I found one interesting application of Bayesian Networks for the Semantic Web, and it’s aimed to help ontology mapping between two different Ontologies, i.e. determine how much Onto1:Concept1 is similar to Onto2:Concept2, thus trying to map two different concepts obtaining a value that corresponds to the degree of similarity.

The details of how it works are pretty interesting. First, the Ontologies are converted to Bayesian Networks using a framework called BayesOWL (references on the paper); the resulting Bayesian Network preserves the semantics of the original ontologies, and support ontology reasoning, within and across ontologies, using Bayesian inferences. This BayesOWL framework provides the methods that utilizes available probability constraints about classes and inter-class relations in constructing the the conditional probability tables of the network.

Prior probability distributions of uncertainty about concepts used for the framework, conditional distributions for relations between classes in the same ontology and joint probability distributions for semantic similarity between different concepts in different ontologies, are constructed based on machine learning of these probabilities using text classification techniques, associating a concept with a group of sample text documents called exemplars, retrieved from a search engine.

The main idea of this research is to provide a simple and efficient way of determine the semantic similarity of two concepts in distinct ontologies. This is an approach that I didn’t know, but it’s worth to keep in mind when doing research in Semantic Web technologies.

Reference:

Pan, Rong, et. Al. **A Bayesian Network Approach to Ontology Mapping**. The Semantic Web – ISWC 2005. Lecture Notes in Computer Science. 2005 Springer Berlin / Heidelberg. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.122.3201&rep=rep1&type=pdf