Quantitative Methods in Defense and National Security 2007
Abstract

Bayesian Ontologies in Net-Centric Systems
Kathryn Blackmond Laskey, (George Mason University), klaskey@gmu.edu, and
Paulo C.G. Costa, (George Mason University), pcosta@gmu.edu

Abstract

Ontologies have become ubiquitous in current-generation information systems, and are a key enabling technology for Net-Centric Warfare. 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.


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