@article {14507,
	title = {Method and apparatus for learning probabilistic relational models having attribute and link uncertainty and for performing selectivity estimation using probabilistic relational models},
	volume = {09/922,324},
	year = {2002},
	month = {2002/08/01/},
	abstract = {The invention comprises a method and apparatus for learning probabilistic models (PRM{\textquoteright}s) with attribute uncertainty. A PRM with attribute uncertainty defines a probability distribution over instantiations of a database. A learned PRM is useful for discovering interesting patterns and dependencies in the data. Unlike many existing techniques, the process is data-driven rather than hypothesis driven. This makes the technique particularly well-suited for exploratory data analysis. In addition, the invention comprises a method and apparatus for handling link uncertainty in PRM{\textquoteright}s. Link uncertainty is uncertainty over which entities are related in our domain. The invention comprises of two mechanisms for modeling link uncertainty: reference uncertainty and existence uncertainty. The invention includes learning algorithms for each form of link uncertainty. The third component of the invention is a technique for performing database selectivity estimation using probabilistic relational...},
	url = {http://www.google.com/patents?id=FVqDAAAAEBAJ},
	author = {Koller,Daphne and Getoor, Lise and Pfeffer,Avi and Friedman,Nir and Taskar,Ben}
}
