Infinite hidden semantic models for learning with OWL DL

We propose a learning model for integrating OWL DL ontologies with statistical learning. In contrast to existing learning methods for the Semantic Web and approaches to the use of prior knowledge in machine learning, we allow for a semantically rich and fully formal representation of rules and constraints which enhance and control the learning task. In our implementation, we achieve this by combining a latent relational graphical model with description logic inference in a modular fashion. To demonstrate the feasibility of our approach we provide experiments with real world data accompanied by a set of SHOIN(D) axioms. The results illustrate two practical advancements: First, the probability of unknown roles of individuals can be inductively inferred without violating the constraints and second, known ABox axioms can be analyzed by means of clustering individuals per associated concept.