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Fusing First-Order Knowledge Compilation and the Lifted Junction Tree Algorithm

  • Tanya Braun
  • Ralf Möller
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11117)

Abstract

Standard approaches for inference in probabilistic formalisms with first-order constructs include lifted variable elimination (LVE) for single queries as well as first-order knowledge compilation (FOKC) based on weighted model counting. To handle multiple queries efficiently, the lifted junction tree algorithm (LJT) uses a first-order cluster representation of a model and LVE as a subroutine in its computations. For certain inputs, the implementation of LVE and, as a result, LJT ground parts of a model where FOKC runs without groundings. The purpose of this paper is to prepare LJT as a backbone for lifted query answering and to use any exact inference algorithm as subroutine. Fusing LJT and FOKC, by setting FOKC as a subroutine, allows us to compute answers faster than FOKC alone and LJT with LVE for certain inputs.

Keywords

Lifting Probabilistic logical models Variable elimination Weighted model counting 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of LübeckLübeckGermany

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