rsLDA: A Bayesian hierarchical model for relational learning

We introduce and evaluate a technique to tackle relational learning tasks combining a framework for mining relational queries with a hierarchical Bayesian model. We present the novel rsLDA algorithm that works as follows. It initially discovers a set of relevant features from the relational data useful to describe in a propositional way the examples. This corresponds to reformulate the problem from a relational representation space into an attribute-value form. Afterwards, given this new features space, a supervised version of the Latent Dirichlet Allocation model is applied in order to learn the probabilistic model. The performance of the proposed method when applied on two real-world datasets shows an improvement when compared to other methods.