TY - JOUR
T1 - Web Intelligence (WI) 3.0
T2 - in search of a better-connected world to create a future intelligent society
AU - Kuai, Hongzhi
AU - Huang, Jimmy X.
AU - Tao, Xiaohui
AU - Pasi, Gabriella
AU - Yao, Yiyu
AU - Liu, Jiming
AU - Zhong, Ning
N1 - This research is supported by the research grants from JSPS Grants-in-Aid for Scientific Research of Japan (19 K12123); Natural Sciences and Engineering Research Council (NSERC) of Canada and York Research Chairs (YRC) program. We thank to the renowned researchers who have supported and contributed to the WI field, including E. A. Feigenbaum, L. A. Zadeh, J. McCarthy, T. M. Mitchell, S. Ohsuga, K. Friston, R. M. Karp, Y. Anzai, J. Hopcroft, A. C.-C. Yao, J. Sifakis, B. Lampson, L. Valiant, R. Reddy, and F. van Harmelen.
Publisher Copyright:
© The Author(s) 2025.
PY - 2025/6/4
Y1 - 2025/6/4
N2 - Over the past two decades, Web Intelligence (WI) has emerged as a key field driving the evolution of AI in the connected world, addressing the demands of a future intelligent society. This paper provides a comprehensive review of WI’s contributions since its inception in 2000, spanning three distinct phases: Wisdom World Wide Web (WI 1.0, 2000–2009), Wisdom Web of Things (WI 2.0, 2010–2017), and Wisdom Web of Everything (WI 3.0, since 2018). For each phase, we examine key advancements, challenges, and future directions from the perspectives of both intelligent machines and human experts, highlighting significant societal impacts. To advance WI research, we propose a large language model-based learning framework for topic analysis and trend prediction. Moving beyond single-perspective approaches, we emphasize the Connected Intelligence Ecosystem defined by the HIGH5 scheme comprising one goal, two twins, three fundamentals, four functions, and five services that are realized through WI 3.0. This vision serves as a bridge from localized models to a global reference framework for addressing sustainability challenges in future societies. To illustrate the real-world implications of WI 3.0, we present case studies focusing on brain-inspired research, particularly in the intersection of brain intelligence, brain health, and brainternet-fostering interdisciplinary collaboration across diverse research communities.
AB - Over the past two decades, Web Intelligence (WI) has emerged as a key field driving the evolution of AI in the connected world, addressing the demands of a future intelligent society. This paper provides a comprehensive review of WI’s contributions since its inception in 2000, spanning three distinct phases: Wisdom World Wide Web (WI 1.0, 2000–2009), Wisdom Web of Things (WI 2.0, 2010–2017), and Wisdom Web of Everything (WI 3.0, since 2018). For each phase, we examine key advancements, challenges, and future directions from the perspectives of both intelligent machines and human experts, highlighting significant societal impacts. To advance WI research, we propose a large language model-based learning framework for topic analysis and trend prediction. Moving beyond single-perspective approaches, we emphasize the Connected Intelligence Ecosystem defined by the HIGH5 scheme comprising one goal, two twins, three fundamentals, four functions, and five services that are realized through WI 3.0. This vision serves as a bridge from localized models to a global reference framework for addressing sustainability challenges in future societies. To illustrate the real-world implications of WI 3.0, we present case studies focusing on brain-inspired research, particularly in the intersection of brain intelligence, brain health, and brainternet-fostering interdisciplinary collaboration across diverse research communities.
KW - Artificial Intelligence (AI)
KW - Brain Intelligence
KW - Connectedness
KW - Future Intelligent Society
KW - Web Intelligence (WI)
KW - Wisdom Web of Everything (W2E)
UR - http://www.scopus.com/inward/record.url?scp=105007223097&partnerID=8YFLogxK
U2 - 10.1007/s10462-025-11203-z
DO - 10.1007/s10462-025-11203-z
M3 - Journal article
AN - SCOPUS:105007223097
SN - 0269-2821
VL - 58
JO - Artificial Intelligence Review
JF - Artificial Intelligence Review
IS - 9
M1 - 265
ER -