KESMR: A Knowledge Enrichment Semantic Model For Recommending Microblogs
Algorithm; Semantic Heterogeneity; Semantic Web; Statistical Techniques; Web 3.0
In today's world, there's an enormous amount of information available on the Internet. Because of this, it's become really important to come up with better and smarter ways to search for things online. The old-fashioned methods, like just matching certain words or using statistics, don't work so well anymore. They often suggest web pages that are irrelevant. As the Semantic Web keeps getting bigger, it needs algorithms for the best fit. In this paper, a way to measure how different the words used for web search. This helps in suggesting the most relevant web pages. A special algorithm that can change its settings. Our proposed method demonstrates 94% accuracy. 2023 IEEE.
Ranjan H.; Mishra S.; Al-Khasawneh M.A.; Singhal M.; Sharma V.; Alkhayyat A.
2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023, pp. 669-674.
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/UPCON59197.2023.10434773 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187316697&doi=10.1109%2fUPCON59197.2023.10434773&partnerID=40&md5=bb9e587ae014d07aa1c6da63cd74dfec
Ranjan H., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Mishra S., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Al-Khasawneh M.A., School of Computing, Skyline University College, University City Sharjah, Sharjah, 1797, United Arab Emirates; Singhal M., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Sharma V., Department of Computational Sciences Christ (Deemed to Be University), Delhi, India; Alkhayyat A., College of Technical Engineering, The Islamic University, Najaf, Iraq