PageRank, ProPPR, and stochastic logic programs

ProPPR is the first probabilistic logic language using personalized PageRank for inference. We consider personalized PageRank on the SLD tree of a stochastic logic program (SLP), show that the resulting probability distribution over answer substitutions can be represented by an incomplete SLP, and relate this result to ProPPR.status: publishe

PageRank, ProPPR, and stochastic logic programs

A key feature of ProPPR, a recent probabilistic logic language inspired by stochastic logic programs (SLPs), is its use of personalized PageRank for efficient inference. We adopt this view of probabilistic inference as a random walk over a graph constructed from a labeled logic program to investigate the relationship between these two languages, showing that the differences in semantics rule out direct, generally applicable translations between them.status: publishe