Graph-based methods for semi-supervised learning have recently been shown to be promising for combining labeled and unlabeled data in classification problems. However, inference f...
In this paper, we propose a novel probabilistic approach to summarize frequent itemset patterns. Such techniques are useful for summarization, post-processing, and end-user interp...
Abstract. This article presents a method for training Dynamic Factor Graphs (DFG) with continuous latent state variables. A DFG includes factors modeling joint probabilities betwee...
Abstract. The Semantic Web is commonly perceived as a web of partially interlinked machine readable data. This data is inherently distributed and resembles the structure of the web...
In this paper, we consider the problem of materializing the complete finite RDFS closure in a scalable manner; this includes those parts of the RDFS closure that are often ignored...