Fully Parallel Inference in Markov Logic Networks
Kaustubh Beedkar, Luciano Del Corro, Rainer Gemulla · 2013
Abstract: Markov logic is apowerful tool for handling the uncertainty that arises in real-world structured data; it has been applied successfully to anumber ofdata management problems. In practice, the resulting ground Markov logic networks can get very large, which poses challenges to scalable inference. In this paper, we present the first fully parallelized approach toinference in Markov logic networks. Inference decomposes into agrounding step and a probabilistic inference step, both of which can be cost-intensive. We propose aparallel grounding algorithm that partitions the Markov logic network based on its corresponding join graph; each partition is ground independently and in parallel. Our partitioning scheme is based on importance sampling, which we use for parallel probabilistic inference, and is also well-suited to other, more efficient parallel inference techniques. Preliminary experiments suggest that significant speedup can be gained by parallelizing both grounding and probabilistic inference. 1