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Improving the Accuracy and Efficiency of MAP Inference for Markov Logic

Riedel, S; (2008) Improving the Accuracy and Efficiency of MAP Inference for Markov Logic. In: McAllester, DA and Myllymäki, P, (eds.) Proceedings of the 24th Conference Conference on Uncertainty in Artificial Intelligence 2008. (pp. 468 - 475). AUAI Press: Helsinki, Finland. Green open access

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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.

Type: Proceedings paper
Title: Improving the Accuracy and Efficiency of MAP Inference for Markov Logic
Event: 24th Conference Conference on Uncertainty in Artificial Intelligence 2008
ISBN: 0-9749039-4-9
Open access status: An open access version is available from UCL Discovery
Publisher version: https://dslpitt.org/uai/displayArticles.jsp?mmnu=1...
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
URI: http://discovery.ucl.ac.uk/id/eprint/1367766
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