Integrating Ontological Prior Knowledge into Relational Learning

Ontologies represent an important source of prior information which lends itself to the integration into statistical modeling. This paper discusses approaches towards employing ontological knowledge for relational learning. Our analysis is based on the IHRM model that performs relational learning by including latent variables that can be interpreted as cluster variables of the entities in the domain. We apply our approach to the modeling of yeast genomic data and demonstrate that the inclusion of ontologies as prior knowledge in relational learning can lead to significantly improved results and to better interpretable clustering structures.