2000 LearningProbRelModels

• (Getoor, 2000) ⇒ Lise Getoor. (2000). “Learning Probabilistic Relational Models.” In: 4th International Symposium on Abstraction, Reformulation, and Approximation (SARA 2000). doi:10.1007/3-540-44914-0.

Subject Headings: Statistical Relational Learning, Probabilistic Relational Model(PRM).

Quotes

• 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.
• A PRM describes a template for a probability distribution over a database. The template includes a relational component, that describes the relational schema for the domain, and a probabilistic component, that describes the probabilistic dependencies that hold in the domain. A PRM, together with a particular database of objects, defines a probability distribution over the attributes of the objects and the relations that hold between them. The relational component describes entities in the model, attributes of each entity, and references from one entity to another. The probabilistic component describes dependencies among attributes, both within the same entity and between attributes in related entities.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2000 LearningProbRelModels				Learning Probabilistic Relational Models			http://dx.doi.org/10.1007/3-540-44914-0	10.1007/3-540-44914-0