Guest editorial: Big data technologies and applications in Web 3.0 – trends an

Web 3.0 is the next era in internet development, and it is characterized as being decentralization, semantic intelligence and user experience oriented. The previous eras focused on user-generated content, but Web 3.0 uses big data, artificial intelligence (AI) and blockchain to create smart and personalized digital environments. Massive volumes of structured and unstructured data generated across devices and platforms fuel adaptive, secure and autonomous services now more and more. Yet, the more these technologies evolve, the more important concerns regarding privacy, scalability and data governance become.This special issue of the International Journal of Web Information Systems explores how Web 3.0 applications are being revolutionized by big data technologies. The accepted pieces cover a range of but connected themes – from personalized learning to AI domain models and privacy in IoT-integrated networks – demonstrating the potential and complexity of this digital revolution. All of the contributions support sustainable development goals (SDGs), including SDG 4 (Quality Education), SDG 9 (Innovation and Infrastructure) and SDG 11 (Sustainable Cities and Communities).The first paper “Personalized and adaptive e-learning systems for semantic Web: a systematic review and roadmap” is a comprehensive survey of intelligent learning systems based on Semantic Web technologies. It addresses the personalization strategies, learner model, adaptive content and ontology-driven architecture for enhancing learning outcomes. The study proposes a roadmap for integrating semantic frameworks with AI to enable personalized, scalable and accessible e-learning systems. This effort is directly contributing to SDG 4 through the promotion of quality and inclusive education that is led by smart Web infrastructures.The second paper, “Chinese named entity recognition in the furniture domain based on ERNIE and adversarial learning,” is suggesting a robust AI model that is intended for a niche but significant industry. The authors develop a framework that integrates ERNIE’s contextual embedding with adversarial learning and BiLSTM-CRF for better Chinese furniture text entity recognition. With a remarkable F1 score of 89.6%, the model demonstrates how domain-specific big data applications can simplify and streamline industrial information extraction and enhance decision-making processes. The research helps SDG 9 through innovation and manufacturing industry efficiency via tailored AI solutions.The third paper, “Privacy Preservation of Internet of Things-Integrated Social Media Networks: A Survey and Future Challenges,” is a timely survey on privacy threats within decentralized rich-data social communities. With the availability of IoT in social sites, sensitive flows of information have increased significantly. The paper elaborates on existing methods – differential privacy, federated learning and blockchain – to prevent threats. It recognizes the strengths of both and the open challenges and demands adaptive and trustworthy privacy-preserving systems. This work enhances SDG 11 by fostering secure and sustainable digital communities.Together, these contributions show how big data technologies can drive smart and context-aware applications for Web 3.0 and address pressing concerns of privacy, flexibility and social good. They demonstrate practical paths for applying AI, semantics and decentralized models in end-to-end systems, linking technological advancement with human development goals. As Web 3.0 continues to evolve, such interdisciplinary work will be even more crucial to shaping an open, secure and intelligent internet future.This paper forms part of a special section “Big data technologies and applications in Web 3.0: trends and challenges”, guest edited by Zhiyuan Tan, Nour Moustafa, Xiangjian He and Kehinde Babaagba.