Advertisement

How does incoherence affect inconsistency-tolerant semantics for Datalog±?

  • Cristhian A. D. Deagustini
  • M. Vanina Martinez
  • Marcelo A. Falappa
  • Guillermo R. Simari
Article
  • 62 Downloads

Abstract

The concept of incoherence naturally arises in ontological settings, specially when integrating knowledge. In the Datalog± literature, however, this is an issue that is yet to be studied more deeply. The main focus of our work is to show how classical inconsistency-tolerant semantics for query answering behaves when dealing with atoms that are relevant to unsatisfiable sets of existential rules, which may hamper the quality of answers and any reasoning task based on those semantics. We also propose a notion of incoherency-tolerant semantics for query answering in Datalog±, and exemplify this notion with a particular semantics based on the transformation of classic Datalog± ontologies into defeasible Datalog± ones, which use argumentation as its reasoning machinery.

Keywords

Incoherence Inconsistency-tolerant semantics Argumentation Datalog± ontologies 

Mathematics Subject Classification (2010)

68T27 68T30 68T35 68T37 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Arenas, M., Bertossi, L.E., Chomicki, J.: Consistent query answers in inconsistent databases. In: Proceedings of PODS, pp 68–79 (1999)Google Scholar
  2. 2.
    Bell, D.A., Qi, G., Liu, W.: Approaches to inconsistency handling in description-logic based ontologies. In: OTM Workshops, vol. 2, pp 1303–1311 (2007)Google Scholar
  3. 3.
    Besnard, P., Hunter, A.: Elements of argumentation. MIT Press (2008)Google Scholar
  4. 4.
    Bienvenu, M.: On the complexity of consistent query answering in the presence of simple ontologies. In: Proceedings of AAAI (2012)Google Scholar
  5. 5.
    Bienvenu, M., Rosati, R.: Tractable approximations of consistent query answering for robust ontology-based data access. In: Proceedings of IJCAI (2013)Google Scholar
  6. 6.
    Black, E., Hunter, A., Pan, J.Z.: An argument-based approach to using multiple ontologies. In: SUM, pp 68–79 (2009)Google Scholar
  7. 7.
    Briguez, C.E., Budȧn, M.C., Deagustini, C.A.D., Maguitman, A.G., Capobianco, M., Simari, G.R.: Argument-based mixed recommenders and their application to movie suggestion. Expert Syst. Appl. 41(14), 6467–6482 (2014)CrossRefGoogle Scholar
  8. 8.
    Calì, A., Gottlob, G., Lukasiewicz, T.: A general Datalog-based framework for tractable query answering over ontologies, vol. 14, pp 57–83 (2012a)Google Scholar
  9. 9.
    Calì, A., Gottlob, G., Lukasiewicz, T.: A general Datalog-based framework for tractable query answering over ontologies. J. Web Semant. 14, 57–83 (2012b)CrossRefGoogle Scholar
  10. 10.
    Calì, A., Lembo, D., Rosati, R.: On the decidability and complexity of query answering over inconsistent and incomplete databases. In: Proceedings of PODS 2003, pp 260–271. ACM (2003)Google Scholar
  11. 11.
    Cecchi, L., Fillottrani, P., Simari, G. R.: On the Complexity of Delp through Game Semantics. In: Dix, J., Hunter, A. (eds.) Proceedings 11Th Intl. Workshop on Nonmonotonic Reasoning (NMR 2006, pp 386–394 (2006)Google Scholar
  12. 12.
    Chomicki, J.: Consistent query answering: five easy pieces. In: Proceedings of ICDT, pp 1–17 (2007)Google Scholar
  13. 13.
    Croitoru, M., Vesic, S.: What can argumentation do for inconsistent ontology query answering?. In: Scalable uncertainty management, pp 15–29. Springer (2013)Google Scholar
  14. 14.
    Deagustini, C.A.D., Dalibón, S.E.F., Gottifredi, S., Falappa, M.A., Chesñevar, C.I., Simari, G.R.: Relational databases as a massive information source for defeasible argumentation. Knowl.-Based Syst. 51, 93–109 (2013)CrossRefGoogle Scholar
  15. 15.
    Deagustini, C.A.D., Martinez, M.V., Falappa, M.A., Simari, G.R.: Datalog ± ontology consolidation. J. Artif. Intell. Research (JAIR) (2016). To appearGoogle Scholar
  16. 16.
    Delgrande, J.P., Jin, Y.: Parallel belief revision: Revising by sets of formulas. Artif. Intell. 176(1), 2223–2245 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Delgrande, J.P., Schaub, T., Tompits, H.: Stefanwoltran merging logic programs under answer set semantics. In: Hill, P., Warren, D. (eds.) ICLP. Vol. 5649 of lecture notes in computer science, pp 160–174. Springer (2009)Google Scholar
  18. 18.
    Dung, P.M.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artif. Intell. 77, 321–357 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Dunne, P., Wooldridge, M.: Argumentation in artificial intelligence, pp 85–104. Springer, Ch. Complexity of Abstract Argumentation (2009)Google Scholar
  20. 20.
    Dvoṙák, W., Woltran, S.: Complexity of semi-stable and stage semantics in argumentation frameworks. Inf. Process. Lett. 110(11), 425–430 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Flouris, G., Huang, Z., Pan, J.Z., Plexousakis, D., Wache, H.: Inconsistencies, negations and changes in ontologies. In: AAAI, pp 1295–1300. AAAI Press (2006)Google Scholar
  22. 22.
    García, A.J., Simari, G.R.: Defeasible logic programming: an argumentative approach. TPLP 4(1–2), 95–138 (2004)MathSciNetzbMATHGoogle Scholar
  23. 23.
    Huang, Z., van Harmelen, F., ten Teije, A.: Reasoning with inconsistent ontologies. In: Proceedings of IJCAI, pp 354–359 (2005)Google Scholar
  24. 24.
    Konieczny, S., Pérez, R.P.: Merging information under constraints: a logical framework. J. Log. Comput. 12(5), 773–808 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Lembo, D., Lenzerini, M., Rosati, R., Ruzzi, M., Savo, D.F.. In: Proceedings of RR, pp 103–117 (2010)Google Scholar
  26. 26.
    Lukasiewicz, T., Martinez, M.V., Simari, G.I.: Inconsistency handling in Datalog+/– ontologies. In: Proceedings of ECAI, pp 558–563 (2012)Google Scholar
  27. 27.
    Ma, Y., Hitzler, P.: Paraconsistent reasoning for OWL 2. In: Proceedings of RR. Vol. 5837 of LNCS, pp 197–211. Springer (2009)Google Scholar
  28. 28.
    Martinez, M.V., Deagustini, C.A.D., Falappa, M.A., Simari, G.R.: Inconsistency-tolerant reasoning in Datalog ± Ontologies via an argumentative semantics. In: Proceedings of IBERAMIA 2014, pp 15–27 (2014)Google Scholar
  29. 29.
    Martinez, M.V., García, A.J., Simari, G.R.: On the use of presumptions in structured defeasible reasoning. In: Proceedings of COMMA, pp 185–196 (2012)Google Scholar
  30. 30.
    Prakken, H., Sartor, G.: Argument-based extended logic programming with defeasible priorities. J. Appl. Non-Classical Logics 7(1) (1997)Google Scholar
  31. 31.
    Qi, G., Du, J.: Model-based revision operators for terminologies in description logics. In: Proceedings of IJCAI, pp 891–897 (2009)Google Scholar
  32. 32.
    Qi, G., Hunter, A.: Measuring incoherence in description logic-based ontologies. In: ISWC/ASWC, pp 381–394 (2007)Google Scholar
  33. 33.
    Rahwan, I., Simari, G.R.: Argumentation in artificial intelligence. Springer (2009)Google Scholar
  34. 34.
    Reiter, R.: A logic for default reasoning. Artif. Intel. 13(1–2), 81–132 (1980)MathSciNetCrossRefzbMATHGoogle Scholar
  35. 35.
    Rosati, R.: On the complexity of dealing with inconsistency in description logic ontologies. In: Proceedings of IJCAI, pp 1057–1062 (2011)Google Scholar
  36. 36.
    Schlobach, S., Cornet, R.: Non-standard reasoning services for the debugging of description logic terminologies. In: Proceedings of IJCAI 2003, pp 355–362 (2003)Google Scholar
  37. 37.
    Simari, G.R., Loui, R.P.: A mathematical treatment of defeasible reasoning and its implementation. Artif. Intell. 53(2–3), 125–157 (1992)MathSciNetCrossRefzbMATHGoogle Scholar
  38. 38.
    Thimm, M.: Realizing argumentation in multi-agent systems using defeasible logic programming. In: Argumentation in multi-agent systems, pp 175–194. Springer (2010)Google Scholar
  39. 39.
    Wooldridge, M., Dunne, P.E., Parsons, S.: On the complexity of linking deductive and abstract argument systems. In: AAAI, vol. 6, pp 299–304 (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Cristhian A. D. Deagustini
    • 1
    • 2
  • M. Vanina Martinez
    • 1
  • Marcelo A. Falappa
    • 1
    • 2
  • Guillermo R. Simari
    • 1
  1. 1.AI R&D Lab., Institute for Computer Science and Engineering (ICIC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)Universidad Nacional del SurBahía BlancaArgentina
  2. 2.Agents and Intelligent Systems Area, Fac. of Management SciencesUniversidad Nacional de Entre RíosEntre RíosArgentina

Personalised recommendations