Speeding up parameter and rule learning for acyclic probabilistic logic programs

© 2018 Elsevier Inc. This paper introduces techniques that speed-up parameter and rule learning for acyclic probabilistic logic programs. We focus on maximum likelihood estimation of parameters, and show that significant improvements can be obtained by efficiently handling probabilistic rules. We then move to structure learning, where we learn sets of rules, by introducing an algorithm that greatly simplifies exact score-based learning. Experiments demonstrate that our methods can produce orders of magnitude speed-ups over the state-of-art in parameter and rule learning.