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First-order rewritability and complexity of two-dimensional temporal ontology-mediated queries. (English) Zbl 07639820

Summary: Aiming at ontology-based data access to temporal data, we design two-dimensional temporal ontology and query languages by combining logics from the (extended) DL-Lite family with linear temporal logic LTL over discrete time \((\mathbb{Z},<)\). Our main concern is first-order rewritability of ontology-mediated queries (OMQs) that consist of a 2D ontology and a positive temporal instance query. Our target languages for FO-rewritings are two-sorted FO(\(<\)) – first-order logic with sorts for time instants ordered by the built-in precedence relation \(<\) and for the domain of individuals – its extension FO(\(<, \equiv)\) with the standard congruence predicates \(t \equiv 0\) (mod \(n\)), for any fixed \(n > 1\), and FO(RPR) that admits relational primitive recursion. In terms of circuit complexity, FO(\(<, \equiv)\)- and FO(RPR)-rewritability guarantee answering OMQs in uniform AC\(^0\) and NC\(^1\), respectively.
We proceed in three steps. First, we define a hierarchy of 2D DL-Lite/LTL ontology languages and investigate the FO-rewritability of OMQs with atomic queries by constructing projections onto 1D LTL OMQs and employing recent results on the FO-rewritability of propositional LTL OMQs. As the projections involve deciding consistency of ontologies and data, we also consider the consistency problem for our languages. While the undecidability of consistency for 2D ontology languages with expressive Boolean role inclusions might be expected, we also show that, rather surprisingly, the restriction to Krom and Horn role inclusions leads to decidability (and ExpSpace-completeness), even if one admits full Booleans on concepts. As a final step, we lift some of the rewritability results for atomic OMQs to OMQs with expressive positive temporal instance queries. The lifting results are based on an in-depth study of the canonical models and only concern Horn ontologies.

MSC:

68T27 Logic in artificial intelligence
68T30 Knowledge representation

Software:

DatalogMTL

References:

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