Web Intelligence = Artificial Intelligence in the Connected World

in Special Issue   Posted on September 28, 2020 

Information for the Special Issue

Submission Deadline: Mon 31 May 2021
Journal Impact Factor : 1.405
Journal Name : World Wide Web
Journal Publisher:
Website for the Special Issue: https://www.springer.com/journal/11280/updates/17869608
Journal & Submission Website: https://www.springer.com/journal/11280

Special Issue Call for Papers:


World Wide Web has evolved as an omnipresent system which highly impacts science, education, industry and everyday life. World Wide Web is now a vast data production and consumption platform at which threads of data evolve from multiple devices, by different human interactions, over worldwide locations under divergent distributed settings. Such a dynamic complex system demands adaptive intelligent solutions, which will advance knowledge, human interactions and innovation. Web intelligence is now a cutting-edge area which must address all open issues towards deepening the understanding of all Web’s entities, phenomena, and developments.
Web Intelligence (WI) aims to achieve a multi-disciplinary balance between research advances in theories and methods usually associated with collective intelligence, data science, human-centric computing, knowledge management, network science, autonomous agents and multi-agent systems. It is committed to addressing research that both deepen the understanding of computational, logical, cognitive, physical, and social foundations of the future Web, and enable the development and application of intelligent technologies.
 “Web Intelligence = AI in the Connected World”, will provide a broad forum that academia, professionals and industry people can exchange their ideas, findings and strategies in utilizing the power of human brains and man-made networks to create a better world. Specifically, the fields of how intelligence is impacting the Web of People, the Web of Data, the Web of Things, the Web of Trust, the Web of Agents, and emerging Web in health and smart living in the 5G Era.
The Special Issue will discuss theories and methodologies from both disciplines of Artificial Intelligence and Web Intelligence with a focus on the evolution of World Wide Web. The discussions will encompass the theoretical basis and related tools to formally represent, measure,
model, and mine meaningful patterns from large-scale online datasets. Key objectives of the Special Issue are:

  • To discuss on challenging issues in Web Intelligence at the intersection of Artificial Intelligence and World Wide Web and seek the related breakthrough for new revolutions and breakthroughs in related fields;
  • To explore an innovative route to innovatively merge technologies in related fields and make online big data more productive and intelligent to enterprises;
  • To discuss on potential research projects in Web Intelligence and explore possible future research and development directions in technologies, methodology, and applications;
  • To provide a forum for researchers to discuss their recent work on the topics related to Web Intelligence to facilitate research collaboration in related areas.

Much effort will be devoted to bringing together in one place researchers cross various research and application domains from different countries and regions, and to stimulating a vivid discussion and exchange of ideas.


Manuscript submission deadline: 31 May 2021
First round notification with reviewer comments: 31 July 2021
Second round submission: 15 September 2021
Final acceptance notification: 31 October 2021

Guest Editors
Professor Yuefeng Li, Queensland University of Technology, Australia
Email: [email protected]

Professor Yuefeng Li is the leader of AI-Based Data Analysis Group of QUT Data Science Centre and the Academic Lead HDR of the School of Computer Science, QUT. He has published over 190 refereed papers (including 60 journal papers). He has demonstrable experience in leading large-scale research projects and has achieved many established research outcomes that have been published and highly cited in many significant Journals and Conferences. He has been a Program Chair of several International Conferences and General Chair of WI-IAT 2020. He is the Editor-in-Chief of Web Intelligence Journal.

Professor Amit Sheth, University of South Carolina, USA
Email: [email protected]

Prof. Sheth is the founding director of Artificial Intelligence Institute at University of South Carolina, Earlier, he was the LexisNexis Ohio Eminent Scholar and executive director of Ohio Center of Excellence in Knowledge-enabled Computing at Wright State University. He is working towards a vision of Computing for Human Experience, which focuses on human-centric future intelligent computing. His recent themes have included semantic-cognitive-perceptual computing over physical-cyber-social big data. His recent focus areas include neurosymbolic computing- esp. knowledge-infused learning. His extensive collaborations with clinicians and biomedical researchers encompass biomedical knowledge discovery; and novel use social media and sensor data for patient-centered care and patient empowerment. Sheth’s most prized achievement is the exceptional success of his past advisees; as of early 2015, majority of his 30 past PhDs advisees have 1000+ citations each. He was among the top 100 most cited computer scientists worldwide in 2018 and during few years earlier (h-index of 105 and 45,000+ citations). His research has led to several commercial products, many deployed applications, three successful companies.

Professor Athena Vakali, Aristotle University of Thessaloniki, Greece
Email: [email protected]

Athena Vakali is a professor at the Department of Informatics, Aristotle University, Greece, where she leads the Online Sources analytics on Web and INternet Distributed platformS (OSWINDS) research group. She holds a PhD degree in Informatics (Aristotle University), and a MSc degree in Computer Science (Purdue University, USA). Her current research interests include big data mining and analytics, Future Internet applications and enablers, online social networks mining, as well as on online sources data management on the cloud. Prof. Vakali has co-edited 3 books, co-authored 16 book chapters, published over than 60 papers in refereed journals and over 90 papers in International conferences. Her publications received over 3500 citations (h-index 28 in Google scholar) and she is in the editorial board of the “Computers & Electrical Engineering” Journal, and ICST Transactions on Social Informatics. She has served as a member in the EU Steering Committee for the Future Internet Assembly (2012-14) and she has been appointed as Director of the Graduate Program in Informatics, Aristotle University (2014-15). She has participated in more than 30 research projects (scientifically led 20) in EU FP7, H2020, and national projects. She has co-chaired major Conferences Program Committees such as: PC co-chair at the EU Network of Excellence 2nd Internet Science Conference (EINS 2015), 15th Web Information Systems Engineering (WISE 2014), and 5th International Conference on Model & Data Engineering (MEDI 2015). She has also served as Workshops co-chair in the WWW 2015 conference and has been a member to numerous International conferences and Workshops.

Associate Professor Xiaohui Tao, University of Southern Queensland, Australia
Email: [email protected]

Xiaohui Tao is Associate Professor in School of Sciences, University of Southern Queensland (USQ), Australia. His research interests include data analytics, machine learning, knowledge engineering, information retrieval, and health informatics. During his research career, Tao gained a wealth of knowledge and experience in dealing with massive data sets and delivering solutions to complex research problems. The research outcomes have been published on many top-tier journals (e.g., IEEE TKDE, KBS WWWJ and PRL) and conferences (e.g., ICDE, CIKM, PAKDD, WISE, and WI-IAT). Xiaohui has served as an Organizing Chair or PC Chair in many research events for the communities of WWW, Web Intelligence, Data Mining, and Healthcare

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