Advertisement

Eurofuse 2011 pp 279-292 | Cite as

Multiset Merging: The Majority Rule

  • Antoon Bronselaer
  • Guy De Tré
  • Daan Van Britsom
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 107)

Abstract

A well known problem that many sources of data nowadays cope with, is the problem of duplicate data. In general, we can represent a data source as a collection of objects. Deduplication then consists of two main problems: (a) finding duplicate objects and (b) processing those duplicate objects. This paper contributes to the study of the latter problem by investigating functions that map a multiset of objects to a single object. Such functions are called merge functions.We investigate the specific case where an object itself is a multiset. An interesting application of this case is the problem of multiple document summarization. Next to the basic definition of such merge functions, we focus on an important property borrowed from the (more general) field of information fusion: the majority rule.

Keywords

Majority Rule Information Fusion Strong Rule Local Precision Triangular Norm 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Elmagarmid, A., Ipeirotis, P., Verykios, V.: Duplicate record detection: A survey. IEEE Transactions on Knowledge and Data Engineering 19(1), 1–16 (2007)CrossRefGoogle Scholar
  2. 2.
    Bronselaer, A., De Tré, G.: Aspects of object merging. In: Proceedings of the NAFIPS Conference, Toronto, Canada, pp. 27–32 (2010)Google Scholar
  3. 3.
    Bronselaer, A., De Tré, G.: Properties of possibilistic string comparison. IEEE Transactions on Fuzzy Systems 18(2), 312–325 (2010)CrossRefGoogle Scholar
  4. 4.
    Schweizer, B., Sklar, A.: Probabilistic metric spaces. Elsevier, Amsterdam (1983)zbMATHGoogle Scholar
  5. 5.
    Fellegi, I., Sunter, A.: A theory for record linkage. American Statistical Association Journal 64(328), 1183–1210 (1969)CrossRefGoogle Scholar
  6. 6.
    Lin, J., Mendelzon, A.: Knowledge base merging by majority. In: Dynamic Worlds: From the Frame Problem to Knowledge Management. Kluwer, Dordrecht (1994)Google Scholar
  7. 7.
    Ricardo, B.-Y., Berthier, R.-N.: Modern information retrieval. ACM Press, New York (1999)Google Scholar
  8. 8.
    Yager, R.: On the theory of bags. International Journal of General Systems 13(1), 23–27 (1986)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Konieczny, S., Pérez, R.: Merging information under constraints: a logical framework. Journal of Logic and Computation 12(1), 111–120 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Antoon Bronselaer
    • 1
  • Guy De Tré
    • 1
  • Daan Van Britsom
    • 1
  1. 1.Department of Telecommunications and Information ProcessingGhent UniversityGhentBelgium

Personalised recommendations