Cyber-physical-Social Semantic Link Network


Is there any cause-effect link between thinking, experiencing and knowledge? This problem has challenged philosophers and scientists for centuries. Under- standing and representing reality is a key step toward finding the link. Semantics modelling is an approach to understanding and representing reality. Computer scientists have studied semantics modelling for about half a century to create better representation systems for developing various application systems through unifying understandings. Traditional methods are mainly based on unary methodology, single abstraction, static representation and single space, which are limited in ability to reflect multi-dimensional, complex, evolutional, physical and social nature of reality. Reality evolves with co-evolution of cyberspace, physical space and social space and interactions between spaces. Understanding and modelling reality in cyber-physical-social space, and unveiling relevant rules and principles become a new challenge. This research creates a Cyber-Physical-Social Semantic Link Network model (CPSoSLN) consisting of a base network reflecting generality and basic structure of reality; a superstructure reflecting particularity and regularity in various spac- es; persistent mappings between the base network and the superstructure and be- tween the spaces that construct the superstructure; and, operations that evolve the base network, superstructure and the spaces with emerging patterns, categories, social linking rules, relational reasoning rules, principles, properties and dimen- sions. The model evolves with incorporating new rules, properties, principles, and methods and finally reaches a general form. It links reality to knowledge with an open and evolving cyber-physical-social relational system that helps un- derstand reality, discover relations and rules, interpret and summarize discover- ies, and predict and influence the evolution of reality.

Publication DOI:
Divisions: ?? 50811700Jl ??
College of Engineering & Physical Sciences > School of Computer Science and Digital Technologies
Additional Information: © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020
ISBN: 978-981-13-7310-7, 978-981-13-7311-4
Last Modified: 29 Sep 2023 09:54
Date Deposited: 13 Nov 2020 08:56
Full Text Link:
Related URLs: https://link.sp ... 981-13-7311-4_3 (Publisher URL)
PURE Output Type: Chapter (peer-reviewed)
Published Date: 2020-11-13
Authors: Zhuge, Hai (ORCID Profile 0000-0001-8250-6408)



Version: Accepted Version

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