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Temporal Minimal-World Query Answering over Sparse ABoxes

Published online by Cambridge University Press:  11 August 2021

STEFAN BORGWARDT
Affiliation:
Chair for Automata Theory, Technische Universität Dresden, Germany (e-mails: [email protected], [email protected], [email protected])
WALTER FORKEL
Affiliation:
Chair for Automata Theory, Technische Universität Dresden, Germany (e-mails: [email protected], [email protected], [email protected])
ALISA KOVTUNOVA
Affiliation:
Chair for Automata Theory, Technische Universität Dresden, Germany (e-mails: [email protected], [email protected], [email protected])
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Abstract

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Ontology-mediated query answering is a popular paradigm for enriching answers to user queries with background knowledge. For querying the absence of information, however, there exist only few ontology-based approaches. Moreover, these proposals conflate the closed-domain and closed-world assumption and, therefore, are not suited to deal with the anonymous objects that are common in ontological reasoning. Many real-world applications, like processing electronic health records, also contain a temporal dimension and require efficient reasoning algorithms. Moreover, since medical data are not recorded on a regular basis, reasoners must deal with sparse data with potentially large temporal gaps. Our contribution consists of two main parts: In the first part, we introduce a new closed-world semantics for answering conjunctive queries (CQs) with negation over ontologies formulated in the description logic $${\mathcal E}{\mathcal L}{{\mathcal H}_ \bot }$$ , which is based on the minimal canonical model. We propose a rewriting strategy for dealing with negated query atoms, which shows that query answering is possible in polynomial time in data complexity. In the second part, we extend this minimal-world semantics for answering metric temporal CQs with negation over the lightweight temporal logic and obtain similar rewritability and complexity results.

Type
Original Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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