The description logic handbook: theory, implementation, and applications
The description logic handbook: theory, implementation, and applications
Managing uncertainty and vagueness in description logics for the Semantic Web
Web Semantics: Science, Services and Agents on the World Wide Web
Probabilistic inductive logic programming
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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.