Semantic query optimization through abduction and constraint handling

  • Gerhard Wetzel
  • Francesca Toni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1495)


The use of integrity constraints to perform Semantic Query-Optimization (SQO) in deductive databases can be formalized in a way similar to the use of integrity constraints in Abductive Logic Programming (ALP) and the use of Constraint Handling Rules in Constraint Logic Programming (CLP). Based on this observation and on the similar role played by, respectively, extensional, abducible and constraint predicates in SQO, ALP and CLP, we present a unified framework from which (variants of) SQO, ALP and CLP can be obtained as special instances. The framework relies on a proof procedure which combines backward reasoning with logic programming clauses and forward reasoning with integrity constraints.


Logic Program Unify Framework Integrity Constraint Deductive Database Constraint Logic Programming 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Abdennadher, S.; Schütz, H.: CHRv: A Flexible Query Language. In this volume.Google Scholar
  2. 2.
    Bressan, S.; Goh, C. H.: Answering Queries in Context. In this volume.Google Scholar
  3. 3.
    Chakravarthy, U. S.; Grant, J.; Minker, J.: Foundations of Semantic Query Optimization for Deductive Databases. In: Minker, J. (ed.): Foundations of Deductive Databases and Logic Programming, pp. 243–273, Morgan Kaufmann 1988Google Scholar
  4. 4.
    Chakravarthy, U. S.; Grant, J.; Minker, J.: Logic-Based Approach to Semantic Query Optimization, ACM Transactions on Database Systems 15 (2), pp. 162–207, 1990CrossRefGoogle Scholar
  5. 5.
    Clark, K. L.: Negation as failure. In: Gallaire, H.; Minker, J. (eds.): Logic and Data Bases, pp. 292–322, Plenum Press 1978Google Scholar
  6. 6.
    Denecker, M.; De Schreye, D.: SLDNFA: an abductive procedure for abductive logic programs, Journal of Logic Programming 34 (2), pp. 111–167, 1997CrossRefGoogle Scholar
  7. 7.
    Frühwirth, T.: Constraint Handling Rules. In: Podelski, A. (ed.): Constraint Programming: Basic and Trends, pp. 90–107, LNCS 910, Springer Verlag 1995Google Scholar
  8. 8.
    Fung, T. H.: Abduction by Deduction. Ph.D. Thesis, Imperial College 1996Google Scholar
  9. 9.
    Fung, T. H.; Kowalski, R. A.: The Iff Proof Procedure for Abductive Logic Programs, Journal of Logic Programming 33 (2), pp. 151–165, 1997zbMATHMathSciNetCrossRefGoogle Scholar
  10. 10.
    Gaasterland, T.; Lobo, J.: Processing Negation and Disjunction in Logic Programs Through Integrity Constraints, Journal of Intelligent Information Systems 2, pp. 225–243, 1993CrossRefGoogle Scholar
  11. 11.
    Godfrey, P.; Grant, J.; Gryz, J.; Minker, J.: Integrity Constraints: Semantics and Applications. To appear in: Chomicki, J.; Saake, G.: Logics for Databases and Information Systems, Kluwer 1998Google Scholar
  12. 12.
    Janson, S.; Haridi, S.: Programming Paradigms of the Andorra kernel language, Saraswat, V.; Ueda, K. (eds.): Proc. of the Int. Symp. on Logic Programming, pp. 167–186, MIT Press 1991Google Scholar
  13. 13.
    Jaffar, J.; Lassez, J.-L.: Constraint Logic Programming, Proc. of the 14 th ACM Symp. on the Principles of Programming Languages, pp. 111–119, 1987Google Scholar
  14. 14.
    Jaffar, J.; Maher, M.: Constraint Logic Programming: A Survey, Journal of Logic Programming 19/20, pp. 503–581, 1994MathSciNetCrossRefGoogle Scholar
  15. 15.
    Jourdan, J.; Sola, T.: The Versatility of Handling Disjunctions as Constraints. In: Bruynooghe, M.; Penjam, J. (eds.): Proc. of the 5 th Intern. Symp. on Programming Languages Implementation and Logic Programming, pp. 60–74, Springer Verlag 1993Google Scholar
  16. 16.
    Kakas, A. C.: Deductive Databases as Theories of Belief, Technical Report, Imperial College, 1991Google Scholar
  17. 17.
    Kakas, A. C.: On the Evolution of Deductive Databases, Technical Report, Imperial College, 1991Google Scholar
  18. 18.
    Kakas, A. C.; Kowalski, R. A.; Toni, F.: Abductive Logic Programming, Journal of Logic and Computation 2 (6), pp. 719–770, 1992zbMATHMathSciNetGoogle Scholar
  19. 19.
    Kakas, A. C.; Kowalski, R. A.; Toni, F.: The role of abduction in logic programming. To appear in: Gabbay, D. M. et al. (eds.): Handbook of logic in Artificial Intelligence and Logic Programming, vol. 5, pp. 235–324, Oxford University Press 1998Google Scholar
  20. 20.
    Kakas, A. C.; Michael, A.: Integrating Abductive and Constraint Logic Programming. In: Sterling, L. (ed.): Proc. of the 12 th Int. Conf. on Logic Programming, pp. 399–413, MIT Press 1995Google Scholar
  21. 21.
    Kowalski, R. A.; Sadri, F.: Logic Programs with Exceptions. In: Warren, D. H. D.; Szeredi, P. (eds.): Proc. of the 7 th Int. Conf. on Logic Programming, pp. 598–613, MIT Press 1990Google Scholar
  22. 22.
    Kowalski, R. A.; Toni, F.; Wetzel, G.: Executing Suspended Logic Programs, to appear in a special issue of Fundamenta Informaticae ed. by K. Apt.Google Scholar
  23. 23.
    Lakshmanan, L. V. S.; Missaoui, R.: Pushing Semantics into Recursion: A General Framework for Semantic Optimization of Recursive Queries. In: Proc. of the Intern. Conf. on Data Engineering, Taiwan, 1995Google Scholar
  24. 24.
    Manthey, R.; Bry, F.: SATCHMO: A Theorem Prover Implemented in PROLOG. In: Lusk, E.; Overbeek, R. (eds.): Proc. of the 9 th Conf. on Automated Deduction, pp. 415–434, LNCS 310, Springer-Verlag 1988Google Scholar
  25. 25.
    Martelli, A., Montanari, U.: An efficient unification algorithm, ACM Trans. on Prog. Lang, and Systems 4 (2), pp. 258–282, 1982zbMATHCrossRefGoogle Scholar
  26. 26.
    McDermott, J.: R1: A Rule-Based Configurer of Computer Systems, Artificial Intelligence 19 (1), pp. 39–88, 1982CrossRefGoogle Scholar
  27. 27.
    Maim, E.: Abduction and Constraint Logic Programming, In: Neumann, B. (ed.): Proc. of the 10 th European Conf. on Artificial Intelligence, 1992Google Scholar
  28. 28.
    Pirotte, A.; Roelants, D.; Zimányi, E.: Controled generation of intensional answers, IEEE Trans. on Knowledge and Data Engineering, 3 (2), pp. 221–236, 1991CrossRefGoogle Scholar
  29. 29.
    Ross, K. A.; Srivastava, D.; Stuckey, P. J.; Sudarshan, S.: Foundations of Aggregation Constraints, Theoretical Computer Science 193 (1–2), pp. 149–179, 1998zbMATHMathSciNetCrossRefGoogle Scholar
  30. 30.
    Van Hentenryck, P.; Saraswat, V. A.; Deville, Y.: Design, Implementation, and Evaluation of the Constraint Language cc(FD). In: Podelski, A. (ed.): Constraint Programming: Basic and Trends, pp. 293–316, Springer Verlag 1995Google Scholar
  31. 31.
    Wetzel, G.: Abductive and Constraint Logic Programming, Ph.D. thesis, Imperial College 1997Google Scholar
  32. 32.
    Wetzel, G.: A Unifying Framework for Abductive and Constraint Logic Programming. In: Bry, F.; Freitag, B.; Seipel, D. (eds.): 12 th Workshop on Logic Programming (WLP'97), pp. 58–68, LMU München 1997Google Scholar
  33. 33.
    Wetzel, G.: Using Integrity Constraints as Deletion Rules. In A. Bonner et al. (eds): Proceedings of the DYNAMICS'97 post-conference (ILPS'97) workshop on (Trans)Actions and Change in Logic Programming and Deductive Databases Google Scholar
  34. 34.
    Wetzel, G.; Kowalski, R. A.; Toni, F.: PROCALOG — Programming with Constraints and Abducibles in Logic. In: Maher, M. (ed.): Proc. of the 1996 Joint Int. Conf. and Symp. on Logic Programming, p. 535Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Gerhard Wetzel
    • 1
  • Francesca Toni
    • 2
  1. 1.Logic Based Systems Lab, Department of Computer ScienceBrooklyn CollegeUSA
  2. 2.Department of ComputingImperial CollegeLondonUK

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