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ECAI
2010
Springer
13 years 6 months ago
Adaptive Markov Logic Networks: Learning Statistical Relational Models with Dynamic Parameters
Abstract. Statistical relational models, such as Markov logic networks, seek to compactly describe properties of relational domains by representing general principles about objects...
Dominik Jain, Andreas Barthels, Michael Beetz
ICML
1996
IEEE
14 years 10 months ago
Learning Relational Concepts with Decision Trees
In this paper, we describe two di erent learning tasks for relational structures. When learning a classi er for structures, the relational structures in the training sets are clas...
Peter Geibel, Fritz Wysotzki
ICDE
1996
IEEE
120views Database» more  ICDE 1996»
14 years 10 months ago
Towards the Reverse Engineering of Denormalized Relational Databases
This paper describes a method to cope with denormalized relational schemas in a database reverse engineering process. We propose two main steps to irnprove the understanding of da...
Jean-Marc Petit, Farouk Toumani, Jean-Franç...
PODS
2003
ACM
135views Database» more  PODS 2003»
14 years 9 months ago
Query-preserving watermarking of relational databases and XML documents
Watermarking allows robust and unobtrusive insertion of information in a digital document. Very recently, techniques have been proposed for watermarking relational databases or XM...
David Gross-Amblard
KI
2007
Springer
14 years 3 months ago
Extending Markov Logic to Model Probability Distributions in Relational Domains
Abstract. Markov logic, as a highly expressive representation formalism that essentially combines the semantics of probabilistic graphical models with the full power of first-orde...
Dominik Jain, Bernhard Kirchlechner, Michael Beetz