As for the Internet is grown this much and the massive data of social networks accounts have been accumulated about users’ behavior on digital platforms, it is found that this data require to be derived on efficient way to increase the personalization for improving the internet user experience. This paper introduces an ontology-based architecture that extracts user’s data from multiple sources such as websites, applications and social networks accounts. The data is then preprocessed and used for developing the domain ontology, which facilitates the structure of user concepts and allows for efficient data integration and intelligent reasoning. The proposed architecture makes the domain ontology applicable in personalized digital marketing, digital cloning, AI (Artificial Intelligence) based virtual assistants and other user-driven applications. In addition, it takes privacy and ethical concerns into account, where user consent mechanisms, data anonymization techniques and compliance with regulations like the General Data Protection Regulation (GDPR) can be utilized. By incorporating such considerations, the architecture ensures responsible use of the collected data for maximum contribution to the digital services, and prevents any form of exploitation against internet users.

[1] Alromema W. and Alahmadi A., “Ontology Building for Patient Bioinformatics of the Smart Card Domain: Implementation Using OWL,” International Journal of Cloud Computing, vol. 11, no. 4, pp. 316-329, 2022. https://doi.org/10.1504/IJCC.2022.124796

[2] Andrades J., Rodriguez I., Benavides C., Moreton H., and Gayo J., “An Ontology-Based Multi- Enhancing Social Networks Services: An Ontology-Based Approach 281 Domain Architecture in Social Networks Analysis: Experimental Validation and Case Study,” Information Sciences, vol. 540, pp. 390-413, 2020. https://doi.org/10.1016/j.ins.2020.06.008

[3] Arafeh M., Ceravolo P., Mourad A, Damiani E., and Bellini E., “Ontology Based Recommender System Using Social Networks Data,” Future Generation Computer Systems, vol. 115, no. 1, pp. 769-779, 2021. https://doi.org/10.1016/j.future.2020.09.030

[4] Bendjamaa F. and Taleb N., “OntoDin: An Islamic Ontology of Quran and Hadith,” The International Arab Journal of Information Technology, vol. 21, no. 5, pp. 773-785, 2024. DOI: https://doi.org/10.34028/iajit/21/5/1

[5] Caon M., “Designing Systems in the Digital Immortality Era,” in Proceedings of the ACM Conference Companion Publication on Designing Interactive Systems, Hong Kong, pp. 237-241, 2018. https://doi.org/10.1145/3197391.3205442

[6] Deng L., Liu B., and Li Z., “Multimodal Sentiment Analysis Based on a Cross-Modal Multihead Attention Mechanism,” Computers, Materials and Continua, vol. 78, no. 1, pp. 1157- 1170, 2024. https://doi.org/10.32604/cmc.2023.042150

[7] Dwivedi Y., Ismagilova E., Hughes D., Carlson J., and et al., “Setting the Future of Digital and Social Networks Marketing Research: Perspectives and Research Propositions,” International Journal of Information Management, vol. 59, pp. 102-168, 2021. https://doi.org/10.1016/j.ijinfomgt.2020.102168

[8] Faroukhi A., El Alaoui I., Gahi Y., and Amine A., “Big Data Monetization Throughout Big Data Value Chain: A Comprehensive Review,” Journal of Big Data, vol. 7, no. 1, pp. 1-22, 2020. https://doi.org/10.1186/s40537-019-0281-5

[9] Galvao V. and Maciel C., “The Acceptability of Digital Immortality: Today’s Human is Tomorrow's Avatar,” in Proceeding of the 16th Brazilian Symposium on Human Factors in Computing Systems, Joinville, pp. 1-4, 2017. https://doi.org/10.1145/3160504.3160580

[10] Garcia S., Gallego S., Luengo J., Benitez J., and Herrera F., “Big Data Preprocessing: Methods and Prospects,” Big Data Analytics, vol. 1, no. 1, pp. 1-22, 2016. https://doi.org/10.1186/s41044-016- 0014-0

[11] Ghani N., Hamid S., Hashem I., and Ahmed E., “Social Networks Big Data Analytics: A Survey,” Computers in Human Behavior, vol. 101, pp. 417- 428, 2019. https://doi.org/10.1016/j.chb.2018.08.039

[12] Google, Data Centers, https://www.google.com/about/datacenters/, Last Visited, 2025.

[13] Huang C., Yang C., and Hsiao Y., “A Novel Framework for Mining Social Media Data Based on Text Mining, Topic Modeling, Random Forest, and DANP Methods,” Mathematics, vol. 9, no. 17, pp. 20-41, 2021. https://www.mdpi.com/2227- 7390/9/17/2041

[14] Labayen V., Magana E., Morato D., and Izal M., “Online Classification of User Activities Using Machine Learning on Network Traffic,” Computer Networks, vol. 181, pp. 107557, 2020. https://doi.org/10.1016/j.comnet.2020.107557

[15] Masoumzadeh A. and Joshi J., “Ontology-Based Access Control for Social Networks Systems,” International Journal of Information Privacy, Security and Integrity, vol. 1, no. 1, pp. 59-78, 2011. https://doi.org/10.1504/IJIPSI.2011.043731

[16] Miller H. and Mork P., “From Data to Decisions: A Value Chain for Big Data,” IT Professional, vol. 15, no. 1, pp. 57-59, 2013. https://doi.org/10.1109/MITP.2013.11

[17] Othman S. and Al-Dhaqm A., “An Improved Machine Learning Method by Applying Cloud Forensic Meta-Architecture to Enhance the Data Collection Process in Cloud Environments,” Engineering, Technology and Applied Science Research, vol. 14, no. 1, pp. 13017-13025, 2024. https://doi.org/10.48084/etasr.6609

[18] Protege, https://protege.stanford.edu/, Last Visited, 2025.

[19] Puri P., Hassler G., Katragadda S., and Shenk A., “Digital Cloning of Online Social Networks for Language-Sensitive Agent-based Modeling of Misinformation Spread,” PLoS ONE, vol. 19, no. 6, pp. 1-19, 2024. https://doi.org/10.1371/journal.pone.0304889

[20] Rahman M. and Reza H., “A Systematic Review Towards Big Data Analytics in Social Networks,” Big Data Mining and Analytics, vol. 5, no. 3, pp. 228-244, 2022. https://doi.org/10.26599/BDMA.2022.9020009

[21] Saenz C., Wong S., Chang Y., and Bravo E., “The Effect of Fair Information Practices and Data Collection Methods on Privacy-Related Behaviors: A Study of Mobile Apps,” Information and Management, vol. 58, no. 1, pp. 103284, 2021. https://doi.org/10.1016/j.im.2020.103284

[22] Sekerci D. and Alp S., “Investigation of European Union Horizon 2020 Information and Communication Technology Projects with the Social Network Analysis Method,” Engineering Technology and Applied Science Research, vol. 13, no. 4, pp. 11182-11190, 2023. https://doi.org/10.48084/etasr.5967

[23] Shadbolt N., Lee T., and Hall W., “The Semantic Web Revisited,” IEEE Intelligent Systems, vol. 21, no. 3, pp. 96-101, 2006. https://doi.org/10.1109/MIS.2006.62

[24] Smith G., Kabban C., Hopkinson K., Oxley M., and et al., “Sensor Fusion for Context Analysis in 282 The International Arab Journal of Information Technology, Vol. 23, No. 2, March 2026 Social Media Covid-19 Data,” in Proceedings of the NAECON-IEEE National Aerospace and Electronics Conference, Dayton, pp. 415-422, 2021. https://doi.org/10.1109/NAECON49338.2021.96 96396

[25] Tene O., “What Google Knows: Privacy and Internet Search Engines,” International Association of Privacy Professionals, vol. 2008, no. 4, pp. 1-72, 2008. https://doi.org/10.63140/eatu6hcsq2

[26] Uschold M. and Gruninger M., “Ontologies: Principles, Methods and Applications,” The Knowledge Engineering Review, vol. 11, no. 2, pp. 93-136, 1996. https://doi.org/10.1017/S0269888900007797

[27] Yang X., McEwen R., Ong L., and Zihayat M., “A Big Data Analytics Architecture for Detecting User-Level Depression from Social Networks,” International Journal of Information Management, vol. 54, pp. 102-141, 2020. DOI: 10.1016/j.ijinfomgt.2020.102141