Abstract
Research in the past decade on statistical relational learning (SRL)
has shown the power of the underlying network of relations in
relational data. Even models built using only relations often
perform comparably to models built using sophisticated relational
learning methods. However, many data sets--such as those in the UCI
machine learning repository--contain no relations. In fact, many data
sets either do not contain relations or have relations which are not
helpful to a specific classification task. The question we
investigate in this paper is whether it is possible to construct
relations such that relational inference results in better
classification performance than non-relational inference. Using
simple similarity-based rules to create relations and weighting the
strength of these relations using homophily on instance labels, we
test whether relational inference techniques are applicable--in other
words, do they perform comparably to standard machine learning
algorithms. We show, in an experimental study on $31$ UCI benchmark
data sets, that relational inferecne wins more than any of the $6$
classifiers we compare against, including a transductive SVM, and that
it wins the majority of the time when compared against any one of
them.