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Bayesian ontologies in AI systems

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dc.contributor.author Costa, Paulo C. G.
dc.contributor.author Laskey, Kathryn B.
dc.contributor.author AlGhamdi, Ghazi
dc.date.accessioned 2006-07-30T03:10:44Z
dc.date.available 2006-07-30T03:10:44Z
dc.date.issued 2006-07-30T03:10:44Z
dc.identifier.citation Costa, Paulo C. G.; Laskey, Kathryn B.; and Alghamdi, Ghazi (2006) Bayesian Ontologies in AI Systems. Proceedings of the Fourth Bayesian Modelling Applications Workshop, held at the Twenty Second Conference on Uncertainty in Artificial Intelligence (UAI 2006). July, 13 2006, Cambridge, MA, USA. en
dc.identifier.uri https://hdl.handle.net/1920/1149
dc.description Paper presented at the Fourth Bayesian Modelling Applications Workshop, held at the Twenty Second Conference on Uncertainty in Artificial Intelligence (UAI 2006). July, 13 2006, Cambridge, MA, USA. en
dc.description.abstract Ontologies have become ubiquitous in current-generation information systems. An ontology is an explicit, formal representation of the entities and relationships that can exist in a domain of application. Following a well-trodden path, initial research in computational ontology has neglected uncertainty, developing almost exclusively within the framework of classical logic. As appreciation grows of the limitations of ontology formalisms that cannot represent uncertainty, the demand from user communities increases for ontology formalisms with the power to express uncertainty. Support for uncertainty is essential for interoperability, knowledge sharing, and knowledge reuse. Bayesian ontologies are used to describe knowledge about a domain with its associated uncertainty in a principled, structured, sharable, and machine-understandable way. This paper considers Multi-Entity Bayesian Networks (MEBN) as a logical basis for Bayesian ontologies, and describes PR-OWL, a MEBN-based probabilistic extension to the ontology language OWL. To illustrate the potentialities of Bayesian probabilistic ontologies in the development of AI systems, we present a case study in information security, in which ontology development played a key role.
dc.format.extent 1115734 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US en
dc.subject PR-OWL en_US
dc.subject probabilistic ontologies en_US
dc.subject multi-entity Bayesian networks en_US
dc.subject Bayesian networks en_US
dc.subject DTB en_US
dc.subject behavioral model en_US
dc.subject uncertainty reasoning en_US
dc.title Bayesian ontologies in AI systems en
dc.type Article en


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