A tetrachotomy of ontology-mediated queries with a covering axiom

Olga Gerasimova, Stanislav Kikot, Agi Kurucz, Vladimir Podolskii, Michael Zakharyaschev

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Our concern is the problem of efficiently determining the data complexity of answering queries mediated by description logic ontologies and constructing their optimal rewritings to standard database queries. Originated in ontology-based data access and datalog optimisation, this problem is known to be computationally very complex in general, with no explicit syntactic characterisations available. In this article, aiming to understand the fundamental roots of this difficulty, we strip the problem to the bare bones and focus on Boolean conjunctive queries mediated by a simple covering axiom stating that one class is covered by the union of two other classes. We show that, on the one hand, these rudimentary ontology-mediated queries, called disjunctive sirups (or d-sirups), capture many features and difficulties of the general case. For example, answering d-sirups is Π2p-complete for combined complexity and can be in [Formula presented] or L-, NL-, P-, or CONP-complete for data complexity (with the problem of recognising FO-rewritability of d-sirups being 2EXPTIME-hard); some d-sirups only have exponential-size resolution proofs, some only double-exponential-size positive existential FO-rewritings and single-exponential-size nonrecursive datalog rewritings. On the other hand, we prove a few partial sufficient and necessary conditions of FO- and (symmetric/linear-) datalog rewritability of d-sirups. Our main technical result is a complete and transparent syntactic [Formula presented]/NL/P/CONP tetrachotomy of d-sirups with disjoint covering classes and a path-shaped Boolean conjunctive query. To obtain this tetrachotomy, we develop new techniques for establishing P- and CONP-hardness of answering non-Horn ontology-mediated queries as well as showing that they can be answered in NL.

Original languageEnglish
Article number103738
JournalARTIFICIAL INTELLIGENCE
Volume309
Issue number103738
DOIs
Publication statusPublished - 1 Aug 2022

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