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Learning with Concept Hierarchies in Probabilistic Relational Data Mining

  • Jianzhong Chen
  • Mary Shapcott
  • Sally McClean
  • Kenny Adamson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2419)

Abstract

Probabilistic relational models (PRMs) extend Bayesian networks to multi-relational domains and represent the dependencies between attributes within a table and across multiple tables. This paper presents a method of integrating and learning with concept hierarchies with PRMs, in order to retrieve richer object and relational information from multi-relational databases. A concept hierarchy defines a partially ordered sequence of mappings from a set of concepts to their higher-level correspondences. Natural concept hierarchies are often associated with some attributes in databases and can be used to discover knowledge. We first introduce concept hierarchies to PRMs by using background knowledge. A score-based search algorithm is then investigated for learning with concept hierarchies in PRMs parameter estimation procedure. The method can learn the most appropriate concepts from the data and use them to update the parameters. Experimental results on both real and synthetic data are discussed.

Keywords

Minimum Description Length Concept Hierarchy Statistical Relational Learning Probabilistic Relational Model Aggregate Rule 
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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References

  1. 1.
    Learning Statistical Models from Relational Data. URL: http://robotics.stanford.edu/srl/ (2000)
  2. 2.
    Elmasri, R., Navathe, S.B.: Fundamantals of Databases Systems. 3rd edn. Addison Wesley, Reading, Massachusetts (2000)Google Scholar
  3. 3.
    Friedman, N., Getoor, L., Koller, D., Pfeffer, A.: Learning Probabilistic Relational Models. In: Proc. IJCAI-99, Stockholm. Morgan Kaufmann (1999) 1300–1307Google Scholar
  4. 4.
    Getoor, L., Taskar, B., Koller, D.: Selectivity Estimation Using Probabilistic Models. In: Proc. ACM SIGMOD, Santa Barbara, California (2001) 21–24Google Scholar
  5. 5.
    Han, J., Fu, Y.: Attribute-Oriented Induction in Data Mining. In: Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy R. (eds.): Advances in Kowledge Discovery and Data Mining, Chapter 16. AAAI Press / The MIT Press, Massachusetts (1996) 399–424Google Scholar
  6. 6.
    Koller, D., Pfeffer, A.: Object-Oriented Bayesian Networks. In: Proc. UAI-97, Providence, Rhode Island (1997) 302–313Google Scholar
  7. 7.
    Mannila, H.: Foreword to the Book. In: Dzeroski, S., Lavra, N. (eds.): Relational Data Mining. Springer, Berlin (2001)Google Scholar
  8. 8.
    McClean, S., Scotney, B., Shapcott, M.: Aggregation of Imprecise and Uncertain Information in Databases. IEEE Transactions on Knowledge and Data Engineering. Vol. 13(6) (2001) 902–912CrossRefGoogle Scholar
  9. 9.
    Shapcott, M., McClean, S., Scotney, B.: Using Background Knowledge with Attribute-Oriented Data Mining. In: Bramer, M.A. (eds.): Kowledge Discovery and Data Mining, IEE Professional Applications of Computing Series 1, Chapter 4. London (1999) 64–84Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Jianzhong Chen
    • 1
  • Mary Shapcott
    • 1
  • Sally McClean
    • 1
  • Kenny Adamson
    • 1
  1. 1.School of Information and Software Engineering, Faculty of InformaticsUniversity of Ulster at JordanstownNorthern IrelandUK

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