A Probabilistic Extension to Ontology Language OWL

Authors: , Yun Peng

Book Title: Proceedings of the 37th Hawaii International Conference On System Sciences (HICSS-37).


Abstract: To support uncertain ontology representation and ontology reasoning and mapping, we propose to incorporate Bayesian networks (BN), a widely used graphic model for knowledge representation under uncertainty and OWL, the de facto industry standard ontology language recommended by W3C. First, OWL is augmented to allow additional probabilistic markups, so probabilities can be attached with individual concepts and properties in an OWL ontology. Secondly, a set of translation rules is defined to convert this probabilistically annotated OWL ontology into the directed acyclic graph (DAG) of a BN. Finally, the BN is completed by constructing conditional probability tables (CPT) for each node in the DAG. Our probabilistic extension to OWL is consistent with OWL semantics, and the translated BN is associated with a joint probability distribution over the application domain. General Bayesian network inference procedures (e.g., belief propagation or junction tree) can be used to compute P(C|e): the degree of the overlap or inclusion between a concept C and a concept represented by a description e. We also provide a similarity measure that can be used to find the most similar concept that a given description belongs to.

Type: InProceedings

Tags: bayesian reasoning, owl, semantic web, uncertainty

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