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.
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References
Learning Statistical Models from Relational Data. URL: http://robotics.stanford.edu/srl/ (2000)
Elmasri, R., Navathe, S.B.: Fundamantals of Databases Systems. 3rd edn. Addison Wesley, Reading, Massachusetts (2000)
Friedman, N., Getoor, L., Koller, D., Pfeffer, A.: Learning Probabilistic Relational Models. In: Proc. IJCAI-99, Stockholm. Morgan Kaufmann (1999) 1300–1307
Getoor, L., Taskar, B., Koller, D.: Selectivity Estimation Using Probabilistic Models. In: Proc. ACM SIGMOD, Santa Barbara, California (2001) 21–24
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) 399–424
Koller, D., Pfeffer, A.: Object-Oriented Bayesian Networks. In: Proc. UAI-97, Providence, Rhode Island (1997) 302–313
Mannila, H.: Foreword to the Book. In: Dzeroski, S., Lavra, N. (eds.): Relational Data Mining. Springer, Berlin (2001)
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) 902–912
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) 64–84
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Chen, J., Shapcott, M., McClean, S., Adamson, K. (2002). Learning with Concept Hierarchies in Probabilistic Relational Data Mining. In: Meng, X., Su, J., Wang, Y. (eds) Advances in Web-Age Information Management. WAIM 2002. Lecture Notes in Computer Science, vol 2419. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45703-8_10
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DOI: https://doi.org/10.1007/3-540-45703-8_10
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