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Reviewing and Predicting Human-Machine Cooperation Based on Knowledge Graph Analysis

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HCI International 2020 - Late Breaking Papers: User Experience Design and Case Studies (HCII 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12423))

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Abstract

Human-Machine Cooperation involves multiple fields and disciplines such as automated vehicles, robots, machine manufacturing, psychological cognition, and artificial intelligence. As research in these areas progresses rapidly, it is important to keep up with new trends and key turning points in the development of collective knowledge. Based on the visualization method, we can quickly sort out the ins and outs of this field in the vast literature and search for valuable and potential literature. First, we introduced the principles of scientific visualization of knowledge graphs. Then we extracted 3257 articles from Web of Science Core Collection and established a research field of bibliographic records of representative data sets. Next, we carried out a visual analysis of the discipline Dual-Map overlay, Co-words, Co-citation, and reviewed some key Highly-Cited documents. Finally, we summarize and analyze the literature with high betweenness centrality, citation bursts, and Sigma value. This review will help professionals to have a more systematic understanding of the entire field and find opportunities for future Human-Machine Cooperation development.

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  1. 1.

    http://cluster.ischool.drexel.edu/~cchen/citespace/download/.

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Authors and Affiliations

  1. College of Design and Innovation, Tongji University, Shanghai, China

    Yujia Liu

Authors
  1. Yujia Liu
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Correspondence to Yujia Liu .

Editor information

Editors and Affiliations

  1. University of Crete and Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Constantine Stephanidis

  2. Aaron Marcus and Associates, Berkeley, CA, USA

    Aaron Marcus

  3. World Usability Day and Bentley User Experience Center, Newton Center, MA, USA

    Elizabeth Rosenzweig

  4. Tsinghua University, Beijing, China

    Pei-Luen Patrick Rau

  5. San Jose State University, San Jose, CA, USA

    Abbas Moallem

  6. Eindhoven Univesity of Technology, Eindhoven, Noord-Brabant, The Netherlands

    Matthias Rauterberg

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Liu, Y. (2020). Reviewing and Predicting Human-Machine Cooperation Based on Knowledge Graph Analysis. In: Stephanidis, C., Marcus, A., Rosenzweig, E., Rau, PL.P., Moallem, A., Rauterberg, M. (eds) HCI International 2020 - Late Breaking Papers: User Experience Design and Case Studies. HCII 2020. Lecture Notes in Computer Science(), vol 12423. Springer, Cham. https://doi.org/10.1007/978-3-030-60114-0_12

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  • DOI: https://doi.org/10.1007/978-3-030-60114-0_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60113-3

  • Online ISBN: 978-3-030-60114-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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