Discriminative Structure and Parameter Learning for Markov Logic Networks
Markov logic networks (MLNs) are an expressive
representation for statistical relational learning
that generalizes both first-order logic and
graphical models. Existing methods for learning
the logical structure of an MLN are not discriminative;
however, many relational learning
problems involve specific target predicates that
must be inferred from given background information.
We found that existing MLN methods
perform very poorly on several such ILP benchmark
problems, and we present improved discriminative
methods for learning MLN clauses
and weights that outperform existing MLN and
traditional ILP methods.