Advertisement

A Description Logic Based Knowledge Representation Model for Concept Understanding

  • Farshad Badie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10839)

Abstract

This research employs Description Logics in order to focus on logical description and analysis of the phenomenon of ‘concept understanding’. The article will deal with a formal-semantic model for figuring out the underlying logical assumptions of ‘concept understanding’ in knowledge representation systems. In other words, it attempts to describe a theoretical model for concept understanding and to reflect the phenomenon of ‘concept understanding’ in terminological knowledge representation systems. Finally, it will design an ontology that schemes the structure of concept understanding based on the proposed semantic model.

Keywords

Concept understanding Conceptualisation Terminological knowledge Interpretation Formal semantics Description logics Ontology 

References

  1. 1.
    Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P.: The Description Logic Handbook: Theory, Implementation and Applications. Cambridge University Press, New York (2010)zbMATHGoogle Scholar
  2. 2.
    Badie, F.: Concept representation analysis in the context of human-machine interactions. In: Proceedings of the 14th International Conference on e-Society. International Association for Development of the Information Society, Portugal (2016a)Google Scholar
  3. 3.
    Badie, F.: Towards concept understanding relying on conceptualisation in constructivist learning. In: Proceedings of the 13th International Conference on Cognition and Exploratory Learning in Digital Age. International Association for Development of the Information Society, Germany (2016b)Google Scholar
  4. 4.
    Badie, F.: A formal semantics for concept understanding relying on description logics. In: Proceedings of the 9th International Conference on Agents and Artificial Intelligence. SCITEPRESS Digital Library, Portugal (2017)Google Scholar
  5. 5.
    Barsalou, L.W.: Perceptual Symbol Systems. The Behavioural and Brain Sciences. Cambridge University Press, New York (1999)Google Scholar
  6. 6.
    Biggs, J.B., Collis, K.F.: Evaluating the Quality of Learning: The SOLO Taxonomy (Structure of the Observed Learning Outcome). Academic Press, New York (2014)Google Scholar
  7. 7.
    Blackburn, S.: The Oxford Dictionary of Philosophy. Oxford University Press, Oxford (2016). WebCrossRefGoogle Scholar
  8. 8.
    Chaitin, G.J.: Algorithmic Information Theory. Cambridge University Press, New York (1987)CrossRefGoogle Scholar
  9. 9.
    Davies, J., Fensel, D., van Harmelen, F.: Towards the Semantic Web, Ontology-Driven Knowledge Management. Wiley Online Publications, New York (2003)Google Scholar
  10. 10.
    di Pellegrino, G., Fadiga, L., Fogassi, L., Gallese, V., Rizzolatti, G.: Understanding motor events: a neurophysiological study. Exp. Brain Res. 91, 176–180 (1992)CrossRefGoogle Scholar
  11. 11.
    von Foerster, H.: Understanding Understanding, Essays on Cybernetics and Cognition. Springer, New York (2003)CrossRefGoogle Scholar
  12. 12.
    Gray, P.M.D., Kulkarni, K.G., Paton, N.W.: Object-Oriented Databases - A Semantic Data Model Approach. Prentice Hall International Series in Computer Science. Prentice Hall, London (1992)Google Scholar
  13. 13.
    Stephan, G., Pascal, H., Andreas, A.: Knowledge representation and ontologies. In: Studer, R., Grimm, S., Abecker, A. (eds.) Semantic Web Services, pp. 51–105. Springer, Heidelberg (2007).  https://doi.org/10.1007/3-540-70894-4_3
  14. 14.
    Honderich, T.: The Oxford Companion to Philosophy. Oxford University Press, Oxford (2005)Google Scholar
  15. 15.
    Jackendoff, R.: Semantic Structures. MIT Press, Cambridge (1990)Google Scholar
  16. 16.
    Kant, I.: Kritik der reinen Vernunft. VMA-Verlag, Wiesbaden. (imprint of the 1924 edt.), p. 967 et passim (1781)Google Scholar
  17. 17.
    Kintsch, W., Welsch, D., Schmalhofer, F., Zimny, S.: Sentence memory: a theoretical analysis. J. Mem. Lang. 29, 133–159 (1990). ElsevierCrossRefGoogle Scholar
  18. 18.
    MacKay, D.: Information Theory, Inference and Learning Algorithms. Cambridge University Press, New York (2003)zbMATHGoogle Scholar
  19. 19.
    Peschl, M.F., Riegler, A.: Does representation need reality? Rethinking Epistemological issues in the light of recent developments and concepts in cognitive science. In: Riegler, A., Peschl, M., von Stein, A. (eds.) Understanding Representation in the Cognitive Sciences, pp. 9–17. Springer, Boston (1999).  https://doi.org/10.1007/978-0-585-29605-0_1Google Scholar
  20. 20.
    Resnik, P.: Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language. J. Artif. Intell. Res. 11, 95–130 (1999)Google Scholar
  21. 21.
    Schmidt-Schauss, M., Smolka, G.: Attributive concept descriptions with complements. Artif. Intell. 48(1), 1–26 (1991). ElsevierMathSciNetCrossRefGoogle Scholar
  22. 22.
    Simpson, J.A., Weiner, E.S.C.: The Oxford English Dictionary. Oxford University Press, Oxford (1989)Google Scholar
  23. 23.
    Staab, S., Studer, R.: Handbook on Ontologies, 2nd edn. Springer, Heidelberg (2009)Google Scholar
  24. 24.
    Uithol, S., van Rooij, I., Bekkering, H., Haselager, P.: Understanding motor resonance. J. Soc. Neurosci. 6(4), 388–397 (2011). RoutledgeCrossRefGoogle Scholar
  25. 25.
    Uithol, S., Paulus, M.: What do infants understand of others’ action? A theoretical account of early social cognition. Psychol. Res. 78(5), 609–622 (2014)CrossRefGoogle Scholar
  26. 26.
    Webb, J.: Understanding Representation. Sage Publications, London (2009)Google Scholar
  27. 27.
    Zwaan, R.A., Taylor, L.J.: Seeing, acting, understanding: motor resonance in language comprehension. J. Exp. Psychol. Gen. 135(1), 1–11 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Center for Computer-mediated EpistemologyAalborg UniversityAalborgDenmark

Personalised recommendations