RockIt: Exploiting Parallelism and Symmetry for MAP Inference in Statistical Relational Models

Noessner, Jan ; Niepert, Mathias ; Stuckenschmidt, Heiner

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Document Type: Conference or workshop publication
Year of publication: 2013
Book title: Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, July 14-18, 2012, Bellevue, Washington, USA
Page range: 739-745
Date of the conference: July 14-18, 2013
Publisher: DesJardins, Marie
Place of publication: Menlo Park, Calif.
Publishing house: AAAI Press
ISBN: 978-1-57735-615-8
Publication language: English
Institution: School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Subject: 004 Computer science, internet
Keywords (English): Markov Logic , Statistical Relational Models , Lifted Inference , Parallelization
Abstract: ROCKIT is a maximum a-posteriori (MAP) query engine for statistical relational models. MAP inference in graphical models is an optimization problem which can be compiled to integer linear programs (ILPs). We describe several advances in translating MAP queries to ILP instances and present the novel meta-algorithm cutting plane aggregation (CPA). CPA exploits local context-specific symmetries and bundles up sets of linear constraints. The resulting counting constraints lead to more compact ILPs and make the symmetry of the ground model more explicit to state-of-the-art ILP solvers. Moreover, ROCKIT parallelizes most parts of the MAP inference pipeline taking advantage of ubiquitous shared-memory multi-core architectures. We report on extensive experiments with Markov logic network (MLN) benchmarks showing that ROCKIT outperforms the state-of-the-art systems ALCHEMY, MARKOV THEBEAST, and TUFFY both in terms of efficiency and quality of results.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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