Jun 21, 2018 · Abstract. Probabilistic Relational Models (PRMs) extend Bayesian net- works (BNs) with the notion of class of relational databases.

Oct 21, 2017 · To present our approach, we describe an implementation based on an ontology of transformation processes and compare its performance to that of a ...

Learning probabilistic relational models using an ontology of transformation processes. January 2017. Authors: Melanie Munch at University of Bordeaux · Melanie ...

To present our approach, we describe an implementation based on an on-tology of transformation processes and compare its performance to that of a method that ...

This paper proposes a method that learns a PRM from data using the semantic knowledge of an ontology describing these data in order to make the learning ...

To present our approach, we describe an implementation based on an ontology of transformation processes and compare its performance to that of a method that ...

Learning Probabilistic Relational Models Using an Ontology of Transformation Processes. https://doi.org/10.1007/978-3-319-69459-7_14 · Full text.

Aug 11, 2000 · My work is on learning Probabilistic Relational Models (PRMs) from structured data (e.g., data in a relational database, an object-oriented ...

Probabilistic Relational Models extend the standard framework of Bayesian Networks by modeling properties and relations between data objects. ...

Learning Probabilistic Relational Models Using an Ontology of Transformation Processes. Lecture Notes in Computer Science - Germany.