Lifted Temporal Most Probable Explanation

Marcel Gehrke*, Tanya Braun, Ralf Möller

*Corresponding author for this work


The lifted dynamic junction tree algorithm (LDJT) answers filtering and prediction queries efficiently for temporal probabilistic relational models by building and then reusing a first-order cluster representation of a knowledge base for multiple queries and time steps. Another type of query asks for a most probable explanation (MPE) for given events. Specifically, this paper contributes (i) LDJTmpe to efficiently solve the temporal MPE problem for temporal probabilistic relational models and (ii) a combination of LDJT and LDJTmpe to efficiently answer assignment queries for a given number of time steps.

Original languageEnglish
Title of host publicationICCS 2019: Graph-Based Representation and Reasoning
EditorsDominik Endres, Mehwish Alam, Diana Şotropa
Number of pages14
Volume11530 LNAI
PublisherSpringer, Cham
Publication date19.06.2019
ISBN (Print)978-3-030-23181-1
ISBN (Electronic)978-3-030-23182-8
Publication statusPublished - 19.06.2019
Event24th International Conference on Conceptual Structures - Marburg, Germany
Duration: 01.07.201904.07.2019
Conference number: 227759

Research Areas and Centers

  • Centers: Center for Artificial Intelligence Luebeck (ZKIL)
  • Research Area: Intelligent Systems


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