Leveraging Ontologies for Lifted Probabilistic Inference and Learning

Exploiting ontologies for efficient inference is one of the most widely studied topics in knowledge representation and reasoning. The use of ontologies for probabilistic inference, however, is much less developed. A number of algorithms for lifted inference in first-order probabilistic languages have been proposed, but their scalability is limited by the combinatorial explosion in the sets of objects that need to be considered. We propose a coarse-to-fine inference approach that leverages a class hierarchy to combat this problem. Starting at the highest level, our approach performs inference at successively finer grains, pruning low-probability atoms before refining. We provide bounds on the error incurred by this approach relative to full ground inference as a function of the pruning threshold. We also show how to learn parameters in a coarse-to-fine manner to maximize the opportunities for pruning during inference. Experiments on link prediction and biomolecular event prediction tasks show our method can greatly improve the scalability of lifted probabilistic inference.