Performance Heterogeneity and Approximate Reasoning in Description Logic Ontologies

  • Rafael S. Gonçalves
  • Bijan Parsia
  • Ulrike Sattler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7649)


Due to the high worst case complexity of the core reasoning problem for the expressive profiles of OWL 2, ontology engineers are often surprised and confused by the performance behaviour of reasoners on their ontologies. Even very experienced modellers with a sophisticated grasp of reasoning algorithms do not have a good mental model of reasoner performance behaviour. Seemingly innocuous changes to an OWL ontology can degrade classification time from instantaneous to too long to wait for. Similarly, switching reasoners (e.g., to take advantage of specific features) can result in wildly different classification times. In this paper we investigate performance variability phenomena in OWL ontologies, and present methods to identify subsets of an ontology which are performance-degrading for a given reasoner. When such (ideally small) subsets are removed from an ontology, and the remainder is much easier for the given reasoner to reason over, we designate them “hot spots”. The identification of these hot spots allows users to isolate difficult portions of the ontology in a principled and systematic way. Moreover, we devise and compare various methods for approximate reasoning and knowledge compilation based on hot spots. We verify our techniques with a select set of varyingly difficult ontologies from the NCBO BioPortal, and were able to, firstly, successfully identify performance hot spots against the major freely available DL reasoners, and, secondly, significantly improve classification time using approximate reasoning based on hot spots.


Description Logic Approximate Reasoning Knowledge Compilation Performance Heterogeneity Original Ontology 
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.


  1. 1.
    Baader, F., Lutz, C., Suntisrivaraporn, B.: CEL — A Polynomial-Time Reasoner for Life Science Ontologies. In: Furbach, U., Shankar, N. (eds.) IJCAR 2006. LNCS (LNAI), vol. 4130, pp. 287–291. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Baader, F., Lutz, C., Suntisrivaraporn, B.: Efficient reasoning in \(\mathcal{EL}^+\). In: Proc. of DL 2006 (2006)Google Scholar
  3. 3.
    Bail, S., Parsia, B., Sattler, U.: JustBench: A Framework for OWL Benchmarking. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010, Part I. LNCS, vol. 6496, pp. 32–47. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Charaniya, S.: Facilitating DL Reasoners Through Ontology Partitioning. Master’s thesis, Nagpur University, India (2006)Google Scholar
  5. 5.
    Cuenca Grau, B., Horrocks, I., Kazakov, Y., Sattler, U.: Modular reuse of ontologies: Theory and practice. J. of Artificial Intelligence Research 31 (2008)Google Scholar
  6. 6.
    Del Vescovo, C., Parsia, B., Sattler, U., Schneider, T.: The modular structure of an ontology: Atomic decomposition. In: Proc. of IJCAI 2011 (2011)Google Scholar
  7. 7.
    Horridge, M., Bechhofer, S.: The OWL API: A Java API for working with OWL 2 ontologies. In: Proc. of OWLED 2009 (2009)Google Scholar
  8. 8.
    Horrocks, I., Kutz, O., Sattler, U.: The even more irresistible \(\mathcal{SROIQ}\). In: Proc. of KR 2006 (2006)Google Scholar
  9. 9.
    Horrocks, I.: The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press (2003)Google Scholar
  10. 10.
    Horrocks, I., Patel-Schneider, P.F.: Evaluating optimised decision procedures for propositional modal k(m) satisfiability. J. of Automated Reasoning 28, 173–204 (2002)MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Kalyanpur, A., Parsia, B., Horridge, M., Sirin, E.: Finding All Justifications of OWL DL Entailments. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ISWC/ASWC 2007. LNCS, vol. 4825, pp. 267–280. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Lin, H., Sirin, E.: Pellint - a performance lint tool for Pellet. In: Proc. of OWLED-08EU (2008)Google Scholar
  13. 13.
    Noy, N.F., Shah, N.H., Whetzel, P.L., Dai, B., Dorf, M., Griffith, N., Jonquet, C., Rubin, D.L., Storey, M.A., Chute, C.G., Musen, M.A.: Bioportal: Ontologies and integrated data resources at the click of a mouse. Nucleic Acids Research 37, W170–W173 (2009)CrossRefGoogle Scholar
  14. 14.
    Patel-Schneider, P.F., Sebastiani, R.: A new general method to generate random modal formulae for testing decision procedures. J. of Artificial Intelligence Research 18, 351–389 (2003)MathSciNetzbMATHGoogle Scholar
  15. 15.
    Ren, Y., Pan, J.Z., Zhao, Y.: Soundness Preserving Approximation for TBox Reasoning. In: Proc. of AAAI 2010 (2010)Google Scholar
  16. 16.
    Rudolph, S., Tserendorj, T., Hitzler, P.: What Is Approximate Reasoning? In: Calvanese, D., Lausen, G. (eds.) RR 2008. LNCS, vol. 5341, pp. 150–164. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  17. 17.
    Sattler, U., Schneider, T., Zakharyaschev, M.: Which kind of module should I extract? In: Proc. of DL 2009 (2009)Google Scholar
  18. 18.
    Schaerf, M., Cadoli, M.: Tractable reasoning via approximation. Artificial Intelligence 74, 249–310 (1995)MathSciNetzbMATHCrossRefGoogle Scholar
  19. 19.
    Shearer, R., Motik, B., Horrocks, I.: HermiT: A highly-efficient OWL reasoner. In: Proc. of OWLED-08EU (2008)Google Scholar
  20. 20.
    Sirin, E., Parsia, B., Cuenca Grau, B., Kalyanpur, A., Katz, Y.: Pellet: A practical OWL-DL reasoner. J. of Web Semantics 5(2) (2007)Google Scholar
  21. 21.
    Tsarkov, D., Horrocks, I.: FaCT++ Description Logic Reasoner: System Description. In: Furbach, U., Shankar, N. (eds.) IJCAR 2006. LNCS (LNAI), vol. 4130, pp. 292–297. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  22. 22.
    W3C OWL Working Group: OWL 2 Web Ontology Language: Document overview. W3C Recommendation (October 27, 2009),
  23. 23.
    Wang, T.D., Parsia, B.: Ontology Performance Profiling and Model Examination: First Steps. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ISWC/ASWC 2007. LNCS, vol. 4825, pp. 595–608. Springer, Heidelberg (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Rafael S. Gonçalves
    • 1
  • Bijan Parsia
    • 1
  • Ulrike Sattler
    • 1
  1. 1.School of Computer ScienceUniversity of ManchesterManchesterUnited Kingdom

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