This paper presents a probabilistic relational modelling (implementation) of the major probabilistic retrieval models. Such a high-level implementation is useful since it supports ...
Abstract. Existing relational learning approaches usually work on complete relational data, but real-world data are often incomplete. This paper proposes the MGDA approach to learn...
Probabilistic inference will be of special importance when one needs to know how much we can say with what all we know given new observations. Bayesian Network is a graphical prob...
Abstract. There is currently a large interest in relational probabilistic models. While the concept of context-specific independence (CSI) has been well-studied for models such as ...
This paper presents three new algorithms for the automatic construction of models from Object Oriented Probabilistic RelationalModels. The first two algorithms are based on the kn...