Traditional machine learning has assumed that the world can be described in terms of features, but the world is made up of objects that interrelate. Data about these worlds in inherently noisy and relational. Statistical relational learning deals with uncertainty and relations among objects. The importance of relational data is evident from its increasing presence: WWW, social networks, bibliographic network, organizational network, molecules, among others. For these cases graphs are not enough to encode probabilistic models: we need logical or relational models. Application areas include biology, robotics, ubiquitous computing, social network analysis, among others. Motivated by this, we are organizing the
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NIPS 2008 Workshop on Probabilistic Programming
Dagstuhl Seminar on Probabilistic, Logical and Relational Learning 2007
SRL 2006
Dagstuhl Seminar on Probabilistic, Logical and Relational Learning - Towards a Synthesis 2005
SRL 2004
SRL 2003
The very first workshop on SRL - 2000