In the last decade, connectionist models have been proposed that can process structured information directly. These methods, which are based on the use of graphs for the representa...
Werner Uwents, Gabriele Monfardini, Hendrik Blocke...
This paper develops a new paradigm for relational learning which allows for the representation and learning of relational information using propositional means. This paradigm sugg...
We address the learning of trust based on past observations and context information. We argue that from the truster's point of view trust is best expressed as one of several ...
Building architectures for autonomous rational behavior requires the integration of several AI components, such as planning, learning and execution monitoring. In most cases, the ...
Abstract. Combining statistical and relational learning receives currently a lot of attention. The majority of statistical relational learning approaches focus on density estimatio...
For the last ten years a lot of work has been devoted to propositionalization techniques in relational learning. These techniques change the representation of relational problems t...
In this work, we study the problem of within-network relational learning and inference, where models are learned on a partially labeled relational dataset and then are applied to ...
Kernel methods have gained a great deal of popularity in the machine learning community as a method to learn indirectly in highdimensional feature spaces. Those interested in rela...