Bayesian ontologies in AI systems

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dc.contributor.authorCosta, Paulo C. G.
dc.contributor.authorLaskey, Kathryn B.
dc.contributor.authorAlGhamdi, Ghazi
dc.date.accessioned2006-07-30T03:10:44Z
dc.date.available2006-07-30T03:10:44Z
dc.date.issued2006-07-30T03:10:44Z
dc.descriptionPaper 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.abstractOntologies 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.extent1115734 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.citationCosta, 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.
dc.identifier.urihttps://hdl.handle.net/1920/1149
dc.language.isoen_US
dc.subjectPR-OWL
dc.subjectProbabilistic ontologies
dc.subjectMulti-entity Bayesian networks
dc.subjectBayesian networks
dc.subjectDTB
dc.subjectBehavioral model
dc.subjectUncertainty reasoning
dc.titleBayesian ontologies in AI systems
dc.typeArticle

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