Declarative Belief Set Merging Using Merging Plans

  • Christoph Redl
  • Thomas Eiter
  • Thomas Krennwallner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6539)


We present a declarative framework for belief set merging tasks over (possibly heterogeneous) knowledge bases, where belief sets are sets of literals. The framework is designed generically for flexible deployment to a range of applications, and allows to specify complex merging tasks in tree-structured merging plans, whose leaves are the possible belief sets of the knowledge bases that are processed using merging operators. A prototype is implemented in MELD (MErging Library for Dlvhex) on top of the dlvhex system for hex-programs, which are nonmonotonic logic programs with access to external sources. Plans in the task description language allow to formulate different conflict resolution strategies, and by shared object libraries, the user may also develop and integrate her own merging operators. MELD supports rapid prototyping of merging tasks, providing a computational backbone such that users can focus on operator optimization and evaluation, and on experimenting with merging strategies; this is particularly useful if a best merging operator or strategy is not known. Example applications are combining multiple decision diagrams (e.g., in biomedicine), judgment aggregation in social choice theory, and ontology merging.


Logic Program Belief Base Full Adder Social Choice Theory Judgment Aggregation 
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.
    Bahar, R.I., Frohm, E.A., Gaona, C.M., Hachtel, G.D., Macii, E., Pardo, A., Somenzi, F.: Algebraic decision diagrams and their applications. In: ICCAD 1993, pp. 188–191 (1993)Google Scholar
  2. 2.
    Dalal, M.: Investigations into a theory of knowledge base revision. In: AAAI, pp. 475–479 (1988)Google Scholar
  3. 3.
    Dasgupta, P.S., Hammond, P.J., Maskin, E.S.: The implementation of social choice rules: Some general results on incentive compatibility. Rev. Econ. Stud. 46(2), 185–216 (1979)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Delgrande, J.P., Liu, D.H., Schaub, T., Thiele, S.: COBA 2.0: A Consistency-Based Belief Change System. In: Mellouli, K. (ed.) ECSQARU 2007. LNCS (LNAI), vol. 4724, pp. 78–90. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Eiter, T., Ianni, G., Schindlauer, R., Tompits, H.: A uniform integration of higher-order reasoning and external evaluations in answer-set programming. In: IJCAI 2005, pp. 90–96 (2005)Google Scholar
  6. 6.
    Eiter, T., Ianni, G., Schindlauer, R., Tompits, H.: dlvhex: A system for integrating multiple semantics in an answer-set programming framework. In: WLP 2006, pp. 206–210 (2006)Google Scholar
  7. 7.
    Gelfond, M., Lifschitz, V.: Classical negation in logic programs and disjunctive databases. New Generat. Comput. 9(3-4), 365–385 (1991)CrossRefzbMATHGoogle Scholar
  8. 8.
    Hué, J., Papini, O., Würbel, E.: Merging belief bases represented by logic programs. In: Sossai, C., Chemello, G. (eds.) ECSQARU 2009. LNCS, vol. 5590, pp. 371–382. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
    Konieczny, S., Lang, J., Marquis, P.: DA2 merging operators. AIJ 157(1-2), 49–79 (2004)zbMATHGoogle Scholar
  10. 10.
    Konieczny, S., Pérez, R.P.: On the logic of merging. In: KR 1998, pp. 488–498 (1998)Google Scholar
  11. 11.
    Liberatore, P., Schaerf, M.: Arbitration (or how to merge knowledge bases). IEEE Trans. Knowl. Data Eng. 10(1), 76–90 (1998)CrossRefGoogle Scholar
  12. 12.
    Liew, A.W.C., Wu, Y., Yan, H.: Selection of statistical features based on mutual information for classification of human coding and non-coding DNA sequences. In: ICPR, pp. 766–769 (2004)Google Scholar
  13. 13.
    Lin, J., Mendelzon, A.: Knowledge base merging by majority. In: Dynamic Worlds: From the Frame problem to Knowledge Management. Kluwer, Dordrecht (1999)Google Scholar
  14. 14.
    Redl, C.: Development of a belief merging framewerk for dlvhex. Master’s thesis, Vienna University of Technology, A-1040 Vienna, Karlsplatz 13 (June 2010),
  15. 15.
    Redl, C.: Merging of biomedical decision diagrams. Master’s thesis, Vienna University of Technology, A-1040 Vienna, Karlsplatz 13 (October 2010),
  16. 16.
    Revesz, P.: On the semantics of arbitration. Intl. J. Algebra Comput. 7(2), 133–160 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Salzberg, S.: Locating protein coding regions in human DNA using a decision tree algorithm. J. Comput. Biol. 2, 473–485 (1995)CrossRefGoogle Scholar
  18. 18.
    Salzberg, S., Delcher, A.L., Fasman, K.H., Henderson, J.: A decision tree system for finding genes in DNA. J. Comput. Biol. 5(4), 667–680 (1998)CrossRefGoogle Scholar
  19. 19.
    Sobin, L., Gospodarowicz, M., Wittekind, C.: TNM Classification of Malignant Tumours, 7th edn. Wiley, Chichester (2009)Google Scholar
  20. 20.
    Sree, P.K., Babu, I.R., Murty, J.V.R., Rao, P.S.: Towards an artificial immune system to identify and strengthen protein coding region identification using cellular automata classifier. Intl. J. Comput. Commun. 1(2), 26–34 (2007)Google Scholar
  21. 21.
    Sree, P.K., Babu, I.R.: Identification of protein coding regions in genomic DNA using unsupervised FMACA based pattern classifier. Intl. J. Comp. Sci. Netw. Secur. 8(1), 305–309 (2008)Google Scholar
  22. 22.
    Stumme, G., Maedche, A.: FCA-MERGE: Bottom-Up Merging of Ontologies. In: IJCAI 2001, pp. 225–230 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Christoph Redl
    • 1
  • Thomas Eiter
    • 1
  • Thomas Krennwallner
    • 1
  1. 1.Institut für InformationssystemeTechnische Universität WienViennaAustria

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