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Statistical Relational Learning with Formal Ontologies

Achim Rettinger, Matthias Nickles, Volker Tresp

Published: 2009

Buchtitel: In Proceedings of The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)
Seiten: 286-301
Verlag: Springer
Referierte Veröffentlichung

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.

Download: Media:2009-Statistical-relational-learning-with-formal-ontologies.pdf
DOI Link: 10.1007/978-3-642-04174-7_19