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Polynomial Algorithms for Computing a Single Preferred Assertional-Based Repair

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Abstract

This paper investigates different approaches for handling inconsistent DL-Lite knowledge bases in the case where the assertional base is prioritized and inconsistent with the terminological base. The inconsistency problem often happens when the assertions are provided by multiple conflicting sources having different reliability levels. We propose different inference strategies based on the selection of one consistent assertional base, called a preferred repair. For each strategy, a polynomial algorithm for computing the associated single preferred repair is proposed. Selecting a unique repair is important since it allows an efficient handling of queries. We provide experimental studies showing (from a computational point of view) the benefits of selecting one repair when reasoning under inconsistency in lightweight knowledge bases.

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Notes

  1. Positive inclusion axioms are of the form \(B_1\sqsubseteq B_2.\)

  2. Available at: https://code.google.com/p/combo-obda/.

References

  1. Arenas M, Bertossi EL, Chomicki J (1999) Consistent query answers in inconsistent databases. In: Proceedings of the eighteenth ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems, Philadelphia, Pennsylvania, USA, pp 68–79, 1999

  2. Artale A, Calvanese D, Kontchakov R, Zakharyaschev M (2009) The DL-Lite family and relations. J Artif Intell Res (JAIR) 36:1–69

    MathSciNet  MATH  Google Scholar 

  3. Baral C, Kraus S, Minker J, Subrahmanian VS (1992) Combining knowledge bases consisting of first-order analysis. Comput Intell 8:45–71

    Article  Google Scholar 

  4. Benferhat S, Bouraoui Z (2015) Min-based possibilistic DL-Lite. J Logic Comput. doi:10.1093/logcom/exv014

    Google Scholar 

  5. Benferhat S, Bouraoui Z, Papini O, Würbel E (2014) A prioritized assertional-based revision for DL-Lite knowledge bases. In: European conference on logics in artificial intelligence, volume 8761 of LNCS, pp 442–456. Springer, 2014

  6. Benferhat S, Bouraoui Z, Tabia K (2015) How to select one preferred assertional-based repair from inconsistent and prioritized DL-Lite knowledge bases? In: Yang Q, Wooldridge M (eds) Proceedings of the twenty-fourth international joint conference on artificial intelligence IJCAI 2015, Buenos Aires, Argentina, July 25–31, 2015, pp 1450–1456. AAAI Press, 2015

  7. Benferhat S, Cayrol C, Dubois D, Lang J, Prade H (1993) Inconsistency management and prioritized syntax-based entailment. In: International joint conference on artificial intelligence, pp 640–647. Morgan Kaufmann, 1993

  8. Benferhat S, Didier D, Henri P (1997) Some syntactic approaches to the handling of inconsistent knowledge bases: a comparative study part 1: The flat case. Studia Logica 58(1):17–45

    Article  MathSciNet  MATH  Google Scholar 

  9. Benferhat S, Dubois D, Prade H (1992) Representing default rules in possibilistic logic. In: Knowledge representation and reasoning, pp 673–684. Morgan Kaufmann, 1992

  10. Benferhat S, Dubois D, Prade H (1995) How to infer from inconsistent beliefs without revising? In: International joint conference on artificial intelligence, pp 1449–1457. Morgan Kaufmann, 1995

  11. Benferhat S, Dubois D, Prade H (1998) Some syntactic approaches to the handling of inconsistent knowledge bases: a comparative study. Part 2: the prioritized case, volume 24, pp 473–511. Physica-Verlag, Heidelberg, 1998

  12. Bertossi LE (2011) Database repairing and consistent query answering. Synthesis lectures on data management. Morgan & Claypool Publishers, San Rafael

    Google Scholar 

  13. Bienvenu M (2012) On the complexity of consistent query answering in the presence of simple ontologies. In: Proceedings of the twenty-sixth AAAI conference on artificial intelligence, 2012

  14. Bienvenu M, Bourgaux C, Goasdoué F (2014) Querying inconsistent description logic knowledge bases under preferred repair semantics. In: AAAI, pp 996–1002, 2014

  15. Bienvenu M, Rosati R (2013) Tractable approximations of consistent query answering for robust ontology-based data access. In: International joint conference on artificial intelligence. IJCAI/AAAI, 2013

  16. Brewka G (1989) Preferred subtheories: an extended logical framework for default reasoning. In: Sridharan NS (ed) International joint conference on artificial intelligence, pp 1043–1048. Morgan Kaufmann, 1989

  17. Calvanese D, De Giacomo G, Lembo D, Lenzerini M, Rosati R (2007) Tractable reasoning and efficient query answering in description logics: the DL-Lite family. J Autom Reason 39(3):385–429

    Article  MathSciNet  MATH  Google Scholar 

  18. Calvanese D, Kharlamov E, Nutt W, Zheleznyakov D (2010) Evolution of DL-Lite knowledge bases. Int Semant Web Conf 1:112–128

    Google Scholar 

  19. Chomicki J (2007) Consistent query answering: five easy pieces. In: Database theory—ICDT 2007, volume 4353 of lecture notes in computer science, pp 1–17. Springer, 2007

  20. Didier D, Lang J, Henri P (1994) Possibilistic logic. In: Volume 3 of handbook on logic in artificial intelligence and logic programming, pp 439–513. Oxford University press, 1994

  21. Du J, Qi G, Shen Y (2013) Weight-based consistent query answering over inconsistent SHIQ knowledge bases. Knowl Inform Syst 34(2):335–371

    Article  Google Scholar 

  22. Dubois D, Prade H (1988) Possibility theory. Plenum Press, New York

    Book  MATH  Google Scholar 

  23. Dubois D, Prade H (1991) Epistemic entrenchment and possibilistic logic. Artif Intell 50(2):223–239

    Article  MathSciNet  MATH  Google Scholar 

  24. Lembo D, Lenzerini M, Rosati R, Ruzzi M, Fabio Savo D (2010) Inconsistency-tolerant semantics for description logics. In: Hitzler P, Lukasiewicz T (eds) RR, volume 6333 of LNCS, pp 103–117. Springer, 2010

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

    Article  Google Scholar 

  26. Lenzerini M (2012) Ontology-based data management. In: Proceedings of the 6th Alberto Mendelzon international workshop on foundations of data management 2012, pp 12–15, 2012

  27. Lukasiewicz T, Vanina Martinez M, Pieris A, Simari GI (2015) From classical to consistent query answering under existential rules. In: Proceedings of the twenty-ninth AAAI conference on artificial intelligence 2015, pp 1546–1552, 2015

  28. Lukasiewicz T, Vanina Martinez M, Simari GI (2012) Inconsistency handling in datalog+/\(-\) ontologies. In: 20th European conference on artificial intelligence ECAI, 2012, pp 558–563, 2012

  29. Lutz C, Seylan I, Toman D, Wolter F (2013) The combined approach to OBDA: taming role hierarchies using filters. In: The semantic web—ISWC, volume 8218 of lecture notes in computer science, pp 314–330. Springer, 2013

  30. Martinez MV, Parisi F, Pugliese A, Simari GI, Subrahmanian VS (2008)Inconsistency management policies. In: Knowledge representation and reasoning, pp 367–377. AAAI Press, 2008

  31. Nebel B (1994) Base revision operations and schemes: semantics, representation and complexity. In: European conference on artificial intelligence, pp 341–345, 1994

  32. Nicholas R, Ruth M (1970) On inference from inconsistent premisses. Theory Decis 1(2):179–217

    Article  Google Scholar 

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

    MATH  Google Scholar 

  34. Qi G, Ji Q, Pan JZ, Du J (2011) Extending description logics with uncertainty reasoning in possibilistic logic. Int J Intell Syst 26(4):353–381

    Article  MATH  Google Scholar 

  35. Staworko S, Chomicki J, Marcinkowski J (2012) Prioritized repairing and consistent query answering in relational databases. Ann Math Artif Intell 64(2–3):209–246

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

The authors would like to thank referees for their useful remarks and comments. This work has received support from the European project H2020 Marie Sklodowska-Curie Actions (MSCA) research and Innovation Staff Exchange (RISE): AniAge (High Dimensional Heterogeneous Data based Animation Techniques for Southeast Asian Intangible Cultural Heritage Digital Content), Project Number 691215.

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Authors and Affiliations

  1. Artois University-Nord de France, CRIL CNRS UMR 8188, Artois, 62307, Lens, France

    Abdelmoutia Telli, Salem Benferhat & Karim Tabia

  2. Cardiff University, Cardiff, UK

    Zied Bouraoui

  3. Mohamed Khider University of Biskra, BP 145 RP, 07000, Biskra, Algeria

    Abdelmoutia Telli

  4. University of M’sila, BP 166 RP, 28000, M’sila, Algeria

    Mustapha Bourahla

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  1. Abdelmoutia Telli
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  2. Salem Benferhat
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  4. Zied Bouraoui
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  5. Karim Tabia
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Correspondence to Salem Benferhat.

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Telli, A., Benferhat, S., Bourahla, M. et al. Polynomial Algorithms for Computing a Single Preferred Assertional-Based Repair. Künstl Intell 31, 15–30 (2017). https://doi.org/10.1007/s13218-016-0466-4

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