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Statistical Relational Learning with Formal Ontologies
[chapter]
2009
Lecture Notes in Computer Science
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
doi:10.1007/978-3-642-04174-7_19
fatcat:n7cxw32gyrau7kd4sc3qqkcvsi