Discriminative Structure Learning of Markov Logic Networks
Marenglen Biba
and
Stefano Ferilli
and
Floriana Esposito
Abstract:
Markov Logic Networks (MLNs) combine Markov networks
and first-order logic by attaching weights to first-order formulas and
viewing these as templates for features of Markov networks. Learning
the structure of MLNs is performed by state-of-the-art methods by max-
imizing the likelihood of a relational database. This can lead to subop-
timal results given prediction tasks. On the other hand better results
in prediction problems have been achieved by discriminative learning of
MLNs weights given a certain structure. In this paper we propose an
algorithm for learning the structure of MLNs discriminatively by max-
imimizing the conditional likelihood of the query predicates instead of
the joint likelihood of all predicates. The algorithm chooses the struc-
tures by maximizing conditional likelihood and sets the parameters by
maximum likelihood. Experiments in two real-world domains show that
the proposed algorithm improves over the state-of-the-art discriminative
weight learning algorithm for MLNs in terms of conditional likelihood.
We also compare the proposed algorithm with the state-of-the-art gen-
erative structure learning algorithm for MLNs and con rm the results in
[22] showing that for small datasets the generative algorithm is compet-
itive, while for larger datasets the discriminative algorithm outperfoms
the generative one.
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