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2008

Improving the Accuracy and Efficiency of MAP Inference for Markov Logic

9 years 5 months ago
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.
Sebastian Riedel
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2008
Where UAI
Authors Sebastian Riedel
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