Improving the Accuracy and Efficiency of MAP Inference for Markov Logic
  In this work we present Cutting Plane Inference (CPI), a Maximum A Posteriori
(MAP) inference method for Statistical Relational Learning. Framed in terms of
Markov Logic and inspired by the Cutting Plane Method, it can be seen as a meta
algorithm that instantiates small parts of a large and complex Markov Network
and then solves these using a conventional MAP method. We evaluate CPI on two
tasks, Semantic Role Labelling and Joint Entity Resolution, while plugging in
two different MAP inference methods: the current method of choice for MAP
inference in Markov Logic, MaxWalkSAT, and Integer Linear Programming. We
observe that when used with CPI both methods are significantly faster than when
used alone. In addition, CPI improves the accuracy of MaxWalkSAT and maintains
the exactness of Integer Linear Programming.
