Statistical Relational Learning with Formal Ontologies

  • Achim Rettinger
  • Matthias Nickles
  • Volker Tresp
Conference paper

DOI: 10.1007/978-3-642-04174-7_19

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5782)
Cite this paper as:
Rettinger A., Nickles M., Tresp V. (2009) Statistical Relational Learning with Formal Ontologies. In: Buntine W., Grobelnik M., Mladenić D., Shawe-Taylor J. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2009. Lecture Notes in Computer Science, vol 5782. Springer, Berlin, Heidelberg

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 \(\mathcal{SHOIN}(D)\) 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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Achim Rettinger
    • 1
  • Matthias Nickles
    • 2
  • Volker Tresp
    • 3
  1. 1.Technische Universität MünchenGermany
  2. 2.University of BathUnited Kingdom
  3. 3.Siemens AG, CT, IC, Learning SystemsGermany

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