PR-OWL 2 RL - A Language for Scalable Uncertainty Reasoning on the Semantic Web information
نویسندگان
چکیده
Probabilistic OWL (PR-OWL) improves the Web Ontology Language (OWL) with the ability to treat uncertainty using Multi-Entity Bayesian Networks (MEBN). PR-OWL 2 presents a better integration with OWL and its underlying logic, allowing the creation of ontologies with probabilistic and deterministic parts. However, there are scalability problems since PR-OWL 2 is built upon OWL 2 DL which is a version of OWL based on description logic SROIQ(D) and with high complexity. To address this issue, this paper proposes PR-OWL 2 RL, a scalable version of PR-OWL based on OWL 2 RL profile and triplestores (databases based on RDF triples). OWL 2 RL allows reasoning in polynomial time for the main reasoning tasks. This paper also presents First-Order expressions accepted by this new language and analyzes its expressive power. A comparison with the previous language presents which kinds of problems are more suitable for each version of PR-OWL.
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