nlp.stanford.edu/IR-book/
4. Vaswani et al., “Attention Is All You Need,” in Advances in Neural Information Processing Systems (NeurIPS), 2017. [Online]. Available: https://arxiv.org/pdf/1706.03762.pdf
5. J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” in NAACL-HLT, 2019. [Online]. Available: https://arxiv.org/pdf/1810.04805.pdf
6. Z. Yang et al., “XLNet: Generalized Autoregressive Pretraining for Language Understanding,” in NeurIPS, 2019. [Online]. Available: https://arxiv.org/pdf/1906.08237.pdf
7. Y. Goldberg, Neural Network Methods for Natural Language Processing. Morgan & Claypool, 2017. [Online]. Available: https://www.morganclaypool.com/doi/abs/10.2200/S00762ED1V01Y201703HLT037
8. C. C. Aggarwal, Machine Learning for Text. Springer, 2018. [Online]. Available: https://link.springer.com/book/10.1007/978-3-319-73531-3
9. B. Liu, Sentiment Analysis and Opinion Mining. Morgan & Claypool, 2012. [Online]. Available: https://www.cs.uic.edu/~liub/FBS/SentimentAnalysis-and-OpinionMining.pdf
10. D. Chen, A. Fisch, J. Weston, and A. Bordes, “Reading Wikipedia to Answer Open-Domain Questions,” in ACL, 2017. [Online]. Available: https://arxiv.org/pdf/1704.00051.pdf
11. R. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval. Addison-Wesley, 2011. [Online]. Available: https://www.mir2ed.org/
12. G. Salton and M. J. McGill, Introduction to Modern Information Retrieval. McGraw-Hill, 1986. [Online]. Available: https://dl.acm.org/doi/book/10.5555/109875
13. C. Shah, Collaborative Information Seeking. Springer, 2015. [Online]. Available: https://link.springer.com/book/10.1007/978-3-319-18988-8
14. L. Mamykina, B. Manoim, M. Mittal, G. Hripcsak, and B. Hartmann, “Design Lessons from Stack Overflow,” in CHI Conference, 2011. [Online]. Available: https://dl.acm.org/doi/10.1145/1978942.1979276
15. Google, “Search Quality Evaluator Guidelines,” 2022. [Online]. Available: https://developers.google.com/search/blog/2022/12/google-search-quality-evaluator-guidelines
16. IEEE, “IEEE Standards for Artificial Intelligence Systems,” 2021. [Online]. Available: https://standards.ieee.org/initiatives/artificial-intelligence-systems/
17. Association for Computing Machinery, “ACM Digital Library – Question Answering Systems,” 2020. [Online]. Available: https://dl.acm.org/
18. Seedha Devi, V., Selvi, D., Uma Maheshwari, K., & Yuvashree, G. (2026). Food linker: A smart system for global waste reduction. International Journal of Engineering & Extended Technologies Research (IJEETR), 8(3), 5012–5021. https://doi.org/10.15662/IJEETR.2026.0803002
19. Mathew, A., & Romasco, L. (2024). Forensic Investigation of Artificial Intelligence Systems. Research Updates in Mathematics and Computer Science Vol. 4, 154-164.
20. Sugumar, R. (2025). Designing Resilient and Scalable Cloud-Native Frameworks for Generative AI Content Production. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(6), 13268-13279.
21. Rajasekar, M., Celine Kavida, A., & Anto Bennet, M. (2020). A pattern analysis based underwater video segmentation system for target object detection. Multidimensional Systems and Signal Processing, 31(4), 1579-1602.
22. Seedha Devi, V., Namitha, B., Divya Dharshini, J., & Livetha, K. (2026). A hybrid biometric and geo-fencing based smart attendance system. International Journal of Advanced Research in Computer Science and Technology (IJARCST), 9(3), 794–802. https://doi.org/10.15662/IJARCST.2026.0903002
23. Poornima, G., & Anand, L. (2025). Medical image fusion model using CT and MRI images based on dual scale weighted fusion based residual attention network with encoder-decoder architecture. Biomedical Signal Processing and Control, 108, 107932.
24. Vani, S., Malathi, P., Ramya, V. J., Sriman, B., Saravanan, M., & Srivel, R. (2024). An efficient black widow optimization-based faster R-CNN for classification of COVID-19 from CT images. Multimedia Systems, 30(2), 108.
25. Narayanan, L. K., Loganayagi, S., Hemavathi, R., Jayalakshmi, D., & Vimal, V. R. (2024, March). Machine learning-based predictive maintenance for industrial equipment optimization. In 2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies (pp. 1-5). IEEE.
26. Alangaram, S., Praveen, S., Rajesh, V., & Sanjai, A. (2026). Sales guard AI-driven decision intelligence platform for business optimization. International Journal of Engineering & Extended Technologies Research (IJEETR), 8(3), 5022–5031. https://doi.org/10.15662/IJEETR.2026.0803003
27. Sharma, K. P., Kumar, I., Singh, P. P., Anbazhagan, K., Albarakati, H. M., Bhatt, M. W., ... & Rana, A. (2024). Advancing spacecraft rendezvous and docking through safety reinforcement learning and ubiquitous learning principles. Computers in Human Behavior, 153, 108110.
28. Seedha Devi, V., Harshini, R., Dhana Lakshmi, E., Gayathri, N., & Nithesha, P. (2026). Low-code mobile application builder with AI-assisted features using Flutter & Firebase. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 9(3), 1001–1010.
29. Socrates, S., Shanmugapriya, M., Murugeshwari, B., & Angalaeswari, S. (2024). Efficient Design for Implantable Device Constant Current Induction Doubly Fed Generating Incorporating Grid Connectivity. In Intelligent Solutions for Sustainable Power Grids (pp. 382-392). IGI Global Scientific Publishing.
30. Seedha Devi, V., Harshini, R., Dhana Lakshmi, E., Gayathri, N., & Nithesha, P. (2026). Low-code mobile application builder with AI-assisted features using Flutter & Firebase. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 9(3), 1001–1010. https://doi.org/10.15662/IJRPETM.2026.0903001
31. Deivendran, P., Babu, P. S., Malathi, G., Anbazhagan, K., & Kumar, R. S. (2023). Emotion Recognition for Challenged People Facial Appearance in Social using Neural Network. arXiv preprint arXiv:2305.06842.
32. Seedha Devi, V., Divya Narasimman, S., Jayaprakash, S., & Mohamed Suhel, H. N. (2026). Smart IoT-based pedestrian power generator using DC motor. International Journal of Computer Technology and Electronics Communication (IJCTEC), 9(3), 990–999. https://doi.org/10.15680/IJCTECE.2026.0903002
33. Vimal, V. R., Anandan, P., & Induja, V. (2024). Estimating the perspicacious features of ECG recording based on template classification for detecting atrial fibrillation. International Journal of Advanced Intelligence Paradigms, 29(1), 17-27.
34. Archana, R., & Anand, L. (2025). Residual u-net with Self-Attention based deep convolutional adaptive capsule network for liver cancer segmentation and classification. Biomedical Signal Processing and Control, 105, 107665.
35. Kaliappan, S., Ragunthar, T., Ali, M., & Murugeshwari, B. (2024). Implementation of Virtual High Speed Data Transfer in Satellite Communication Systems Using PLC and Cloud Computing. In AI Approaches to Smart and Sustainable Power Systems (pp. 274-286). IGI Global Scientific Publishing.
36. Seedha Devi, V., Mahalakshimi, P. V., & Anitha, A. (2026). Automated skin disease analysis and detection using AI-powered mobile application. International Journal of Research and Applied Innovations (IJRAI), 9(3), 531–539. https://doi.org/10.15662/IJRAI.2026.0903004
37. Mathew, D. A. (2024). Time-triggered ethernet (ttethernet) and artificial Intelligence. International Journal of Development Research, 14.
38. Revathi, K. G., Ananth, B. J., Saravanan, M. L., & Kumar, A. R. (2021). Gps enabled vehicle location identification using gsm and fare collection using smart card. Turkish journal of computer and mathematics education, 12(10), 2657-2668.
39. Sugumar, R. (2025). Federated AI in Offline-First Mobile Health Architectures for Privacy-Preserving Clinical Intelligence. International Journal of Science, Research and Technology, 8(4), 14589-14600.
40. Rajasekar, M., Aruldoss, A. C., & Bennet, M. A. (2018). A novel method to detect corrosion in underwater infrastructure using an image processing. ARPN Journal of Engineering and Applied Science, 13(7), 2556-2561.
41. Alangaram, S., Udaykiran, M., Rajkumar, K., & Yogeeswaran, T. (2026). Enhancing customer churn prediction and retention for e-commerce. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 9(3), 803–813. https://doi.org/10.15662/IJARCST.2026.0903003
