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Learning probabilistic relational models (1999)

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by Nir Friedman , Lise Getoor , Daphne Koller , Avi Pfeffer
Venue:In IJCAI
Citations:612 - 30 self
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BibTeX

@INPROCEEDINGS{Friedman99learningprobabilistic,
    author = {Nir Friedman and Lise Getoor and Daphne Koller and Avi Pfeffer},
    title = {Learning probabilistic relational models},
    booktitle = {In IJCAI},
    year = {1999},
    pages = {1300--1309},
    publisher = {Springer-Verlag}
}

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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 significantly 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. 1

Keyphrases

probabilistic relational model    bayesian network    parameter estimation    well-known statistical method    flat form    dependency structure    recent work    commercial relational database system    learning procedure    real-world data    present experimental result    large datasets    statistical learning method    flat quot    standard model    data representation    standard database retrieval technique    automatic induction    related object    relational structure present    synthetic relational database    large portion   

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