Author:

Keywords:

statistical relational learning, probabilistic logical models, inductive logic programming, Bayesian networks, probability trees, structure learning, Science & Technology, Technology, Physical Sciences, Computer Science, Artificial Intelligence, Mathematics, Applied, Computer Science, Mathematics, Statistical relational learning, Probabilistic logical models, Inductive logic programming, Probability trees, Structure learning, BAYESIAN NETWORKS, 0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0802 Computation Theory and Mathematics, Artificial Intelligence & Image Processing, 4602 Artificial intelligence, 4613 Theory of computation, 4901 Applied mathematics

Abstract:

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 show how to upgrade another algorithm for learning Bayesian networks, namely ordering-search. For Bayesian networks, ordering-search was found to work better than structure-search. It is non-obvious that these results carry over to the relational case, however, since there ordering-search needs to be implemented quite differently. Hence, we perform an experimental comparison of these upgraded algorithms on four relational domains. We conclude that also in the relational case ordering-search is competitive with structure-search in terms of quality of the learned models, while ordering-search is significantly faster.