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and Gomes, Paulo Instance-based Probabilistic Reasoning in the Semantic Web.
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. cumbersome and difficult task, invalidating all the gains that could arise from the annotation. In fact, uncertainty is a common characteristic of the current Web. When we create a webpage, for example, search engines are responsible to assert what is the probabilistic relevance of it, compared to other pages, to certain topics. We don’t have to explicitly refer that information: we just create its content, and search engines do the rest. So, we must develop similar automatic mechanisms to perform reasoning in the Semantic Web. In this work, we study how we can make probabilistic reasoning on OWL ontologies without any kind of uncertainty annotation. To assert the uncertainty of its axioms, we use solely the information of its instances. For this purpose, we use Markov logic , a novel approach that combines logic and probability in the same representation.
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