Abstract

Relational learning analyzes the probabilistic constraints between the attributes of entities and relationships. We extend the expressiveness of relational models by introducing for each entity (or object) an infinite-state latent variable as part of a Dirichlet process (DP) mixture model. It can be viewed as a relational generalization of hidden Markov random field. The information propagates in the intern-connected network via latent variables, reducing the necessary for extensive structure learning. For inference, we explore a Gibbs sampling method based on the Chinese restaurant process. The performance of our model is demonstrated in three applications: the movie recommendation, the function prediction of genes and a medical recommendation system.

Citations