KR2021Proceedings of the 18th International Conference on Principles of Knowledge Representation and ReasoningProceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning

Online event. November 3-12, 2021.

Edited by

ISSN: 2334-1033
ISBN: 978-1-956792-99-7

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Copyright © 2021 International Joint Conferences on Artificial Intelligence Organization

Finitely Materialisable Datalog Programs with Metric Temporal Operators

  1. Przemysław A. Wałęga(University of Oxford)
  2. Michał Zawidzki(University of Oxford)
  3. Bernardo Cuenca Grau(University of Oxford)


  1. Geometric, spatial, and temporal reasoning


DatalogMTL is an extension of Datalog with metric temporal operators that has recently received significant attention. In contrast to plain Datalog, where scalable implementations are often based on materialisation (a.k.a. forward chaining), reasoning algorithms for recursive fragments of DatalogMTL are automata-based and not well suited for practice. In this paper we propose the class of finitely materialisable DatalogMTL programs, for which forward chaining reasoning terminates after finitely many rounds of rule application. We show that, for bounded programs (a large fragment of DatalogMTL where temporal intervals are restricted to not mention infinity), checking whether a program is finitely materialisable is feasible in exponential time, and propose sufficient conditions for finite materialisability that can be checked more efficiently. We finally show that fact entailment over finitely materialisable bounded programs is ExpTime-complete, and hence no harder than Datalog reasoning.