Advertisement

Journal on Data Semantics

, Volume 4, Issue 2, pp 81–101 | Cite as

Preference-Based Query Answering in Probabilistic Datalog+/–  Ontologies

  • Thomas Lukasiewicz
  • Maria Vanina Martinez
  • Gerardo I. Simari
  • Oana Tifrea-Marciuska
Original Article

Abstract

The incorporation of preferences into information systems, such as databases, has recently seen a surge in interest, mainly fueled by the revolution in Web data availability. Modeling the preferences of a user on the Web has also increasingly become appealing to many companies since the explosion of popularity of social media. The other surge in interest is in modeling uncertainty in these domains, since uncertainty can arise due to many uncontrollable factors. In this paper, we propose an extension of the Datalog+/– family of ontology languages with two models: one representing user preferences and one representing the (probabilistic) uncertainty with which inferences are made. Assuming that more probable answers are in general more preferable, one asks how to rank answers to a user’s queries, since the preference model may be in conflict with the preferences induced by the probabilistic model—the need thus arises for preference combination operators. We propose four specific operators and study their semantic and computational properties. We also provide an algorithm for ranking answers based on the iteration of the well-known skyline answers to a query and show that, under certain conditions, it runs in polynomial time in the data complexity. Furthermore, we report on an implementation and experimental results.

Notes

Acknowledgments

This work was supported by the EPSRC grant EP/J008346/1 “PrOQAW: Probabilistic Ontological Query Answering on the Web”, by the European Research Council (FP7/2007–2013/ERC) grant 246858 “DIADEM”, by a Google European Doctoral Fellowship, and by a Yahoo! Research Fellowship. We are grateful to the reviewers of this paper and of its ODBASE-2013 preliminary version [24] for their useful feedback, as well as to Giorgio Orsi for his help with the Datalog+/– query answering engine.

References

  1. 1.
    Atallah MJ, Qi Y (2009) Computing all skyline probabilities for uncertain data. In: Proceedings of PODS. ACM Press, New York, pp 279–287Google Scholar
  2. 2.
    Beeri C, Vardi MY (1987) The implication problem for data dependencies. In: Proceedings of ICALP. Springer, Berlin, pp 73–85Google Scholar
  3. 3.
    Berners-Lee T, Hendler J, Lassila O (2001) The semantic web. Sci Am 284(5):34–43CrossRefGoogle Scholar
  4. 4.
    Börzsönyi S, Kossmann D, Stocker K (2001) The skyline operator. In: Proceedings of ICDE. IEEE Computer Society, Los Alamitos, pp 421–430Google Scholar
  5. 5.
    Calì A, Gottlob G, Kifer M (2008) Taming the infinite chase: query answering under expressive relational constraints. In: Proceedings of KR. AAAI Press, Menlo Park, pp 70–80Google Scholar
  6. 6.
    Calì A, Gottlob G, Lukasiewicz T (2012) A general Datalog-based framework for tractable query answering over ontologies. J Web Sem 14:57–83CrossRefGoogle Scholar
  7. 7.
    Chomicki J (2003) Preference formulas in relational queries. ACM Trans Database Syst 28(4):427–466CrossRefGoogle Scholar
  8. 8.
    Chomicki J (2007) Database querying under changing preferences. Ann Math Artif Intell 50(1/2):79–109CrossRefzbMATHMathSciNetGoogle Scholar
  9. 9.
    Domingos P, Webb WA (2012) A tractable first-order probabilistic logic. In: Proceedings of AAAI. AAAI Press, Menlo Park, pp 1902–1909Google Scholar
  10. 10.
    Finger M, Wassermann R, Cozman FG (2011) Satisfiability in \(\cal EL\) with sets of probabilistic ABoxes. In: Proceedings of DLGoogle Scholar
  11. 11.
    Gaertner W (2009) A primer in social choice theory: revised edition. Oxford University Press, OxfordGoogle Scholar
  12. 12.
    Gottlob G, Lukasiewicz T, Martinez MV, Simari GI (2013) Query answering under probabilistic uncertainty in Datalog+/- ontologies. Ann Math Artif Intell 69(1):37–72CrossRefzbMATHMathSciNetGoogle Scholar
  13. 13.
    Gottlob G, Orsi G, Pieris A (2011) Ontological queries: Rewriting and optimization. In: Proceedings of ICDE. IEEE Computer Society, Washington, DC, pp 2–13Google Scholar
  14. 14.
    Govindarajan K, Jayaraman B, Mantha S (1995) Preference logic programming. In: Proceedings of ICLP. MIT Press, Cambridge, pp 731–745Google Scholar
  15. 15.
    Govindarajan K, Jayaraman B, Mantha S (2001) Preference queries in deductive databases. New Generat Comput 19(1):57–86CrossRefzbMATHGoogle Scholar
  16. 16.
    Hansson SO (1995) Changes in preference. Theory Decis 38:1–28CrossRefzbMATHMathSciNetGoogle Scholar
  17. 17.
    Jung JC, Lutz C (2012) Ontology-based access to probabilistic data with OWL QL. In: Proceedings of ISWC. Springer, Berlin, pp 182–197Google Scholar
  18. 18.
    Kim JH, Pearl J (1983) A computational model for causal and diagnostic reasoning in inference systems. In: Proceedings of IJCAI. William Kaufmann, Karlsruhe, pp 190–193Google Scholar
  19. 19.
    Lacroix M, Lavency P (1987) Preferences: putting more knowledge into queries. In: Proceedings of VLDB. Morgan Kaufmann, Burlington, pp 1–4Google Scholar
  20. 20.
    Lin X, Zhang Y, Zhang W, Cheema MA (2011) Stochastic skyline operator. In: Proceedings of ICDE. IEEE Computer Society, pp 721–732Google Scholar
  21. 21.
    Lukasiewicz T, Martinez MV, Orsi G, Simari GI (2012) Heuristic ranking in tightly coupled probabilistic description logics. In: Proceedings of UAI. AUAI, Edinburgh, pp 554–563Google Scholar
  22. 22.
    Lukasiewicz T, Martinez MV, Simari GI (2012) Consistent answers in probabilistic Datalog+/- ontologies. In: Proceedings of RR. Springer, Berlin, pp 156–171Google Scholar
  23. 23.
    Lukasiewicz T, Martinez MV, Simari GI (2013) Preference-based query answering in Datalog+/- ontologies. In: Proceedings of IJCAI. AAAI Press / IJCAI, Menlo Park, pp 1017–1023Google Scholar
  24. 24.
    Lukasiewicz T, Martinez MV, Simari GI (2013) Preference-based query answering in probabilistic Datalog+/- ontologies. In: Proceedings of ODBASE. Springer, Berlin, pp 501–518Google Scholar
  25. 25.
    Noessner J, Niepert M (2011) ELOG: A probabilistic reasoner for OWL EL. In: Proceedings of RR. Springer, Berlin, pp 281–286 Google Scholar
  26. 26.
    Pei J, Jiang B, Lin X, Yuan Y (2007) Probabilistic skylines on uncertain data. In: Proceedings of VLDB. ACM Press, New York, pp 15–26Google Scholar
  27. 27.
    Pini MS, Rossi F, Venable KB, Walsh T (2009) Aggregating partially ordered preferences. J Log Comput 19(3):475–502CrossRefzbMATHMathSciNetGoogle Scholar
  28. 28.
    Richardson M, Domingos P (2006) Markov logic networks. Mach Learn 62(1/2):107–136CrossRefGoogle Scholar
  29. 29.
    Soliman MA, Ilyas IF, Chen-Chuan Chang K (2007) Top-k query processing in uncertain databases. In: Proceedings of ICDE. IEEE Computer Society, pp 896–905Google Scholar
  30. 30.
    Stefanidis K, Koutrika G, Pitoura E (2011) A survey on representation, composition and application of preferences in database systems. ACM Trans Database Syst 36(3):19:1–19:45Google Scholar
  31. 31.
    Warren HS Jr (1975) A modification of Warshall’s algorithm for the transitive closure of binary relations. Commun ACM 18(4):218–220CrossRefMathSciNetGoogle Scholar
  32. 32.
    Warshall S (1962) A theorem on Boolean matrices. J ACM 9(1):11–12CrossRefzbMATHMathSciNetGoogle Scholar
  33. 33.
    Zhang X (2010) Probabilities and sets in preference querying. Ph.D. thesis, University at Buffalo, State University of New YorkGoogle Scholar
  34. 34.
    Zhang X, Chomicki J (2009) Semantics and evaluation of top-k queries in probabilistic databases. Distrib Parallel Dat 26:67–126CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Thomas Lukasiewicz
    • 1
  • Maria Vanina Martinez
    • 1
  • Gerardo I. Simari
    • 1
  • Oana Tifrea-Marciuska
    • 1
  1. 1.Department of Computer ScienceUniversity of OxfordOxfordUK

Personalised recommendations