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PKDD
2009
Springer

Statistical Relational Learning with Formal Ontologies

9 years 24 days ago
Statistical Relational Learning with Formal Ontologies
Abstract. We propose a learning approach for integrating formal knowledge into statistical inference by exploiting ontologies as a semantically rich and fully formal representation of prior knowledge. The logical constraints deduced from ontologies can be utilized to enhance and control the learning task by enforcing description logic satisfiability in a latent multi-relational graphical model. To demonstrate the feasibility of our approach we provide experiments using real world social network data in form of a SHOIN(D) ontology. The results illustrate two main practical advancements: First, entities and entity relationships can be analyzed via the latent model structure. Second, enforcing the ontological constraints guarantees that the learned model does not predict inconsistent relations. In our experiments, this leads to an improved predictive performance.
Achim Rettinger, Matthias Nickles, Volker Tresp
Added 27 May 2010
Updated 27 May 2010
Type Conference
Year 2009
Where PKDD
Authors Achim Rettinger, Matthias Nickles, Volker Tresp
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