Instance-based Probabilistic Reasoning in the Semantic Web

Authors

Abstract

Most of the approaches for dealing with uncertainty in the Semantic Web rely on the principle that this uncertainty is already asserted. In this paper, we propose a new approach to learn and reason about uncertainty in the Semantic Web. Using instance data, we learn the uncertainty of an OWL ontology, and use that information to perform probabilistic reasoning on it. For this purpose, we use Markov logic, a new representation formalism that combines logic with probabilistic graphical models.

Keywords

Semantic Web, Probabilistic Reasoning, Markov Logic

Subject

Semantic Web

Conference

18th International World Wide Web Conference, April 2009