Knowledge Representation on the Web Revisited: The Case for Prototypes

  • Michael CochezEmail author
  • Stefan Decker
  • Eric Prud’hommeaux
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9981)


Recently, RDF and OWL have become the most common knowledge representation languages in use on the Web, propelled by the recommendation of the W3C. In this paper we examine an alternative way to represent knowledge based on Prototypes. This Prototype-based representation has different properties, which we argue to be more suitable for data sharing and reuse on the Web. Prototypes avoid the distinction between classes and instances and provide a means for object-based data sharing and reuse.

In this paper we discuss the requirements and design principles for Knowledge Representation based on Prototypes on the Web, after which we propose a formal syntax and semantics. We further show how to embed knowledge representation based on Prototypes in the current Semantic Web stack and report on an implementation and practical evaluation of the system.


Linked data Knowledge representation Prototypes 



Stefan Decker would like to thank Pat Hayes, Eric Neumann, and Hong-Gee Kim for discussions about Prototypes and Knowledge Representation in general.

Michael Cochez performed parts of this research at the Industrial Ontologies Group of the University of Jyväskylä, Finland, and at the Insight Centre for Data Analytics in Galway, Ireland.


  1. 1.
    Black, A.P., Hutchinson, N.C., Jul, E., Levy, H.M.: The development of the emerald programming language. In: Proceedings of the Third ACM SIGPLAN Conference on History of Programming Languages, HOPL III, pp. 11-1-11-51. ACM, New York (2007).
  2. 2.
    Brachman, R.J.: A structural paradigm for representing knowledge. Technical report, BBN Report 3605, Bolt, Beraneck and Newman Inc., Cambridge, MA (1978)Google Scholar
  3. 3.
    Cochez, M., Decker, S., Prud’hommeaux, E.G.: Knowledge representation on the web revisited: tools for prototype based ontologies. In: arXiv (2016)., arXiv:1607.04809 [cs.AI]
  4. 4.
    Cochez, M., Mou, H.: Twister tries: Approximate hierarchical agglomerative clustering for average distance in linear time. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp. 505–517. ACM (2015)Google Scholar
  5. 5.
    Cook, W.R., Hill, W., Canning, P.S.: Inheritance is not subtyping. In: Proceedings of the 17th ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, POPL 1990, pp. 125–135. ACM, New York (1990).
  6. 6.
    Decker, S., Fensel, D., van Harmelen, F., Horrocks, I., Melnik, S., Klein, M., Broekstra, J.: Knowledge representation on the web. In: Proceedings of the 2000 Description Logic Workshop (DL 2000), CEUR, vol. 33, pp. 89–98 (2000). (
  7. 7.
    Duerst, M., Suignard, M.: Internationalized resource identifiers (IRIS). RFC 3987, RFC Editor, January 2005.
  8. 8.
    European Computer Manufacturers Association and others: Standard ecma-262 ecmascrippt 2015 language specification, June 2015Google Scholar
  9. 9.
    Gabriel, R.: The rise of “worse is better”. In: LISP: Good News, Bad News, How to Win Big 2, 5 (1991)Google Scholar
  10. 10.
    Hayes, P.J.: The logic of frames. In: Metzing, D. (ed.) Frame Conceptions and Text Understanding, pp. 46–61. Walter de Gruyter and Co., Berlin (1979)Google Scholar
  11. 11.
    Horrocks, I., Sattler, U., Tobies, S.: Practical reasoning for expressive description logics. In: Ganzinger, H., McAllester, D., Voronkov, A. (eds.) Proceedings of the 6th International Conference on Logic for Programming and Automated Reasoning (LPAR 1999). LNAI, vol. 1705, pp. 161–180. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  12. 12.
    Horrocks, I.: Using an expressive description logic: FaCT or fiction? In: Proceedings of the 6th International Conference on Principles of Knowledge Representation and Reasoning (KR 1998), pp. 636–647 (1998)Google Scholar
  13. 13.
    Horrocks, I., Patel-Schneider, P.F., Harmelen, F.: From SHIQ and RDF to OWL: the making of a web ontology language. J. Web Semant. 1(1), 7–26 (2003)CrossRefGoogle Scholar
  14. 14.
    Israel, D.J., Brachman, R.J.: Some remarks on the semantics of representation languages. In: Brodie, M.L., Mylopoulos, J., Schmidt, J.W. (eds.) On Conceptual Modelling: Perspectives from Artificial Intelligence, Databases, and Programming Languages, pp. 119–142. Springer, New York (1984)CrossRefGoogle Scholar
  15. 15.
    Karp, P.D.: The design space of frame knowledge representation systems. Technical report, SRI International Artificial Intelligence (1993)Google Scholar
  16. 16.
    Levesque, H.J., Brachman, R.J.: A fundamental tradeoff in knowledge representation and reasoning (revised version). In: Brachman, R.J., Levesque, H.J. (eds.) Readings in Knowledge Representation, pp. 41–70. Kaufmann, Los Altos (1985)Google Scholar
  17. 17.
    Lieberman, H.: Using prototypical objects to implement shared behavior in object-oriented systems. In: Conference Proceedings on Object-oriented Programming Systems, Languages and Applications, OOPLSA 1986, pp. 214–223. ACM, New York (1986).
  18. 18.
    Liskov, B.: Keynote address - data abstraction and hierarchy. SIGPLAN Not. 23(5), 17–34 (1987). CrossRefGoogle Scholar
  19. 19.
    Minsky, M.: A framework for representing knowledge. Technical report, Massachusetts Institute of Technology, Cambridge, MA, USA (1974)Google Scholar
  20. 20.
    Minsky, M.: A framework for representing knowledge. In: Haugeland, J. (ed.) Mind Design: Philosophy, Psychology, Artificial Intelligence, pp. 95–128. MIT Press, Cambridge (1981)Google Scholar
  21. 21.
    Mitchell, T.M., Allen, J., Chalasani, P., Cheng, J., Etzioni, O., Ringuette, M., Schlimmer, J.C.: Theo: a framework for self-improving systems. In: Architectures for Intelligence, pp. 323–356 (1991)Google Scholar
  22. 22.
    Prud’hommeaux, E., Labra Gayo, J.E., Solbrig, H.: Shape expressions: an RDF validation and transformation language. In: Proceedings of the 10th International Conference on Semantic Systems, pp. 32–40. ACM (2014)Google Scholar
  23. 23.
    Quillian, M.R.: Semantic memory. Technical report, DTIC Document (1966)Google Scholar
  24. 24.
    Rector, A.L.: Defaults, context, and knowledge: alternatives for owl-indexed knowledge bases. In: Altman, R.B., Dunker, A.K., Hunter, L., Jung, T.A., Klein, T.E. (eds.) Pacific Symposium on Biocomputing, pp. 226–237. World Scientific, Singapore (2004). Google Scholar
  25. 25.
    Rodriguez, N.D.L.R., Ierusalimschy, R., Rangel, J.L.: Types in school. SIGPLAN Not 28(8), 81–89 (1993). CrossRefGoogle Scholar
  26. 26.
    Taivalsaari, A.: On the notion of inheritance. ACM Comput. Surv. (CSUR) 28(3), 438–479 (1996)CrossRefGoogle Scholar
  27. 27.
    Ungar, D., Smith, R.B.: Self. In: Ryder, B.G., Hailpern, B. (eds.) Proceedings of the Third ACM SIGPLAN History of Programming Languages Conference (HOPL-III), San Diego, California, USA, 9–10 June 2007, pp. 1–50. ACM (2007).
  28. 28.
    Victor, T., Dalzell, T.: The Concise New Partridge Dictionary of Slang and Unconventional English. Routledge, London (2007)Google Scholar
  29. 29.
    Wille, R.: Formal concept analysis as mathematical theory of concepts and concept hierarchies. In: Ganter, B., Stumme, G., Wille, R. (eds.) Formal Concept Analysis. LNCS (LNAI), vol. 3626, pp. 1–33. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Michael Cochez
    • 1
    • 2
    • 4
    Email author
  • Stefan Decker
    • 1
    • 2
  • Eric Prud’hommeaux
    • 3
  1. 1.Fraunhofer Institute for Applied Information Technology FITSankt AugustinGermany
  2. 2.Informatik 5RWTH Aachen UniversityAachenGermany
  3. 3.World Wide Web Consortium (W3C)Stata Center, MITCambridgeUSA
  4. 4.Department of Mathematical Information TechnologyUniversity of JyvaskylaUniversity of JyvaskylaFinland

Personalised recommendations