Skip to main content

Inconsistency-Tolerant Query Answering: Rationality Properties and Computational Complexity Analysis

  • Conference paper
  • First Online:
Logics in Artificial Intelligence (JELIA 2016)

Abstract

Generalising the state of the art, an inconsistency-tolerant semantics can be seen as a couple composed of a modifier operator and an inference strategy. In this paper we deepen the analysis of such general setting and focus on two aspects. First, we investigate the rationality properties of such semantics for existential rule knowledge bases. Second, we unfold the broad landscape of complexity results of inconsistency-tolerant semantics under a specific (yet expressive) subclass of existential rules.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    For readability, we restrict our focus to Boolean conjunctive queries, however the framework and the obtained results can be directly extended to general conjunctive queries.

  2. 2.

    Note however that CAR and ICAR [16] are close to \(\langle \mathsf {RC},\forall \rangle \) and \(\langle \mathsf {RC}, \cap \rangle \) resp., but not equivalent. They could be covered by considering other elementary modifiers.

  3. 3.

    Most examples in this section are provided in DL-Lite\(_{\mathcal R}\) in order to show that some rationality properties do not hold even in this simple fragment of existential rules.

  4. 4.

    This example also shows that CAR and ICAR [16] do not satisfy ConsS (although they do when the conclusion is a single atom).

  5. 5.

    We have adopted here a formulation close to the one of KLM logical properties, even at the cost of simplicity. For instance \(\langle {\mathcal {T}},{\mathcal {A}}_{\alpha }\rangle \models \langle {\mathcal {T}},{\mathcal {A}}_{\beta }\rangle \) could have been simplified in \(\langle {\mathcal {T}},{\mathcal {A}}_{\alpha }\rangle \models {\mathcal {A}}_{\beta }\). We remind that \(\models \) and \(\equiv \) denote standard logical entailment and equivalence.

  6. 6.

    This complexity measure is usually considered for query answering problems. Only the data (here the ABox) are considered in the problem input.

  7. 7.

    PP includes NP, co-NP and \(\varTheta _2^P\).

References

  1. Arenas, M., Bertossi, L.E., Chomicki, J.: Consistent query answers in inconsistent databases. In: Proceedings of the Eighteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pp. 68–79 (1999)

    Google Scholar 

  2. Baget, J.-F., Benferhat, S., Bouraoui, Z., Croitoru, M., Mugnier, M.-L., Papini, O., Rocher, S., Tabia, K.: A general modifier-based framework for inconsistency-tolerant query answering. In: Proceedings of the Fifteenth International Conference on Principles of Knowledge Representation and Reasoning, KR (2016)

    Google Scholar 

  3. Baget, J.-F., Leclère, M., Mugnier, M.-L., Salvat, E.: On rules with existential variables: Walking the decidability line. Artif. Intell. 175(9–10), 1620–1654 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  4. Benferhat, S., Bouraoui, Z., Croitoru, M., Papini, O., Tabia, K.: Non-objection inference for inconsistency-tolerant query answering. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (2016)

    Google Scholar 

  5. Benferhat, S., Cayrol, C., Dubois, D., Lang, J., Prade, H.: Inconsistency management and prioritized syntax-based entailment. In: Proceedings of the 13th International Joint Conference on Artificial Intelligence, pp. 640–647 (1993)

    Google Scholar 

  6. Bienvenu, M.: On the complexity of consistent query answering in the presence of simple ontologies. In: Proceedings of the Twenty-Sixth Conference on Artificial Intelligence (2012)

    Google Scholar 

  7. Bienvenu, M., Bourgaux, C., Goasdoué, F.: Querying inconsistent description logic knowledge bases under preferred repair semantics. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, pp. 996–1002 (2014)

    Google Scholar 

  8. Calì, A., Gottlob, G., Pieris, A.: Towards more expressive ontology languages: the query answering problem. Artif. Intell. 193, 87–128 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  9. Calì, A., Gottlob, G., Lukasiewicz, T.: A general datalog-based framework for tractable query answering over ontologies. J. Web Sem. 14, 57–83 (2012)

    Article  Google Scholar 

  10. 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  MathSciNet  MATH  Google Scholar 

  11. Casini, G., Straccia, U.: Rational closure for defeasible description logics. In: Janhunen, T., Niemelä, I. (eds.) JELIA 2010. LNCS (LNAI), vol. 6341, pp. 77–90. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15675-5_9

    Chapter  Google Scholar 

  12. Gärdenfors, P., Makinson, D.: Nonmonotonic inference based on expectations. Artif. Intell. 65(2), 197–245 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  13. Gill, J.: Computational complexity of probabilistic turing machines. SIAM J. Comput. 6(4), 675–695 (1977)

    Article  MathSciNet  MATH  Google Scholar 

  14. Kraus, S., Lehmann, D.J., Magidor, M.: Nonmonotonic reasoning, preferential models and cumulative logics. Artif. Intell. 44(1–2), 167–207 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  15. Lembo, D., Lenzerini, M., Rosati, R., Ruzzi, M., Savo, D.F.: Inconsistency-tolerant semantics for description logics. In: Hitzler, P., Lukasiewicz, T. (eds.) RR 2010. LNCS, vol. 6333, pp. 103–117. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15918-3_9

    Chapter  Google Scholar 

  16. Lembo, D., Lenzerini, M., Rosati, R., Ruzzi, M., Savo, D.F.: Inconsistency-tolerant query answering in ontology-based data access. J. Web Sem. 33, 3–29 (2015)

    Article  Google Scholar 

  17. Lenzerini, M.: Ontology-based data management. In: Proceedings of the 6th Alberto Mendelzon International Workshop on Foundations of Data Management 2012, pp. 12–15 (2012)

    Google Scholar 

  18. Poggi, A., Lembo, D., Calvanese, D., De Giacomo, G., Lenzerini, M., Rosati, R.: Linking data to ontologies. J. Data Semant. 10, 133–173 (2008)

    MATH  Google Scholar 

Download references

Acknowledgments

This work was supported by the projects ASPIQ (ANR-12-BS02-0003), PAGOGA (ANR-12-JS02-007-01) and the ERC Starting Grant 637277.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zied Bouraoui .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Baget, J.F. et al. (2016). Inconsistency-Tolerant Query Answering: Rationality Properties and Computational Complexity Analysis. In: Michael, L., Kakas, A. (eds) Logics in Artificial Intelligence. JELIA 2016. Lecture Notes in Computer Science(), vol 10021. Springer, Cham. https://doi.org/10.1007/978-3-319-48758-8_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-48758-8_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48757-1

  • Online ISBN: 978-3-319-48758-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics