Semantic Web Languages: Expressivity of SWL

  • Martin ŽáčekEmail author
  • Alena Lukasová
  • Marek Vajgl
  • Zdeňka Telnarová
Part of the Studies in Computational Intelligence book series (SCI, volume 830)


The paper tries to discuss from a slightly higher level a focus oriented towards general properties that a Semantic Web Language (SWL) must have. We state as a ground the following two points of view: to compare (1) Expressivity of the SWL, and (2) a possibility to infer new knowledge from an SWL knowledge base with corresponding properties of the classical First Order Logics (FOPL) as a measure of their basic quality (now prepared). From expressivity, the language for the semantic web must be a common communication tool for computers as well as for people fulfilling an easy-to-use condition by means of adding more semantics directly into the language's syntax. After a discussion of properties and critical recommendations two languages, OWL DL1 and RDF CFL have been proposed here to become the SWL both having the expressivity comparable with the FOPL.


Semantic web language SWL First order logics FOPL Syntax Semantic 


  1. 1.
    Straccia, U.: Foundations of Fuzzy Logic and Semantic Web Languages. Chapman and Hall/CRC (2016)Google Scholar
  2. 2.
    Smullyan, R.R.: First-Order Logic, vol. 43. Springer Science & Business Media (2012)Google Scholar
  3. 3.
    Baader, et al. (eds.): The Description Logic Handbook—Theory, Interpretation, and Applications. Cambridge University Press (2003)Google Scholar
  4. 4.
    Horrocks, I., Patel-Schneider, P.F., Van Hamelen, F.: From SHIQ and RDF to OWL. The making of a web ontology language. J. Web Semant. (2003). Scholar
  5. 5.
    McGuinness, D.L., Van Harmelen, F.: OWL web ontology language overview. W3C Recomm. 10(10) (2004)Google Scholar
  6. 6.
    Hastings, J.: Primer on ontologies. In: The Gene Ontology Handbook, pp. 3–13. Humana Press, New York (2017)Google Scholar
  7. 7.
    Barwise, J., Feferman, S., Feferman, S. (eds.): Model-Theoretic Logics, vol. 8. Cambridge University Press (2017)Google Scholar
  8. 8.
    Lukasová, A., Vajgl, M., Žáček, M.: Reasoning in RDF graphic formal system with quantifiers. In: Proceedings of the International Multiconference on Computer Science and Information Technology, Poland, pp. 67–72. IEEE Computer Society, Mragowo (2010)Google Scholar
  9. 9.
    Maedche, A., Staab, S.: Ontology learning for the semantic web. IEEE Intell. Syst. 16(2), 72–79 (2001)CrossRefGoogle Scholar
  10. 10.
    Richards, T.: Clausal Form Logic. An Introduction to the Logic of Computer Reasoning. Addison-Wesley (1989)Google Scholar
  11. 11.
    Lukasová, A., Žáček, M., Vajgl, M.: Reasoning in Graph-based clausal form logic. IJCSI Int. J. Comput. Sci. Issues 9, 37–43 (2012). ISSN 1694-0814Google Scholar
  12. 12.
    Lukasová, A., Žáček, M., Vajgl, M., Kotyrba, M.: Resolution reasoning by RDF clausal form logic. IJCSI Int. J. Comput. Sci. Issues 9(3) (2012). ISSN (Online): 1694-0814Google Scholar
  13. 13.
    Lukasová, A., Žáček, M., Vajgl, M.: Building a non-monotonic default theory in GCFL graph-version of RDF. In: Modern Trends and Techniques in Computer Science, AISC 285, pp. 455–466. Springer International Publishing, Springer International Publishing, Switzerland (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Informatics and Computers, Faculty of ScienceUniversity of OstravaOstravaCzech Republic

Personalised recommendations