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ELOG: A Probabilistic Reasoner for OWL EL

  • Jan Noessner
  • Mathias Niepert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6902)

Abstract

Log-linear description logics are probabilistic logics combining several concepts and methods from the areas of knowledge representation and reasoning and statistical relational AI. We describe some of the implementation details of the log-linear reasoner ELOG. The reasoner employs database technology to dynamically transform inference problems to integer linear programs (ILP). In order to lower the size of the ILPs and reduce the complexity we employ a form of cutting plane inference during reasoning.

Keywords

Integer Linear Program Description Logic Conjunctive Query Ground Atom Markov Logic Network 
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.

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References

  1. 1.
    Aho, A., Beeri, C., Ullman, J.: The theory of joins in relational databases. ACM Transactions on Database Systems (TODS) 4(3), 297–314 (1979)CrossRefGoogle Scholar
  2. 2.
    Baader, F., Brandt, S., Lutz, C.: Pushing the \(\mathcal{EL}\) envelope. In: Proceedings of IJCAI (2005)Google Scholar
  3. 3.
    Getoor, L., Taskar, B.: Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)zbMATHGoogle Scholar
  4. 4.
    Koller, D., Levy, A., Pfeffer, A.: P-classic: A tractable probabilistic description logic. In: Proceedings of the 14th AAAI Conference on Artificial Intelligence (1997)Google Scholar
  5. 5.
    Lukasiewicz, T., Straccia, U.: Managing uncertainty and vagueness in description logics for the semantic web. J. of Web Sem. 6 (2008)Google Scholar
  6. 6.
    Niepert, M.: A Delayed Column Generation Strategy for Exact k-Bounded MAP Inference in Markov Logic Networks. In: Proceedings of UAI (2010)Google Scholar
  7. 7.
    Niepert, M., Meilicke, C., Stuckenschmidt, H.: A Probabilistic-Logical Framework for Ontology Matching. In: Proceedings of AAAI (2010)Google Scholar
  8. 8.
    Niepert, M., Noessner, J., Stuckenschmidt, H.: Log-Linear Description Logics. In: Proceedings of IJCAI (2011)Google Scholar
  9. 9.
    Poon, H., Domingos, P.: Sound and efficient inference with probabilistic and deterministic dependencies. In: Proceedings of AAAI (2006)Google Scholar
  10. 10.
    Richardson, M., Domingos, P.: Markov logic networks. Machine Learning 62(1-2) (2006)Google Scholar
  11. 11.
    Riedel, S.: Improving the accuracy and efficiency of map inference for markov logic. In: Proceedings of UAI (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jan Noessner
    • 1
  • Mathias Niepert
    • 1
  1. 1.KR & KM Research GroupUniversität MannheimMannheimGermany

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