Answering temporal CQs over temporalized Description Logic knowledge bases (TKB) is a main technique to realize ontology-based situation recognition. In case the collected data in such a knowledge base is inaccurate, important query answers can be missed. In this paper we introduce the TKB Alignment problem, which computes a variant of the TKB that minimally changes the TKB, but entails the given temporal CQ and is in that sense (cost-)optimal. We investigate this problem for ALC TKBs and conjunctive queries with LTL operators and devise a solution technique to compute (cost-optimal) alignments of TKBs that extends techniques for the alignment problem for propositional LTL over finite traces.

Optimal Alignment of Temporal Knowledge Bases / Fernandez-Gil, Oliver; Patrizi, Fabio; Perelli, Giuseppe; Turhan, Anni-Yasmin. - 372:(2023), pp. 708-715. (Intervento presentato al convegno European Conference on Artificial Intelligence tenutosi a Cracovia; Poland) [10.3233/FAIA230335].

Optimal Alignment of Temporal Knowledge Bases

Fabio Patrizi
;
Giuseppe Perelli
;
2023

Abstract

Answering temporal CQs over temporalized Description Logic knowledge bases (TKB) is a main technique to realize ontology-based situation recognition. In case the collected data in such a knowledge base is inaccurate, important query answers can be missed. In this paper we introduce the TKB Alignment problem, which computes a variant of the TKB that minimally changes the TKB, but entails the given temporal CQ and is in that sense (cost-)optimal. We investigate this problem for ALC TKBs and conjunctive queries with LTL operators and devise a solution technique to compute (cost-optimal) alignments of TKBs that extends techniques for the alignment problem for propositional LTL over finite traces.
2023
European Conference on Artificial Intelligence
Description Logic; Linear Temporal Logic; Knowledge Bases; Temporal Reasoning;
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Optimal Alignment of Temporal Knowledge Bases / Fernandez-Gil, Oliver; Patrizi, Fabio; Perelli, Giuseppe; Turhan, Anni-Yasmin. - 372:(2023), pp. 708-715. (Intervento presentato al convegno European Conference on Artificial Intelligence tenutosi a Cracovia; Poland) [10.3233/FAIA230335].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1691214
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