Skip to main content
Log in

Data Complexity of Query Answering in Expressive Description Logics via Tableaux

  • Published:
Journal of Automated Reasoning Aims and scope Submit manuscript

Abstract

The logical foundations of the standard web ontology languages are provided by expressive Description Logics (DLs), such as \(\mathcal{SHIQ}\) and \(\mathcal{SHOIQ}\). In the Semantic Web and other domains, ontologies are increasingly seen also as a mechanism to access and query data repositories. This novel context poses an original combination of challenges that has not been addressed before: (i) sufficient expressive power of the DL to capture common data modelling constructs; (ii) well established and flexible query mechanisms such as those inspired by database technology; (iii) optimisation of inference techniques with respect to data size, which typically dominates the size of ontologies. This calls for investigating data complexity of query answering in expressive DLs. While the complexity of DLs has been studied extensively, few tight characterisations of data complexity were available, and the problem was still open for most DLs of the \(\mathcal{SH}\) family and for standard query languages like conjunctive queries and their extensions. We tackle this issue and prove a tight coNP upper bound for positive existential queries without transitive roles in \(\mathcal{SHOQ{\text{,}}\, SHIQ}\), and \(\mathcal{SHOI}\). We thus establish that, for a whole range of sublogics of \(\mathcal{SHOIQ}\) that contain \(\mathcal{AL}\), answering such queries has coNP-complete data complexity. We obtain our result by a novel tableaux-based algorithm for checking query entailment, which uses a modified blocking condition in the style of Carin. The algorithm is sound for \(\mathcal{SHOIQ}\), and shown to be complete for all considered proper sublogics in the \(\mathcal{SH}\) family.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Baader, F., Hanschke, P.: A schema for integrating concrete domains into concept languages. In: Proc. of the 12th Int. Joint Conf. on Artificial Intelligence (IJCAI’91), pp. 452–457, Sydney, 26 August 1991

  2. Baader, F., Sattler, U.: An overview of tableau algorithms for description logics. Stud. Log. 69(1), 5–40 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  3. Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P.F. (eds.): The Description Logic Handbook: Theory, Implementation and Applications. Cambridge University Press, Cambridge (2003)

    MATH  Google Scholar 

  4. Berardi, D., Calvanese, D., De Giacomo, G.: Reasoning on UML class diagrams. Artif. Intell. 168(1–2), 70–118 (2005)

    Article  MATH  Google Scholar 

  5. Borgida, A., Brachman, R.J.: Conceptual modeling with description logics. In: Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P.F. (eds.) The Description Logic Handbook: Theory, Implementation and Applications, chap 10, pp. 349–372. Cambridge University Press, Cambridge (2003)

  6. Calvanese, D., De Giacomo, G.: Expressive description logics. In: Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P.F. (eds.) The Description Logic Handbook: Theory, Implementation and Applications, chap 5, pp. 178–218. Cambridge University Press, Cambridge (2003)

  7. Calvanese, D., De Giacomo, G., Lenzerini, M.: On the decidability of query containment under constraints. In: Proc. of the 17th ACM SIGACT SIGMOD SIGART Symp. on Principles of Database Systems (PODS’98), pp. 149–158. ACM, New York (1998)

    Google Scholar 

  8. Calvanese, D., Lenzerini, M., Nardi, D.: Unifying class-based representation formalisms. J. Artif. Intell. Res. 11, 199–240 (1999)

    MATH  MathSciNet  Google Scholar 

  9. Calvanese, D., De Giacomo, G., Lenzerini, M.: Answering queries using views over description logics knowledge bases. In: Proc. of the 17th Nat. Conf. on Artificial Intelligence (AAAI 2000), pp. 386–391. AAAI, Menlo Park (2000)

    Google Scholar 

  10. Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M., Rosati, R.: DL-Lite: tractable description logics for ontologies. In: Proc. of the 20th Nat. Conf. on Artificial Intelligence (AAAI 2005), pp. 602–607. AAAI, Menlo Park (2005)

    Google Scholar 

  11. Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M., Rosati, R.: Data complexity of query answering in description logics. In: Proc. of the 10th Int. Conf. on the Principles of Knowledge Representation and Reasoning (KR 2006), pp. 260–270, Lake District, 2–5 June 2006

  12. 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. Autom. Reason. 39(3), 385–429 (2007)

    Article  MATH  Google Scholar 

  13. Calvanese, D., Eiter, T., Ortiz, M.: Answering regular path queries in expressive description logics: an automata-theoretic approach. In: Proc. of the 22nd Nat. Conf. on Artificial Intelligence (AAAI 2007), pp. 391–396. AAAI, Menlo Park (2007)

    Google Scholar 

  14. Chandra, A.K., Merlin, P.M.: Optimal implementation of conjunctive queries in relational data bases. In: Proc. of the 9th ACM Symp. on Theory of Computing (STOC’77), pp. 77–90. ACM, New York (1977)

    Google Scholar 

  15. De Giacomo, G., Massacci, F.: Combining deduction and model checking into tableaux and algorithms for converse-PDL. Inf. Comput. 160(1–2), 117–137 (2000)

    Article  Google Scholar 

  16. Donini, F.M., Lenzerini, M., Nardi, D., Schaerf, A.: Deduction in concept languages: from subsumption to instance checking. J. Log. Comput. 4(4), 423–452 (1994)

    Article  MATH  MathSciNet  Google Scholar 

  17. Glimm, B., Horrocks, I., Sattler, U.: Conjunctive query answering for description logics with transitive roles. In: Proc. of the 2006 Description Logic Workshop (DL 2006), CEUR Electronic Workshop Proceedings, vol. 189. http://ceur-ws.org/Vol-189/ (2006)

  18. Glimm, B., Horrocks, I., Sattler, U.: Conjunctive query entailment for \(\mathcal{SHOQ}\). In: Proc. of the 2007 Description Logic Workshop (DL 2007), CEUR Electronic Workshop Proceedings, vol. 250, pp. 65–75. http://ceur-ws.org/Vol-250/ (2007)

  19. Glimm, B., Horrocks, I., Lutz, C., Sattler, U.: Conjunctive query answering for the description logic \(\mathcal{SHIQ}\). J. Artif. Intell. Res. 31, 151–198 (2008)

    Google Scholar 

  20. Gottlob, G., Koch, C., Schulz, K.U.: Conjunctive queries over trees. In: Proc. of the 23rd ACM SIGACT SIGMOD SIGART Symp. on Principles of Database Systems (PODS 2004), pp. 189–200. ACM, New York (2004)

    Google Scholar 

  21. Haarslev, V., Möller, R.: RACER system description. In: Proc. of the Int. Joint Conf. on Automated Reasoning (IJCAR 2001), Springer, Lecture Notes in Artificial Intelligence, vol. 2083, pp. 701–705. Springer, Heidelberg (2001)

    Google Scholar 

  22. Heflin, J., Hendler, J.: A portrait of the Semantic Web in action. IEEE Intell. Syst. 16(2), 54–59 (2001)

    Article  Google Scholar 

  23. Horrocks, I.: The FaCT system. In: de Swart, H. (ed.) Proc. of the 7th Int. Conf. on Automated Reasoning with Analytic Tableaux and Related Methods (TABLEAUX’98), Springer, Lecture Notes in Artificial Intelligence, vol. 1397, pp. 307–312. Springer, New York (1998)

    Chapter  Google Scholar 

  24. Horrocks, I., Sattler, U.: A tableau decision procedure for \(\mathcal{SHOIQ}\). J. Autom. Reason. 39(3), 249–276 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  25. Horrocks, I., Tessaris, S.: A conjunctive query language for description logic ABoxes. In: Proc. of the 17th Nat. Conf. on Artificial Intelligence (AAAI 2000), pp. 399–404 (2000)

  26. Horrocks, I., Sattler, U., Tobies, S.: Reasoning with individuals for the description logic \(\mathcal{SHIQ}\). In: McAllester, D. (ed.) Proc. of the 17th Int. Conf. on Automated Deduction (CADE 2000), vol. 1831, pp. 482–496. Springer, Lecture Notes in Computer Science. Springer, New York (2000)

  27. Horrocks, I., Patel-Schneider, P.F., van Harmelen, F.: From \(\mathcal{SHIQ}\) and RDF to OWL: the making of a web ontology language. J. Web Semant. 1(1), 7–26 (2003)

    Google Scholar 

  28. Hustadt, U., Motik, B., Sattler, U.: A decomposition rule for decision procedures by resolution-based calculi. In: Proc. of the 11th Int. Conf. on Logic for Programming, Artificial Intelligence and Reasoning (LPAR 2004), pp. 21–35, Montevideo, 14–18 March 2005 (2004)

  29. Hustadt, U., Motik, B., Sattler, U.: Reducing \(\mathcal{SHIQ}\)-description logic to disjunctive datalog programs. In: Proc. of the 9th Int. Conf. on the Principles of Knowledge Representation and Reasoning (KR 2004), pp. 152–162, Whistler, 2–5 June 2004

  30. Hustadt, U., Motik, B., Sattler, U.: Data complexity of reasoning in very expressive description logics. In: Proc. of the 19th Int. Joint Conf. on Artificial Intelligence (IJCAI 2005), pp. 466–471, Edinburgh, 30 July–5 August 2005

  31. Kazakov, Y., Motik, B.: A resolution-based decision procedure for \(\mathcal{SHOIQ}\). J. Autom. Reason. 40(2–3), 89–116 (2008)

    Article  MATH  Google Scholar 

  32. Lenzerini, M.: Data integration: a theoretical perspective. In: Proc. of the 21st ACM SIGACT SIGMOD SIGART Symp. on Principles of Database Systems (PODS 2002), pp. 233–246. ACM, New York (2002)

    Google Scholar 

  33. Levy, A.Y., Rousset, M.C.: Combining Horn rules and description logics in CARIN. Artif. Intell. 104(1–2), 165–209 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  34. Lutz, C.: Description logics with concrete domains: a survey. In: Balbiani, P., Suzuki, N.Y., Wolter, F., Zakharyaschev, M. (eds.) Advances in Modal Logics, vol. 4. King’s College, Edmonton (2003)

    Google Scholar 

  35. Lutz, C.: Inverse roles make conjunctive queries hard. In: Proc. of the 2007 Description Logic Workshop (DL 2007), CEUR Electronic Workshop Proceedings, vol. 250, pp. 100–111. http://ceur-ws.org/Vol-250/ (2007)

  36. Motik, B., Shearer, R., Horrocks, I.: Optimized reasoning in description logics using hypertableaux. In: Proc. of the 21st Int. Conf. on Automated Deduction (CADE 2007), Springer, Lecture Notes in Computer Science, vol. 4603, pp. 67–83. Springer, Heidelberg (2007)

    Google Scholar 

  37. Ortiz, M.M., Calvanese, D., Eiter, T.: Characterizing data complexity for conjunctive query answering in expressive description logics. In: Proc. of the 21st Nat. Conf. on Artificial Intelligence (AAAI 2006), pp. 275–280. AAAI, Menlo Park (2006)

    Google Scholar 

  38. Patel-Schneider, P., Hayes, P., Horrocks, I.: OWL Web Ontology Language semantics and abstract syntax. W3C Recommendation. http://www.w3.org/TR/owl-semantics/

  39. Schaerf, A.: On the complexity of the instance checking problem in concept languages with existential quantification. J. Intell. Inf. Sys. 2, 265–278 (1993)

    Article  MathSciNet  Google Scholar 

  40. Schaerf, A.: Reasoning with individuals in concept languages. Data Knowl. Eng. 13(2), 141–176 (1994)

    Article  Google Scholar 

  41. Tobies, S.: The complexity of reasoning with cardinality restrictions and nominals in expressive description logics. J. Artif. Intell. Res. 12, 199–217 (2000)

    MATH  MathSciNet  Google Scholar 

  42. Tobies, S.: Complexity results and practical algorithms for logics in knowledge representation. Ph.D. thesis, LuFG Theoretical Computer Science, RWTH-Aachen, Germany (2001)

  43. Vardi, M.Y.: The complexity of relational query languages. In: Proc. of the 14th ACM SIGACT Symp. on Theory of Computing (STOC’82), pp. 137–146. ACM, New York (1982)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Magdalena Ortiz.

Additional information

This work has been partially supported by FET project TONES (Thinking ONtologiES), funded within the EU 6th Framework Programme under contract FP6-7603, by the PRIN 2006 project NGS (New Generation Search), funded by MIUR, and by the Mexican National Council for Science and Technology (CONACYT).

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ortiz, M., Calvanese, D. & Eiter, T. Data Complexity of Query Answering in Expressive Description Logics via Tableaux. J Autom Reasoning 41, 61–98 (2008). https://doi.org/10.1007/s10817-008-9102-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10817-008-9102-9

Keywords

Navigation