Learning directed probabilistic logical models: Ordering-search versus structure-search

We discuss how to learn non-recursive directed probabilistic logical models from relational data. This problem has been tackled before by upgrading the structure-search algorithm initially proposed for Bayesian networks. In this paper we propose to upgrade another algorithm, namely ordering-search, since for Bayesian networks this was found to work better than structure-search. We experimentally compare the two upgraded algorithms on two relational domains. We conclude that there is no signifcant difference between the two algorithms in terms of quality of the learnt models while ordering-search is signifcantly faster.Acceptance rate = 24%status: publishe