Fundamental concepts of qualitative probabilistic networks
Artificial Intelligence
Enabling technology for knowledge sharing
AI Magazine
A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Building Probabilistic Networks: 'Where Do the Numbers Come From?' Guest Editors' Introduction
IEEE Transactions on Knowledge and Data Engineering
Bayesian Networks for Reliability Analysis of Complex Systems
IBERAMIA '98 Proceedings of the 6th Ibero-American Conference on AI: Progress in Artificial Intelligence
Seabreeze Prediction Using Bayesian Networks
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Combining knowledge from different sources in causal probabilistic models
The Journal of Machine Learning Research
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
OntoBayes: An Ontology-Driven Uncertainty Model
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
Learning Bayesian Networks
Towards efficient variables ordering for Bayesian networks classifier
Data & Knowledge Engineering
Using Ranked Nodes to Model Qualitative Judgments in Bayesian Networks
IEEE Transactions on Knowledge and Data Engineering
PR-OWL: A Bayesian Ontology Language for the Semantic Web
Uncertainty Reasoning for the Semantic Web I
Verifying monotonicity of Bayesian networks with domain experts
International Journal of Approximate Reasoning
Formalizing information security knowledge
Proceedings of the 4th International Symposium on Information, Computer, and Communications Security
Ontologies and Bayesian Networks in Medical Diagnosis
HICSS '11 Proceedings of the 2011 44th Hawaii International Conference on System Sciences
Multiplicative factorization of noisy-max
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Elicitation of probabilities for belief networks: combining qualitative and quantitative information
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Learning Bayesian network structures by searching for the best ordering with genetic algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Data & Knowledge Engineering
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Bayesian networks are commonly used for determining the probability of events that are influenced by various variables. Bayesian probabilities encode degrees of belief about certain events, and a dynamic knowledge body is used to strengthen, update, or weaken these assumptions. The creation of Bayesian networks requires at least three challenging tasks: (i) the determination of relevant variables (nodes), (ii) the determination of relationships between the identified variables (links), and (iii) the calculation of the conditional probability tables (CPTs) for each node in the Bayesian network. Based on existing domain ontologies, we propose a method for the ontology-based construction of Bayesian networks. The method supports (i) the construction of the graphical Bayesian network structure (nodes and links), (ii) the construction of CPTs that preserve semantic constraints of the ontology, and (iii) the incorporation of already existing knowledge facts (findings). The developed method enables the efficient construction and modification of Bayesian networks based on existing ontologies.