Learning and Reasoning about Uncertainty in the Semantic Web
The main idea behind the Semantic Web is the representation of knowledge in an explicit and formal way. This is done using ontology representation languages as OWL, which is based on Description Logics and other logic formalisms. One of the main objectives with this kind of knowledge representation is that it can then be used for reasoning. But the way reasoning is done in the Semantic Web technology is very strict, defining only a right and wrong view of the world. The real world is uncertain and humans have learned how to deal with this crucial aspect. In this paper, we present an approach to reasoning with uncertainty information in the Semantic Web. We have applied Markov Logic, which is able to reason with uncertainty information, to several Semantic Web ontologies, showing that it can be used in several applications. We also describe the main challenges for reasoning with uncertainty in the Semantic Web.
Semantic Web, Probabilistic Reasoning, Markov Logic
14th Portuguese Conference on Artificial Intelligence, October 2009
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