  Relational logistic regression (RLR) is a representation of conditional
probability in terms of weighted formulae for modelling multi-relational data.
In this paper, we develop a learning algorithm for RLR models. Learning an RLR
model from data consists of two steps: 1- learning the set of formulae to be
used in the model (a.k.a. structure learning) and learning the weight of each
formula (a.k.a. parameter learning). For structure learning, we deploy Schmidt
and Murphy's hierarchical assumption: first we learn a model with simple
formulae, then more complex formulae are added iteratively only if all their
sub-formulae have proven effective in previous learned models. For parameter
learning, we convert the problem into a non-relational learning problem and use
an off-the-shelf logistic regression learning algorithm from Weka, an
open-source machine learning tool, to learn the weights. We also indicate how
hidden features about the individuals can be incorporated into RLR to boost the
learning performance. We compare our learning algorithm to other structure and
parameter learning algorithms in the literature, and compare the performance of
RLR models to standard logistic regression and RDN-Boost on a modified version
of the MovieLens data-set.
