Dirichlet enhanced relational learning

We apply nonparametric hierarchical Bayesian modelling to relational learning. In a hierarchical Bayesian approach, model parameters can be "personalized", i.e., owned by entities or relationships, and are coupled via a common prior distribution. Flexibility is added in a nonparametric hierarchical Bayesian approach, such that the learned knowledge can be truthfully represented. We apply our approach to a medical domain where we form a nonparametric hierarchical Bayesian model for relations involving hospitals, patients, procedures and diagnosis. The experiments show that the additional flexibility in a nonparametric hierarchical Bayes approach results in a more accurate model of the dependencies between procedures and diagnosis and gives significantly improved estimates of the probabilities of future procedures.

• Type: Conference
• Year: 2005
• Where: ICML
• Authors: Zhao Xu, Volker Tresp, Kai Yu, Shipeng Yu, Hans-Peter Kriegel
• Keywords: Hierarchical Bayesian Approach, Hierarchical Bayesian Model, ICML 2005, Machine Learning, Nonparametric Hierarchical Bayes