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Daphne Koller Publications

Learning probabilistic relational models (1999)

by N. Friedman, L. Getoor, D. Koller, and A. Pfeffer


Abstract: A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with ``flat'' data representations. Thus, to apply these methods, we are forced to convert our data into a flat form, thereby losing much of the relational structure present in our database. This paper builds on the recent work on probabilistic relational models (PRMs), and describes how to learn them from databases. PRMs allow the properties of an object to depend probabilistically both on other properties of that object and on properties of related objects. Although PRMs are more expressive than standard models, such as Bayesian networks, we show how to extend well-known statistical methods for learning Bayesian networks to learn these models. We describe both parameter estimation and structure learning - the automatic induction of the dependency structure in a model. Moreover, we show how the learning procedure can exploit standard database retrieval techniques for efficient learning from large datasets. We present experimental results on both real and synthetic relational databases.


Download Information

N. Friedman, L. Getoor, D. Koller, and A. Pfeffer (1999). "Learning probabilistic relational models." Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI-99) (pp. 1300-1309). pdf ps.gz

Bibtex citation

@inproceedings{Friedman+al:IJCAI99,
  author =       "N. Friedman and L. Getoor and D. Koller and A. Pfeffer",
  booktitle =    "Proceedings of the Sixteenth International Joint
                 Conference on Artificial Intelligence ({IJCAI}-99)",
  title =        "Learning probabilistic relational models",
  publisher =    "Morgan Kaufman",
  address =      "Stockholm, Sweden",
  pages =        "1300--1309",
  year =         "1999",
}

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