• Dept of Computer Science, School of Informatics and Digital Engineering, Aston University

      B4 7ET Birmingham

      United Kingdom

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    Personal profile

    Research Interests

    My major research interest is to explore the fundamental issues on semantics, knowledge, dimension, and self-organization in a multi-disciplinary context.  Therefore, I created the Semantic Link Network model and the Resource Space Model and integrated them as a fundamental semantic space to support semantic management and exploration of various resource spaces.  Research has established systematic theory and method, which have been used in many applications.

    In recent years, I am leading research towards a new science and engineering for Cyber-Physical Society.



    I have made distinguished contribution to Semantics Modelling and the Future Interconnection Environment.

    (a) Semantics Modeling

    I created a semantic space model with systematic theory and methods for discovering, mapping and managing the patterns of data, the patterns in the physical space, and the patterns of understanding so that various information services such as QA, recommendation and summarisation can be precisely provided by generating the space of understanding and the space of data, and adapting the spaces according to change, and mapping one space into the other. My contribution significantly advances data modelling and semantic web research, especially facing the emerging cyber-physical society. The semantic space model is demonstrated by the following characteristics:

    (1) Multi-dimensional category space, which is created for modelling multi-dimensional abstraction of cyberspace and concept space with an uniform theory. It is the first non-relational data model that deals with big data through multi-dimensional categorization. Regarding dimension as computing, the model becomes a multi-dimensional computing model. The model has been further developed into a multi-dimensional methodology for creating, integrating and managing methodologies for observing, analysing, developing, managing and studying various human-machine symbiotic systems.

    (2) Semantic link network, which models self-organized semantic connectivity in cyberspace, physical space and concept space based on semantic link discovery, semantic linking rules and reasoning, topological centrality measure, semantics emerging principles, and interactivity between spaces. Different from previous logic-based approaches, it creates a social and interactive approach to the Semantic Web and provides a means to analyse, model, manage and study various spaces. Regarding nodes as computing and enabling semantic links to carry data flow, knowledge flow and material flow, the model evolves into a general self-organized computing model in concept space, cyberspace and physical space.  This work is earlier than Berners-Lee's Linked Data (2006) and Google's Knowledge Graph (2012).

    (3) Decentralised semantic data management platform, which supports self-organization of dynamic and uncertain data with an efficient information routing algorithm based on a metric space and a semantic space, a method for automatically constructing large-scale decentralised structured network platform, a scalable method for constructing and analyzing structured peer-to-peer network based on small-world features, which can be applied to various topological structure and distance spaces and the analysis and design of various high-performance structured self-organized networks and data routing methods.

    The model and method have been verified in such applications as QA, recommendation and summarization, and applied to create many advanced mechanisms and methods in past 15 years. The models and methods have been adopted widely for big data management, faceted classification, healthcare multimedia management, mechanical fault diagnosis system, ……, etc. One of my patents was transferred to enterprise for content management. I am extending this model to a multi-dimensional methodology for supporting data-intensive scientific exploration.

    (b) Future Interconnection Environment

    As a cyber-infrastructure, the Internet has connected not only computers but also various devices and the people who use the devices such as mobile phones and smart watches.  The Internet of Things is a technique that facilitates the connection between the Internet and various devices.  The rapid development of various applications on the Internet has accelerated the expansion of data.  A significant transformation is to extend "Things" from devices to the physical space and social space. The thought model for the Future Interconnection Environment was proposed in WAIM 2004 (Keynote). It includes the notion of the ternary universe consists of the physical world, virtual world and mental world and a multi-level reference architecture with the nature and devices at the bottom level followed by the level of the Internet, sensor network, and mobile network, and intelligent human-machine communities at the top level, which supports geographically dispersed users to cooperatively accomplish tasks and solve problems by using the network to actively promote the flow of material, energy, techniques, information, knowledge, and services in this environment.  An implementation architecture IMAGINE-1 for e-science application was proposed [Zhuge, 2005]. This thought model is the first thought model after the The Internet of Things was coined.  It envisioned the development trend of the Internet of Things and other research areas.



    I conduct research on the following EU project:

    Big Data Corridor: A New Business Economy (No. 12R16P00220, 2016-2020)
    It provides innovative computing services on big data for over 20 SMEs in Birmingham.   Two postdoctors work on this project.  
    A group of projects for SMEs will be developed under this projects.
    Research Monographs

    [1] H. Zhuge, Cyber-Physical-Social Intelligence on Human-Machine-Nature Symbiosis, Springer, 2020.

        This monograph studies the symbiosis between humans, machines, and nature, including the rules and emerging patterns of recognition, and the integration and optimization of various flows through cyberspace, physical space and social space.

    [2] H. Zhuge, Multi-Dimensional Sumamrization in Cyber-Physical Society, Morgan Kaufmann, 2016. 

            This monograph presents a methodology for general summarization in cyber-physical-social space  through a multi-dimensional lens of semantic computing. It transforms the paradigm of summarization research and deepens people’s understanding on semantics,dimension, knowledge and computing.

          Brief Video Introduction

          A lecture on summarization and the way to develop research

    [3] H. Zhuge, The Knowledge Grid, World Scientific, 2004 (1st edition), 2012 (2nd edition with subtitle: Toward Cyber-Physical Society).  Both editions are best sellers.  The 3rd edition has been invited for publication.

    [4] H. Zhuge, The Web Resource Space Model, Springer, 2008.  It introduces the systematic theory, model and method of multi-dimensional categorization as a semantic model.



    The following invited papers are among the most cited papers in Artificial Intelligence journal, the leading journal in Artificial Intelligence area:

    [1] H. Zhuge, Semantic linking through spaces for cyber-physical-socio intelligence: A methodology, Artificial Intelligence, 175(2011)988-1019.    It proposes the notion of Cyber-Physical-Socio Intelligence for the first time.  It was raked No.4 of the most cited papers in AI journal since 2011.

    [2] H. Zhuge, Interactive Semantics, Artificial Intelligence, 174(2010)190-204.


    Resource Space Model.

    [3] H. Zhuge and Y.Xing, Probabilistic Resource Space Model for Managing Resources in Cyber-Physical Society, IEEE Transactions on Service Computing, vol. 5, no. 3, 2012, pp. 404-421.

    [4] H. Zhuge, Y. Xing and P. Shi, Resource Space Model, OWL and Database: Mapping and Integration, ACM Transactions on Internet Technology, 8/4, 2008.


    Semantic Link Network

    [5 ] W. Li and H. Zhuge, Abstractive Multi-Document Summarization based on Semantic Link Network, IEEE Transactions on Knowledge and Data Engineering, 33(1)(2021)43-54.

    [6] B. Xu and H. Zhuge, The influence of semantic link network on the ability of question-answering system.  Future Generation Computer Systems, 108(2020)1-14.

    [7] W. Li and H. Zhuge, Probabilistic inference on uncertain semantic link network and its application in event identification. Future Generation Computer Systems, 104 (2020)32-42.

    [8] M. Cao and H. Zhuge, Grouping sentences as better language unit for extractive text summarization. Future Generation Computer Systems, 109(2020) 331-359.

    [9] X. Sun and H. Zhuge, Summarization of Scientific Paper Through Reinforcement Ranking on Semantic Link Network. IEEE ACCESS, vol.6, 2018, pp. 40611-40625.

    [10] H.Zhuge, Communities and Emerging Semantics in Semantic Link Network: Discovery and Learning, IEEE Transactions on Knowledge and Data Engineering, vol.21, no.6, 2009, pp. 785-799.

    [11] H. Zhuge and X. Li, Peer-to-Peer in Metric Space and Semantic Space, IEEE Transactions on Knowledge and Data Engineering, 19 (6) (2007) 759-771.

    The following papers are on peer-to-peer computing platforms:

    [12] H. Zhuge, et al, A Scalable P2P Platform for the Knowledge Grid, IEEE Transactions on Knowledge and Data Engineering, 17 (12) (2005) 1721-1736.

    [13] H. Zhuge and L. Feng, Distributed Suffix Tree Overlay for Peer-to-Peer Search, IEEE Transactions on Knowledge and Data Engineering, 20 (2) (2008) 276-285.

    [14] H. Zhuge, X. Chen, X. Sun and E.Y ao, HRing: A Structured P2P Overlay Based on Harmonic Series, IEEE Transactions on Parallel and Distributed Systems,19 (2) (2008) 145-158.


    Future Interconnection Environment

    [15] H. Zhuge, The Future Interconnection Environment, IEEE Computer, 38 (4) (2005) 27-33.

    [16] H. Zhuge, Discovery of Knowledge Flow in Science, Communications of the ACM, 49 (5) (2006) 101-107.

    [17] H.Zhuge and X. Shi, Toward the Eco-grid: A Harmoniously Evolved Interconnection Environment. Communications of the ACM, 47(9)(2004)78-83.

    [18] H. Zhuge, Future Interconnection Environment – Dream, Principle, Challenge and Practice, Keynote at the 5th International Conference on Web-Age Information Management,WAIM 2004: Advances in Web-Age Information Management, July 15-17, 2004, pp. 13-22. (This is the first thought model for the Internet of Things after it was named).



    Membership of Professional Bodies

    Distinguished Scientist of the ACM (Association of Computing Machinery)

    Fellow of British Computer Society

    Teaching Activity

    Teaching is a part of my research.  I regularly supervise final year projects, play the role of personal tutor of several undergraduate students, and lecture final-year undergraduate and master module 'Enterprise Computing Strategies in Cyber-Physical Society'.  I also teach undergraduate module 'Information Systems and Databases'.   I often incorporate my own research and state-of-the-art technologies into teaching.


    ACM Distinguished Scientist for 'Significant accomplishments in, and impact on, the computing field

    ACM Distinguished Speaker

    Distinguished Visiting Fellow of Royal Society of Engineering.

    Fellow of British Computer Society



    Chair in Computer Science

    Professional/editorial offices

    Journal Editorial Board: IEEE Intelligent Systems



    MB 214G, Computer Science

    School of Engineering and Applied Science

    Aston University, Birmingham, B4 7ET, UK

    PhD Supervision

    I have supervised 30 PhD students, some of them became professors and associate professors and some are working in well-known enterprises.

    Media Contributions

    I regularly offer ACM Distinguished Lectures at international conferences and universities world widely.

    H. Zhuge, Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure, 30 June 2015. ACM Distinguished Lecture: Part1, Part2.

    H. Zhuge, Mapping Big Data into Knowledge Space - from Culture, Science, Industry and Computing Perspectives, Aston Inaugural Lecture, Video.

    External Connections and Partnerships

    Recent Keynote

    H. Zhuge, Semantic Mapping of Big Data.  International Conference on Big Data Analysis and Business ICBDBI2017, Shuzhou, China, June 2017.

    Abstract: Big data research is shifting the science paradigm and driving the fourth industrial revolution but What is the fundamental challenge of big data computing? How can we map big data facing the challenge?  This lecture will introduce two techniques to semantically map big data, including semantic link Network and multi-dimensional category space.

    Teaching Activity

    MSc Projects
    • Discover Knowledge Flow from Scientific Papers
    • Whether we are aware of it or not, knowledge flows within society and in the Internet-mediated interconnection environment people increasingly rely on for work and daily life.  Recognizing and understanding knowledge flow between people is valuable for understanding knowledge.  Discovering, managing and utilizing such knowledge can provide intelligent services for people.   This project is to discover knowledge flow through citation network of scientific papers on a particular (e.g., medical) domain.   A key step is to construct citatin network on papers.
    • Reading:
    • [1] H. Zhuge, Discovery of knowledge flow in Science, Communications of the ACM, 49(5)(2006)101-107.
    • [2] H. Zhuge, Chpater 5 in The Knowledge Grid - Toward Cyber-Physical Society, World Scientific Publishing Co, 2012 (Available in library)
    • Suitable for: MSc AI / Applied AI / CS
    • Behaviour-Inspired Text Summarization
    • Text summarization is to transform a long text into a short text that represents its core content. It is a research area of natural language processing.  Traditional research mainly focuses on computing techniques on text including statistics and structure analysis.  The result of summarization is not satisfied.  Human-inspired text summarization is to look into the human behaviours in text summarization and find some rules to improve the existing text summarization approaches.  A key step is to observe human summarization behaviors to observe characteristics.
    • Reading:
    • [1] H. Zhuge, Chapter 1-3 in Multi-Dimensional Summarization in Cyber-Physical Society, 2016 (available in Library).
    • Suitable for: MSc AI / Applied AI / CS
    • Muti-Dimensional Personal Resource Space
    • Traditional relational databases manage data based on relational data model.  Different data models have been developed to meet the need of managing diverse resources.  The Resource Space Model is a data model based on multi-dimensional classifications on diverse resources.   This research is  to improve the existing methods and implement a software system that can manage texts, images and videos.   A key step is to find a method for uniformly classifying texts.
    • Reading:
    • [1] H. Zhuge, Chapter 3 in The Knowledge Grid - Toward Cyber-Physical Soceity, World Scientific, 2012.
    • [2] H. Zhuge, Web Resource Space Model, Springer, 2008 (available in Library).
    • Suitable for: MSc AI / Applied AI / CS


    • Cyber-Physical Society
    • Semantics
    • Knowledge


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