First-Order Rewritability of Ontology-Mediated Queries in Linear Temporal Logic

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Alessandro Artale - , Free University of Bozen-Bolzano (Author)
  • Roman Kontchakov - , Birkbeck University of London (Author)
  • Alisa Kovtunova - , Chair of Automata Theory (Author)
  • Vladislav Ryzhikov - , Birkbeck University of London (Author)
  • Frank Wolter - , University of Liverpool (UOL) (Author)
  • Michael Zakharyaschev - , Birkbeck University of London, Higher School of Economics (Author)

Abstract

We investigate ontology-based data access to temporal data. We consider temporal ontologies given in linear temporal logic LTL interpreted over discrete time (Z,<). Queries are given in LTL or MFO(<), monadic first-order logic with a built-in linear order. Our concern is first-order rewritability of ontology-mediated queries (OMQs) consisting of a temporal ontology and a query. By taking account of the temporal operators used in the ontology and distinguishing between ontologies given in full LTL and its core, Krom and Horn fragments, we identify a hierarchy of OMQs with atomic queries by proving rewritability into either FO(<), first-order logic with the built-in linear order, or FO(<,≡), which extends FO(<) with the standard arithmetic predicates x≡0(modn), for any fixed n>1, or FO(RPR), which extends FO(<) with relational primitive recursion. In terms of circuit complexity, FO(<,≡)- and FO(RPR)-rewritability guarantee OMQ answering in uniform [Formula presented] and, respectively, [Formula presented]. We obtain similar hierarchies for more expressive types of queries: positive LTL-formulas, monotone MFO(<)- and arbitrary MFO(<)-formulas. Our results are directly applicable if the temporal data to be accessed is one-dimensional; moreover, they lay foundations for investigating ontology-based access using combinations of temporal and description logics over two-dimensional temporal data.

Details

Original languageEnglish
Article number103536
JournalArtificial intelligence
Volume299
Publication statusPublished - Oct 2021
Peer-reviewedYes

External IDs

Scopus 85106470272