Efficient Inference and Learning in a Large Knowledge Base: Reasoning
  with Extracted Information using a Locally Groundable First-Order
  Probabilistic Logic
  One important challenge for probabilistic logics is reasoning with very large
knowledge bases (KBs) of imperfect information, such as those produced by
modern web-scale information extraction systems. One scalability problem shared
by many probabilistic logics is that answering queries involves "grounding" the
query---i.e., mapping it to a propositional representation---and the size of a
"grounding" grows with database size. To address this bottleneck, we present a
first-order probabilistic language called ProPPR in which that approximate
"local groundings" can be constructed in time independent of database size.
Technically, ProPPR is an extension to stochastic logic programs (SLPs) that is
biased towards short derivations; it is also closely related to an earlier
relational learning algorithm called the path ranking algorithm (PRA). We show
that the problem of constructing proofs for this logic is related to
computation of personalized PageRank (PPR) on a linearized version of the proof
space, and using on this connection, we develop a proveably-correct approximate
grounding scheme, based on the PageRank-Nibble algorithm. Building on this, we
develop a fast and easily-parallelized weight-learning algorithm for ProPPR. In
experiments, we show that learning for ProPPR is orders magnitude faster than
learning for Markov logic networks; that allowing mutual recursion (joint
learning) in KB inference leads to improvements in performance; and that ProPPR
can learn weights for a mutually recursive program with hundreds of clauses,
which define scores of interrelated predicates, over a KB containing one
million entities.
