A GUI Tool for Plausible Reasoning in the Semantic Web Using MEBN

  • Rommel N. Carvalho
  • Marcelo Ladeira
  • Laécio L. Santos
  • Shou Matsumoto
  • Paulo Cesar G. Costa
Part of the Studies in Computational Intelligence book series (SCI, volume 169)


As the work with semantics and services grows more ambitious in the Semantic Web community, there is an increasing appreciation on the need for principled approaches for representing and reasoning under uncertainty. Reacting to this trend, the World Wide Web Consortium (W3C) has recently created the Uncertainty Reasoning for the World Wide Web Incubator Group (URW3-XG) to better define the challenge of reasoning with and representing uncertain information available through the World Wide Web and related WWW technologies. In according to the URW3-XG effort this Chapter presents the implementation of a graphical user interface (GUI) for building probabilistic ontologies, an application programming interface (API) for saving and loading these ontologies and a grammar proposal to specify formulas for creating conditional probabilistic tables dynamically. The language used for building probabilistic ontologies is Probabilistic OWL (PR-OWL), an extension for OWL based on Multi-Entity Bayesian Network (MEBN). The GUI, API, and the compiler for the proposed grammar were implemented into UnBBayes-MEBN, an open source, Java-based application that provides an easy way for building probabilistic ontologies and reasoning based on the PR-OWL/MEBN framework.


Bayesian Network Graphical User Interface Application Programming Interface Plausible Reasoning Magnetic Disturbance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Carvalho, R.N., Santos, L.L., Ladeira, M., Costa, P.C.G.: A GUI Tool for Plausible Reasoning in the Semantic Web using MEBN. In: Proceedings of the Seventh International Conference on Intelligent Systems Design and Applications, Rio de Janeiro, Brazil, October 2007, pp. 381–386. IEEE Computer Society Press, Los Alamitos (2007)CrossRefGoogle Scholar
  2. 2.
    Costa, P.C.G.: Bayesian Semantics for the Semantic Web. PhD thesis, Department of Systems Engineering and Operational Research, George Mason University (2005)Google Scholar
  3. 3.
    Costa, P.C.G., Laskey, K.B.: PR-OWL: A Framework for Probabilistic Ontologies. In: Proceedings of the Fourth International Conference on Formal Ontology in Information Systems, Baltimore, USA (2006)Google Scholar
  4. 4.
    Costa, P.C.G., Laskey, K.B., Laskey, K.J.: PR-OWL: A Bayesian Ontology Language for the Semantic Web. In: Proceedings of the ISWC Workshop on Uncertainty Reasoning for the Semantic Web, Galway, Ireland (2005)Google Scholar
  5. 5.
    Heflin, J.: OWL Web Ontology Language - Use Cases and Requirements (W3C Recommendation) (2004),
  6. 6.
    Laskey, K.B.: MEBN: A Language for First-Order Bayesian Knowledge Bases. Artificial Intelligence 172(2–3), 172–225 (2007)MathSciNetGoogle Scholar
  7. 7.
    Laskey, K.B., Costa, P.C.G.: Of Klingons and Starships: Bayesian Logic for the 23rd Century. In: Proceedings of the Twenty-first Conference Uncertainty in Artificial Intelligence (UAI 2005), Edinburgh, Scotland, pp. 346–353 (2005)Google Scholar
  8. 8.
    Laskey, K.J., Laskey, K.B., Costa P.C.G.: Uncertainty Reasoning for the World Wide Web Incubator Group Charter (W3C Incubator Activity) (2007),
  9. 9.
    Mahoney, S.M., Laskey, K.B.: Constructing Situation Specific Networks. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI 1998), University of Wisconsin Business School, Madison, WI, USA, pp. 370–378 (July 1998)Google Scholar
  10. 10.
    Patel-Schneider, P.F., Hayes, P., Horrocks, I.: OWL Web Ontology Language - Semantics and Abstract Syntax (W3C Recommendation) (2004),
  11. 11.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, San Mateo (1988)Google Scholar
  12. 12.
    Schum, D.A.: Evidential Foundations of Probabilistic Reasoning. Wiley Interscience, Hoboken (1994)Google Scholar
  13. 13.
    Shafer, G.: The Construction of Probability Arguments. Boston University Law Review 66(3–4), 799–823 (1986)Google Scholar
  14. 14.
    Toffler, A.: The Third Wave. Morrow, New York (1980)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Rommel N. Carvalho
    • 1
  • Marcelo Ladeira
    • 1
  • Laécio L. Santos
    • 1
  • Shou Matsumoto
    • 1
  • Paulo Cesar G. Costa
    • 2
  1. 1.Computer Science DepartmentUniversity of BrasiliaBrasiliaBrazil
  2. 2.Center of Excellence in C4IGeorge Mason UniversityFairfaxUSA

Personalised recommendations