@article {14357,
	title = {Learning probabilistic relational models},
	journal = {Abstraction, Reformulation, and Approximation},
	year = {2000},
	month = {2000///},
	pages = {322 - 323},
	abstract = {My work is on learning Probabilistic Relational Models (PRMs) from structured data (e.g., data in a relational database, an object-oriented database or a frame-based system). This work has as a starting point the framework of Probabilistic Relational Models, introduced in [5, 7]. We adapt and extend the machinery that has been developed over the years for learning Bayesian networks from data [1, 4, 6] to the task of learning PRMs from structured data. At the heart of this work is a search algorithm that explores the space of legal models using search operators that abstract or refine the model.},
	doi = {10.1007/3-540-44914-0_25},
	author = {Getoor, Lise}
}
