Improving the Accuracy and Efficiency of MAP Inference for Markov Logic
Sebastian Riedel
Abstract:
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.
Download:
PDF