Abstract In the Semantic Web vision of the World Wide Web, content will not only be
accessible to humans but will also be available in machine interpretable form as ontological …

Advanced statistical modeling and knowledge representation techniques for a newly
emerging area of machine learning and probabilistic reasoning; includes introductory …

Probabilistic logic programs are logic programs in which some of the facts are annotated
with probabilities. This paper investigates how classical inference and learning tasks known …

A multitude of different probabilistic programming languages exists today, all extending a
traditional programming language with primitives to support modeling of complex, structured …

Inducing causal relationships from observations is a classic problem in scientific inference,
statistics, and machine learning. It is also a central part of human learning, and a task that …

There have been many proposals for first-order belief networks (ie, where we quantify over
individuals) but these typically only let us reason about the individuals that we know about …

We present a prototype system, code-named Pulse, for mining topics and sentiment
orientation jointly from free text customer feedback. We describe the application of the …

Many AI problems, ranging from sensor data association to linguistic coreference resolution,
involve making inferences about real-world objects that underlie some data. In many cases …

Probabilistic inductive logic programming aka. statistical relational learning addresses one
of the central questions of artificial intelligence: the integration of probabilistic reasoning with …

Previous papers have studied learning of Stochastic Logic Programs (SLPs) either as a
purely parametric estimation problem or separated structure learning and parameter …