Preventing Groundings and Handling Evidence in the Lifted Junction Tree Algorithm

Tanya Braun, Ralf Möller


For inference in probabilistic formalisms with first-order constructs, lifted variable elimination (LVE) is one of the standard approaches for single queries. To handle multiple queries efficiently, the lifted junction tree algorithm (LJT) uses a specific representation of a first-order knowledge base and LVE in its computations. Unfortunately, LJT induces unnecessary groundings in cases where the standard LVE algorithm, GC-FOVE, has a fully lifted run. Additionally, LJT does not handle evidence explicitly. We extend LJT (i) to identify and prevent unnecessary groundings and (ii) to effectively handle evidence in a lifted manner. Given multiple queries, e.g., in machine learning applications, our extension computes answers faster than LJT and GC-FOVE.
Original languageEnglish
Title of host publicationKI 2017: Advances in Artificial Intelligence
EditorsGabriele Kern-Isberner, Johannes Fürnkranz, Matthias Thimm
Number of pages14
Place of PublicationCham
PublisherSpringer International Publishing
Publication date19.09.2017
ISBN (Print)978-3-319-67189-5
ISBN (Electronic)978-3-319-67190-1
Publication statusPublished - 19.09.2017
Event40th Annual German Conference on Artificial Intelligence - Dortmund, Germany
Duration: 25.09.201729.09.2017
Conference number: 199309

DFG Research Classification Scheme

  • 409-01 Theoretical Computer Science


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