Learning probabilistic relational models

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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:	613 - 30 self

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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