Applying the Information Bottleneck Approach to SRL: Learning LPAD Parameters

In this paper, we propose to apply the Information Bottleneck (IB) approach to a sub-class of Statistical Relational Learning (SRL) languages. Learning parameters in SRL dealing with domains that involve hidden variables requires the use of techniques for learning from incomplete data such as the expectation maximization (EM) algorithm. Recently, IB was shown to overcome well known problems of the EM algorithm. Here we show that learning in SRL languages reducible to Bayesian Networks can be obtained by applying the IB approach. In particular, our focus is on the problem of learning the parameters of Logic Programs with Annotated Disjunction (LPADs). We adopt a reductionist approach in which an acyclic LPAD is translated into a Bayesian network. The reduction process introduces in the network some hidden variables thus naturally requiring the use of the IB approach. The paper illustrates the algorithm Relational Information Bottleneck (RIB) that learns LPAD parameters and shows some promising experimental results.