Verification of Context-Sensitive Knowledge and Action Bases

  • Diego Calvanese
  • İsmail İlkan Ceylan
  • Marco Montali
  • Ario Santoso
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8761)


Knowledge and Action Bases (KABs) have been recently proposed as a formal framework to capture the dynamics of systems which manipulate Description Logic (DL) Knowledge Bases (KBs) through action execution. In this work, we enrich the KAB setting with contextual information, making use of different context dimensions. On the one hand, context is determined by the environment using context-changing actions that make use of the current state of the KB and the current context. On the other hand, it affects the set of TBox assertions that are relevant at each time point, and that have to be considered when processing queries posed over the KAB. Here we extend to our enriched setting the results on verification of rich temporal properties expressed in μ-calculus, which had been established for standard KABs. Specifically, we show that under a run-boundedness condition, verification stays decidable and does not incur in any additional cost in terms of worst-case complexity.We also show how to adapt syntactic conditions ensuring run-boundedness so as to account for contextual information, taking into account context-dependent activation of TBox assertions.


Model Check Transition System Action Base Multiagent System Free Variable 
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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  1. 1.
    Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P.F. (eds.): The Description Logic Handbook: Theory, Implementation and Applications. Cambridge University Press (2003)Google Scholar
  2. 2.
    Baader, F., Knechtel, M., Peñaloza, R.: Context-dependent views to axioms and consequences of semantic web ontologies. John Wiley & Sons 12–13, 22–40 (2012)Google Scholar
  3. 3.
    Bagheri Hariri, B., Calvanese, D., De Giacomo, G., Deutsch, A., Montali, M.: Verification of relational data-centric dynamic systems with external services. In: Proc. of the 32nd ACM SIGACT SIGMOD SIGAI Symp. on Principles of Database Systems (PODS), pp. 163–174 (2013)Google Scholar
  4. 4.
    Bagheri Hariri, B., Calvanese, D., Montali, M., De Giacomo, G., De Masellis, R., Felli, P.: Description logic knowledge and action bases. J. of Artificial Intelligence Research 46, 651–686 (2013)zbMATHGoogle Scholar
  5. 5.
    Borgida, A., Serafini, L.: Distributed description logics: Assimilating information from peer sources. J. on Data Semantics 1, 153–184 (2003)Google Scholar
  6. 6.
    Bozzato, L., Ghidini, C., Serafini, L.: Comparing contextual and flat representations of knowledge: A concrete case about football data. In: Proc. of the 7th Int. Conf. on Knowledge Capture (K-CAP), pp. 9–16. ACM Press (2013)Google Scholar
  7. 7.
    Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M., Poggi, A., Rodríguez-Muro, M., Rosati, R.: Ontologies and databases: The DL-Lite approach. In: Tessaris, S., Franconi, E., Eiter, T., Gutierrez, C., Handschuh, S., Rousset, M.-C., Schmidt, R.A. (eds.) Reasoning Web. LNCS, vol. 5689, pp. 255–356. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M., Rosati, R.: EQL-Lite: Effective first-order query processing in description logics. In: Proc. of the 20th Int. Joint Conf. on Artificial Intelligence (IJCAI), pp. 274–279 (2007)Google Scholar
  9. 9.
    Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M., Rosati, R.: Tractable reasoning and efficient query answering in description logics: The DL-Lite family. J. of Automated Reasoning 39(3), 385–429 (2007)CrossRefzbMATHGoogle Scholar
  10. 10.
    Calvanese, D., De Giacomo, G., Lembo, D., Montali, M., Santoso, A.: Ontology-based governance of data-aware processes. In: Krötzsch, M., Straccia, U. (eds.) RR 2012. LNCS, vol. 7497, pp. 25–41. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  11. 11.
    Calvanese, D., De Giacomo, G., Montali, M., Patrizi, F.: Verification and synthesis in description logic based dynamic systems. In: Faber, W., Lembo, D. (eds.) RR 2013. LNCS, vol. 7994, pp. 50–64. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  12. 12.
    Calvanese, D., Kharlamov, E., Montali, M., Santoso, A., Zheleznyakov, D.: Verification of inconsistency-aware knowledge and action bases. In: Proc. of the 23rd Int. Joint Conf. on Artificial Intelligence, IJCAI (2013)Google Scholar
  13. 13.
    Ceylan, İ.İ., Peñaloza, R.: The Bayesian description logic \(\mathcal{BEL}\). In: Demri, S., Kapur, D., Weidenbach, C. (eds.) IJCAR 2014. LNCS, vol. 8562, pp. 480–494. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  14. 14.
    Clarke, E.M., Grumberg, O., Peled, D.A.: Model checking. The MIT Press, Cambridge (1999)Google Scholar
  15. 15.
    Deutsch, A., Hull, R., Patrizi, F., Vianu, V.: Automatic verification of data-centric business processes. In: Proc. of the 12th Int. Conf. on Database Theory (ICDT), pp. 252–267 (2009)Google Scholar
  16. 16.
    Fagin, R., Kolaitis, P.G., Miller, R.J., Popa, L.: Data exchange: Semantics and query answering. Theoretical Computer Science 336(1), 89–124 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Giunchiglia, F., Bouquet, P.: Introduction to contextual reasoning. an artificial intelligence perspective. In: Perspectives on Cognitive Science, pp. 138–159. NBU Press (1997)Google Scholar
  18. 18.
    Klarman, S., Gutiérrez-Basulto, V.: \(\mathcal{ALC}_\mathcal{ALC}\): A context description logic. In: Janhunen, T., Niemelä, I. (eds.) JELIA 2010. LNCS, vol. 6341, pp. 208–220. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  19. 19.
    Limonad, L., De Leenheer, P., Linehan, M., Hull, R., Vaculín, R.: Ontology of dynamic entities. In: Atzeni, P., Cheung, D., Ram, S. (eds.) ER 2012. LNCS, vol. 7532, pp. 345–358. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  20. 20.
    McCarthy, J.: Generality in artificial intelligence. Commun. ACM 30(12), 1030–1035 (1987)CrossRefzbMATHGoogle Scholar
  21. 21.
    McCarthy, J.: Notes on formalizing context. In: Proc. of the 13th Int. Joint Conf. on Artificial Intelligence (IJCAI), pp. 555–560 (1993)Google Scholar
  22. 22.
    Montali, M., Calvanese, D., De Giacomo, G.: Verification of data-aware commitment-based multiagent systems. In: Proc. of the 13th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2014), pp. 157–164 (2014)Google Scholar
  23. 23.
    Park, D.M.R.: Finiteness is Muineffable. Theoretical Computer Science 3(2), 173–181 (1976)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Serafini, L., Homola, M.: Contextualized knowledge repositories for the semantic web. J. of Web Semantics 12, 64–87 (2012)CrossRefGoogle Scholar
  25. 25.
    Stirling, C.: Modal and Temporal Properties of Processes. Springer (2001)Google Scholar
  26. 26.
    Vianu, V.: Automatic verification of database-driven systems: A new frontier. In: Proc. of the 12th Int. Conf. on Database Theory (ICDT), pp. 1–13 (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Diego Calvanese
    • 1
  • İsmail İlkan Ceylan
    • 2
  • Marco Montali
    • 1
  • Ario Santoso
    • 1
  1. 1.Free University of Bozen-BolzanoItaly
  2. 2.Technische Universität DresdenGermany

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