Advertisement

Interactive Visualization of Ontology-Based Conceptual Domain Models in Learning and Scientific Research

  • Dmitry Litovkin
  • Anton AnikinEmail author
  • Marina Kultsova
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1041)

Abstract

The paper presents an approach to knowledge transferring and sharing on the base of the semantic link network (SLN) representing expert knowledge in the explicit form. To provide an efficient SLN understanding, it is represented with a geometric graph which can be interactively visualized using a combination of the appropriate visualization methods. The coupling of these methods allows getting a different level of details of the SLN visualization in accordance with the user needs. The proposed approach is planned being implemented in the knowledge management system for learning and scientific research.

Keywords

Semantic link network Ontology Graph Interactive visualization Semantic zooming 

Notes

Acknowledgements

This paper presents the results of research carried out under the RFBR grants 18-07-00032 and 18-47-340014.

References

  1. 1.
    A. Anikin, M. Kultsova, D. Litovkin, E. Sarkisova, Representation of what-knowledge structures as ontology design patterns, in 2018 9th International Conference on Information, Intelligence, Systems Applications (IISA), July 2018Google Scholar
  2. 2.
    A. Anikin, D. Litovkin, M. Kultsova, E. Sarkisova, T. Petrova, Ontology visualization: approaches and software tools for visual representation of large ontologies in learning. Commun. Comput. Inform. Sci. 754, 133–149 (2017)CrossRefGoogle Scholar
  3. 3.
    A. Anikin, D. Litovkin, M. Kultsova, E. Sarkisova, Ontology-based collaborative development of domain information space for learning and scientific research, in Knowledge Engineering and Semantic Web: 7th International Conference (KESW 2016), Prague, Czech Republic, ed. by A.C. Ngonga Ngomo, P. Křemen, 21–23 Sept 2016, pp. 301–315. Springer International Publishing, Cham (2016).  https://doi.org/10.1007/978-3-319-45880-9_23Google Scholar
  4. 4.
    M. Dudáš, O. Zamazal, V. Svátek, Roadmapping and navigating in the ontology visualization landscape, in Knowledge Engineering and Knowledge Management, ed. by K. Janowicz, S. Schlobach, P. Lambrix, E. Hyvönen (Springer International Publishing, Cham, 2014), pp. 137–152CrossRefGoogle Scholar
  5. 5.
    T. Halpin, ORM 2 Graphical Notation (Neumont University, Salt Lake City, 2005)Google Scholar
  6. 6.
    I. Herman, G. Melançon, M.S. Marshall, Graph visualization and navigation in information visualization: a survey. IEEE Trans. Visual. Comput. Graphics 6(1), 24–43 (2000).  https://doi.org/10.1109/2945.841119CrossRefGoogle Scholar
  7. 7.
    D. Kudryavtsev, T. Gavrilova, From anarchy to system: a novel classification of visual knowledge codification techniques. Knowl. Process Manage. 24(1), 3–13 (2017). https://onlinelibrary.wiley.com/doi/abs/10.1002/kpm.1509CrossRefGoogle Scholar
  8. 8.
    T. von Landesberger, A. Kuijper, T. Schreck, J. Kohlhammer, J.J. van Wijk, J. Fekete, D.W. Fellner, Visual analysis of large graphs: state-of-the-art and future research challenges. Comput. Graph. Forum 30(6), 1719–1749 (2011)CrossRefGoogle Scholar
  9. 9.
    T. Munzner, in Interactive visualization of large graphs and networks, Ph.D. thesis, Stanford University, June 2000Google Scholar
  10. 10.
    T. Munzner, E. Maguire, Visualization Analysis and Design, AK Peters visualization series (CRC Press, Boca Raton, 2015)Google Scholar
  11. 11.
    K. Nazemi, Adaptive semantics visualization, in Studies in Computational Intelligence, vol. 646 (Springer, Berlin, 2016).  https://doi.org/10.1007/978-3-319-30816-6CrossRefGoogle Scholar
  12. 12.
    J.D. Novak, A.J. Caas, Theoretical origins of concept maps, how to construct them, and uses in education. Reflect. Educ. 3(1), 29–42 (2007)Google Scholar
  13. 13.
    OWL 2 Web Ontology Language Primer. https://www.w3.org/TR/owl2-primer/
  14. 14.
    S. Ramakrishnan, A. Vijayan, A study on development of cognitive support features in recent ontology visualization tools. Artif. Intell. Rev. 41(4), 595–623 (2014).  https://doi.org/10.1007/s10462-012-9326-2CrossRefGoogle Scholar
  15. 15.
    C.E. Shannon, A mathematical theory of communication. Bell Syst. Tech. J. 27(3), 379–423, 623–656 (1948)MathSciNetCrossRefGoogle Scholar
  16. 16.
    B. Shneiderman, The eyes have it: a task by data type taxonomy for information visualizations, in Proceedings of the 1996 IEEE Symposium on Visual Languages (VL’96) (IEEE Computer Society, Washington, DC, USA, 1996), pp. 336–343Google Scholar
  17. 17.
    G. Stapleton, M. Compton, J. Howse, Visualizing owl 2 using diagrams, in 2017 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), Oct 2017, pp. 245–253Google Scholar
  18. 18.
    W. Weaver, Recent contributions to the mathematical theory of communication, in A Mathematical Theory of Communication (University of Illinois Press, Champaign, 1949)Google Scholar
  19. 19.
    V. Wiens, S. Lohmann, S. Auer, Semantic zooming for ontology graph visualizations, in Proceedings of the Knowledge Capture Conference (K-CAP 2017) (ACM, New York, 2017), pp. 4:1–4:8Google Scholar
  20. 20.
    H. Zhuge, Semantic linking through spaces for cyber-physical-socio intelligence: a methodology. Artif. Intell. 175(5), 988–1019 (2011) (special Review Issue)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Dmitry Litovkin
    • 1
  • Anton Anikin
    • 1
    Email author
  • Marina Kultsova
    • 1
  1. 1.Volgograd State Technical UniversityVolgogradRussia

Personalised recommendations