Improving the Accuracy and Efficiency of MAP Inference for Markov Logic

  title={Improving the Accuracy and Efficiency of MAP Inference for Markov Logic},
  author={Sebastian Riedel},
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… CONTINUE READING
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