Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Toward principles for the design of ontologies used for knowledge sharing
International Journal of Human-Computer Studies - Special issue: the role of formal ontology in the information technology
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Learning equivalence classes of bayesian-network structures
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
Bayesian network learning algorithms using structural restrictions
International Journal of Approximate Reasoning
FBIT '07 Proceedings of the 2007 Frontiers in the Convergence of Bioscience and Information Technologies
Ontology change: Classification and survey
The Knowledge Engineering Review
Integrating Ontological Knowledge for Iterative Causal Discovery and Visualization
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Causal inference and causal explanation with background knowledge
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
A versioning management model for ontology-based data warehouses
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Learning causal bayesian networks from observations and experiments: a decision theoretic approach
MDAI'06 Proceedings of the Third international conference on Modeling Decisions for Artificial Intelligence
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Learning Causal Bayesian Networks (CBNs) is a new line of research in the machine learning field. Within the existing works in this direction [8,12,13], few of them have taken into account the gain that can be expected when integrating additional knowledge during the learning process. In this paper, we present a new serendipitous strategy for learning CBNs using prior knowledge extracted from ontologies. The integration of such domain's semantic information can be very useful to reveal new causal relations and provide the necessary knowledge to anticipate the optimal choice of experimentations. Our strategy also supports the evolving character of the semantic background by reusing the causal discoveries in order to enrich the domain ontologies.