Ontology Adaptation upon Updates

  • Alessandro Solimando
  • Giovanna Guerrini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7955)


Ontologies, like any other model, change over time due to modifications in the modeled domain, deeper understanding of the domain by the modeler, error corrections, simple refactoring or shift of modeling granularity level. Local changes usually impact the remainder of the ontology as well as any other data and metadata defined over it. The massive size of ontologies and their possible fast update rate requires automatic adaptation methods for relieving ontology engineers from a manual intervention, in order to allow them to focus mainly on high-level inspection. This paper, in spirit of the Principle of minimal change, proposes a fully automatic ontology adaptation approach that reacts to ontology updates and computes sound reformulations of ontological axioms triggered by the presence of certain preconditions. The rule-based adaptation algorithm covers up to \(\mathcal{SROIQ}\) DL.


Description Logic Belief Revision Adaptation Algorithm Ontology Evolution Adaptation Rule 
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.: Least Common Subsumers and Most Specific Concepts in a Description Logic with Existential Restrictions and Terminological Cycles. In: International Joint Conference on Artificial Intelligence, vol. 18, pp. 319–324 (2003)Google Scholar
  2. 2.
    Flouris, G., Manakanatas, D., Kondylakis, H., Plexousakis, D., Antoniou, G.: Ontology change: Classification and survey. Knowl. Eng. Rev. 23(2), 117–152 (2008)CrossRefGoogle Scholar
  3. 3.
    Hartung, M., Groß, A., Rahm, E.: COnto–Diff: Generation of Complex Evolution Mappings for Life Science Ontologies. Journal of Biomedical Informatics (2012)Google Scholar
  4. 4.
    Hartung, M., Groß, A., Rahm, E.: Rule-based Generation of Diff Evolution Mappings between Ontology Versions. CoRR abs/1010.0122 (2010)Google Scholar
  5. 5.
    Horrocks, I., Kutz, O., Sattler, U.: The Even More Irresistible SROIQ. In: Principles of Knowledge Representation and Reasoning – KR 2006, pp. 57–67 (2006)Google Scholar
  6. 6.
    Katsuno, H., Mendelzon, A.O.: Propositional Knowledge Base Revision and Minimal Change. Artificial Intelligence 52(3), 263–294 (1991)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Kondylakis, H., Plexousakis, D.: Ontology Evolution: Assisting Query Migration. In: Atzeni, P., Cheung, D., Ram, S. (eds.) ER 2012. LNCS, vol. 7532, pp. 331–344. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  8. 8.
    Noy, N., Klein, M.: Ontology Evolution: Not the same as Schema Evolution. Knowledge and Information Systems 6(4), 428–440 (2004)CrossRefGoogle Scholar
  9. 9.
    Ribeiro, M.M., Wassermann, R., Antoniou, G., Flouris, G., Pan, J.: Belief Contraction in Web-Ontology Languages. In: Workshop on Ontology Dynamics, IWOD (2009)Google Scholar
  10. 10.
    Rudolph, S.: Foundations of Description Logics. In: Polleres, A., d’Amato, C., Arenas, M., Handschuh, S., Kroner, P., Ossowski, S., Patel-Schneider, P. (eds.) Reasoning Web 2011. LNCS, vol. 6848, pp. 76–136. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  11. 11.
    Stojanovic, L.: Methods and Tools for Ontology Evolution. Ph.D. thesis, University of Karlsruhe (2004)Google Scholar
  12. 12.
    W3C as Hitzler, P., Krötzsch, M., Parsia, B., Patel-Schneider, P.F., Rudolph, S.: OWL 2 Web Ontology Language Primer (2009),

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alessandro Solimando
    • 1
  • Giovanna Guerrini
    • 1
  1. 1.Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei SistemiUniversità di GenovaItaly

Personalised recommendations