Status: Past project

Project Description:
Dealing with uncertainty is crucial in ontology engineering tasks such as domain modeling, ontology reasoning, and concept mapping between ontologies. The Bayes OWL project addresses this problem by exploring how uncertainty can be modeled in ontologies using Bayesian networks (BN). Our approach involves extending OWL to allow additional probabilistic markups for attaching probability information. Having done so, we can directly convert a probabilistically annotated OWL ontology into a BN structure using a set of structural translation rules. The conditional probability tables (CPTs) of this BN can then be constructed using a new method based on iterative proportional fitting procedure (IPFP). Such translated BNs can be used to support more accurate ontology reasoning under uncertainty as Bayesian inferences.

Start Date: September 2003

End Date: December 2006

Principal Investigator:
Yun Peng

Students:
Zhongli Ding
Rong Pan

There are 4 associated publications:  Hide the list...

4 Refereed Publications

2005

1. Yun Peng et al., "Modifying Bayesian Networks by Probability Constraints", InProceedings, Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence, July 2005, 735 downloads.

2. Zhongli Ding et al., "A Bayesian Methodology towards Automatic Ontology Mapping", InProceedings, Proceedings of the AAAI-05 C&O Workshop on Contexts and Ontologies: Theory, Practice and Applications, July 2005, 1524 downloads.

2004

3. Zhongli Ding et al., "A Bayesian Approach to Uncertainty Modeling in OWL Ontology", InProceedings, Proceedings of the International Conference on Advances in Intelligent Systems - Theory and Applications, November 2004, 1277 downloads.

4. Zhongli Ding et al., "A Probabilistic Extension to Ontology Language OWL", InProceedings, Proceedings of the 37th Hawaii International Conference On System Sciences (HICSS-37)., January 2004, 1532 downloads.

There is 1 associated resource:  Hide the list...

1. Uncertainty in Ontology Mapping: A Bayesian Perspective, Presentation.

Research Areas:
 Knowledge Representation and Reasoning
 Semantic Web