Multi-viewpoint Ontologies for Decision-Making Support

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 649)


Considering multiple viewpoints is often required when building ontologies for decision-making support systems. The notion of subjective context is useful for designing such a systems. We review the evolution of the subjectivity representation in the knowledge engineering, then choose an appropriate definition of the context for our application. This allows formulating the functional requirements for a multi-viewpoint decision-making support system and choosing the technical way of context representation. We propose a method of ontological representation of multiple viewpoints using named graphs as a response to these requirements. Decision-making support in the socio-economic realms is an especially valuable application for multi-viewpoint ontologies. We consider a demonstration use case, including software implementation. The inference rules may be used in such applications both for making conclusions within every particular context, or transferring knowledge between them. We present a set of sample rules for our demonstration use case and discuss the results achieved.


Multi-viewpoint ontology Decision making support Context modeling 


  1. 1.
    Baader, F., Horrocks, I., Sattler, U.: Description logics. In: van Harmelen, F., Lifschitz, V., Porter, B. (eds.) Handbook of Knowledge Representation, pp. 135–180. Elsevier, Amsterdam (2008)CrossRefGoogle Scholar
  2. 2.
    Baader, F., Kusters, R., Wolter, F.: Extensions to description logic. In: Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P. (eds.) Handbook of Knowledge Representation, pp. 219–261. Cambridge University Press, Cambridge (2003)Google Scholar
  3. 3.
    Barwise, J., Perry, J.: Situations and Attitudes. MIT Press, Cambridge (1983)zbMATHGoogle Scholar
  4. 4.
    Boer, A.: Legal Theory, Sources of Law and the Semantic Web. IOS Press, Amsterdam (2009)Google Scholar
  5. 5.
    Bouquet, P., Giunchiglia, F., van Harmelen, F., Serafini, L., Stuckenschmidt, H.: C-OWL: contextualizing ontologies. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 164–179. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  6. 6.
    Carroll, J., Bizer, C., Hayes, P., Stickler, P.: Named graphs, provenance and trust. In: Proceedings of the 14th International Conference on World Wide Web (2005)Google Scholar
  7. 7.
    Djakhdjakha, L., Hemam, M., Boufaida, Z.: Foundations on multi-viewpoints ontology alignment In: Proceedings of 4th International Conference on Web and Information Technologies, AlgeriaGoogle Scholar
  8. 8.
    Djezzar, M., Boufaida, Z.: Ontological classification of individuals: a multi-viewpoint approach. Int. J. Reasoning-Based Intell. Syst. 7(3/4), 276–285 (2015)CrossRefGoogle Scholar
  9. 9.
    Dung, P.M.: On the acceptability of arguments and its fundamental role in non-monotonic reasoning, logic programming and n-person games. Artif. Intell. 77(2), 321–357 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Hintikka, J.: Knowledge and Belief: An Introduction to the Logic of the Two Notions: Contemporary Philosophy. Cornell University Press, Ithaca (1962)Google Scholar
  11. 11.
    Hintikka, J.: Modality and quantification. Theoria 27(3), 119–128 (1961)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Klarman, S.: Reasoning with contexts in description logics. Ph.D. thesis, Vu University Amsterdam (2013)Google Scholar
  13. 13.
    Kripke, S.: Semantical analysis of modal logic I. Normal propositional calculi. Zeitschrift fur mathematicshe Logik und Grundlagen der Mathematik 9(56), 67–96 (1963)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Kurucz, A., Wolter, F., Zakharyaschev, M., Gabbay, D.: Many-Dimensional Modal Logics: Theory and Applications: Studies in Logic and the Foundations of Mathematic. Elsevier Science, Amsterdam (2003)zbMATHGoogle Scholar
  15. 15.
    McCarthy, J.: Notes on formalizing context. In: Proceedings of the 13th International Joint Conference of Artificial Intelligence – Volume I, IJCAI 1993, pp. 555–560. Morgan Kaufmann Publishers (1993)Google Scholar
  16. 16.
    Montague, R.: Pragmatics and intensional logic. In: Davidson, D., Harman, G. (eds.) Semantics of Natural Language, vol. 42, pp. 142–168. Springer, Heidelberg (1970)Google Scholar
  17. 17.
    Nickles, M., Cobos, R.: Social contexts and the probabilistic fusion and ranking of opinions: towards a social semantics for the semantic web In: Proceedings of the Second International Conference on Uncertainty Reasoning for the Semantic Web – Volume 218 (2006)Google Scholar
  18. 18.
    Obrst, L.: Ontological architectures. In: Poli, R., Healy, M., Kameas, A. (eds.) Theory and Applications of Ontology. Springer, Heidelberg (2010)Google Scholar
  19. 19.
    Obrst, L., Nichols, D.: Context and ontologies: contextual indexing of ontological expressions. In: AAAI 2005 Workshop on Context and Ontologies, AAAI 2005, Pittsburgh, PA (2005)Google Scholar
  20. 20.
    Sattler, U., Calvanese, D., Molitor, R.: The description logic handbook, pp. 137–177. Cambridge University Press (2003)Google Scholar
  21. 21.
    Sowa, J.: Knowledge Representation: Logical, Philosophical and Computational Foundations. Brooks/Cole, Boston (2000)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.TriniDataEkaterinburgRussia
  2. 2.EkaterinburgRussia

Personalised recommendations