Coupling Tableau Algorithms for Expressive Description Logics with Completion-Based Saturation Procedures

  • Andreas Steigmiller
  • Birte Glimm
  • Thorsten Liebig
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8562)


Nowadays, saturation-based reasoners for the OWL EL profile are able to handle large ontologies such as SNOMED very efficiently. However, saturation-based reasoning procedures become incomplete if the ontology is extended with axioms that use features of more expressive Description Logics, e.g., disjunctions. Tableau-based procedures, on the other hand, are not limited to a specific OWL profile, but even highly optimised reasoners might not be efficient enough to handle large ontologies such as SNOMED. In this paper, we present an approach for tightly coupling tableau- and saturation-based procedures that we implement in the OWL DL reasoner Konclude. Our detailed evaluation shows that this combination significantly improves the reasoning performance on a wide range of ontologies.


Description Logic Node Label Representative Node Saturation Procedure Candidate Concept 
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.
    Armas Romero, A., Cuenca Grau, B., Horrocks, I.: MORe: Modular combination of OWL reasoners for ontology classification. In: Cudré-Mauroux, P., Heflin, J., Sirin, E., Tudorache, T., Euzenat, J., Hauswirth, M., Parreira, J.X., Hendler, J., Schreiber, G., Bernstein, A., Blomqvist, E. (eds.) ISWC 2012, Part I. LNCS, vol. 7649, pp. 1–16. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  2. 2.
    Baader, F., Brandt, S., Lutz, C.: Pushing the \(\mathcal{EL}\) envelope. In: Proc. 19th Int. Joint Conf. on Artificial Intelligence (IJCAI 2005), pp. 364–369. Professional Book Center (2005)Google Scholar
  3. 3.
    Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P. (eds.): The Description Logic Handbook: Theory, Implementation, and Applications, 2nd edn. Cambridge University Press (2007)Google Scholar
  4. 4.
    Gardiner, T., Horrocks, I., Tsarkov, D.: Automated benchmarking of description logic reasoners. In: Proc. 19th Int. Workshop on Description Logics (DL 2006), vol. 198. CEUR (2006)Google Scholar
  5. 5.
    Glimm, B., Horrocks, I., Motik, B., Shearer, R., Stoilos, G.: A novel approach to ontology classification. J. of Web Semantics 14, 84–101 (2012)CrossRefGoogle Scholar
  6. 6.
    Horrocks, I., Kutz, O., Sattler, U.: The even more irresistible \(\mathcal{SROIQ}\). In: Proc. 10th Int. Conf. on Principles of Knowledge Representation and Reasoning (KR 2006), pp. 57–67. AAAI Press (2006)Google Scholar
  7. 7.
    Horrocks, I., Tobies, S.: Reasoning with axioms: Theory and practice. In: Proc. 7th Int. Conf. on Principles of Knowledge Representation and Reasoning (KR 2000), pp. 285–296. Morgan Kaufmann (2000)Google Scholar
  8. 8.
    Hudek, A.K., Weddell, G.E.: Binary absorption in tableaux-based reasoning for description logics. In: Proc. 19th Int. Workshop on Description Logics (DL 2006), vol. 189. CEUR (2006)Google Scholar
  9. 9.
    Kazakov, Y.: \(\mathcal{RIQ}\) and \(\mathcal{SROIQ}\) are harder than \(\mathcal{SHOIQ}\). In: Proc. 11th Int. Conf. on Principles of Knowledge Representation and Reasoning (KR 2008), pp. 274–284. AAAI Press (2008)Google Scholar
  10. 10.
    Kazakov, Y.: Consequence-driven reasoning for Horn-\(\mathcal{SHIQ}\) ontologies. In: Proc. 21st Int. Conf. on Artificial Intelligence (IJCAI 2009), pp. 2040–2045. IJCAI (2009)Google Scholar
  11. 11.
    Matentzoglu, N., Bail, S., Parsia, B.: A corpus of OWL DL ontologies. In: Proc. 26th Int. Workshop on Description Logics (DL 2013), vol. 1014. CEUR (2013)Google Scholar
  12. 12.
    Simančík, F., Kazakov, Y., Horrocks, I.: Consequence-based reasoning beyond Horn ontologies. In: Proc. 22nd Int. Joint Conf. on Artificial Intelligence (IJCAI 2011), pp. 1093–1098. IJCAI/AAAI (2011)Google Scholar
  13. 13.
    Smith, B., Ashburner, M., Rosse, C., Bard, J., Bug, W., Ceusters, W., Goldberg, L.J., Eilbeck, K., Ireland, A., Mungall, C.J., The, O.B.I., Consortium, L.N., Rocca-Serra, P., Ruttenberg, A., Sansone, S.A., Scheuermann, R.H., Shah, N., Whetzeland, P.L., Lewis, S.: The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nature Biotechnology 25, 1251–1255 (2007)CrossRefGoogle Scholar
  14. 14.
    Song, W., Spencer, B., Du, W.: WSReasoner: A prototype hybrid reasoner for \(\mathcal{ALCHOI}\) ontology classification using a weakening and strengthening approach. In: Proc. 1st Int. Workshop on OWL Reasoner Evaluation (ORE 2012), vol. 858. CEUR (2012)Google Scholar
  15. 15.
    Steigmiller, A., Glimm, B., Liebig, T.: Nominal schema absorption. In: Proc. 23rd Int. Joint Conf. on Artificial Intelligence (IJCAI 2013), pp. 1104–1110. AAAI Press (2013)Google Scholar
  16. 16.
    Steigmiller, A., Glimm, B., Liebig, T.: Coupling tableau algorithms for the DL \(\mathcal{SROIQ}\) with completion-based saturation procedures. Tech. Rep. UIB-2014-02, University of Ulm, Ulm, Germany (2014),
  17. 17.
    Steigmiller, A., Liebig, T., Glimm, B.: Konclude: system description. J. of Web Semantics (accepted, 2014)Google Scholar
  18. 18.
    Tsarkov, D., Horrocks, I., Patel-Schneider, P.F.: Optimizing terminological reasoning for expressive description logics. J. of Automated Reasoning 39, 277–316 (2007)CrossRefzbMATHMathSciNetGoogle Scholar
  19. 19.
    W3C OWL Working Group: OWL 2 Web Ontology Language: Document Overview. W3C Recommendation (October 27, 2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Andreas Steigmiller
    • 1
  • Birte Glimm
    • 1
  • Thorsten Liebig
    • 2
  1. 1.University of UlmUlmGermany
  2. 2.derivo GmbHUlmGermany

Personalised recommendations