Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Formal ontology, conceptual analysis and knowledge representation
International Journal of Human-Computer Studies - Special issue: the role of formal ontology in the information technology
Probabilistic Expert Systems
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
The description logic handbook: theory, implementation, and applications
The description logic handbook: theory, implementation, and applications
A Probabilistic Extension to Ontology Language OWL
HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 4 - Volume 4
Auto-Extraction, Representation and Integration of a Diabetes Ontology Using Bayesian Networks
CBMS '07 Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
A computational model for causal and diagnostic reasoning in inference systems
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
Extending ontology queries with Bayesian network reasoning
INES'09 Proceedings of the IEEE 13th international conference on Intelligent Engineering Systems
Aircraft interior failure pattern recognition utilizing text mining and neural networks
Journal of Intelligent Information Systems
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Probabilistic reasoning is an essential feature when dealing with many application domains. Starting with the idea that ontologies are the right way to formalize domain knowledge and that Bayesian networks are the right tool for probabilistic reasoning, we propose an approach for extracting a Bayesian network from a populated ontology and for reasoning over it. The paper presents the theory behind the approach, its design and examples of its use.