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Counting query answers over a DL-Lite knowledge base
Umeå University, Faculty of Science and Technology, Department of Computing Science. Free University of Bozen-Bolzano, Italy.ORCID iD: 0000-0001-5174-9693
Free University of Bozen-Bolzano, Italy.
Free University of Bozen-Bolzano, Italy.
Max-Planck-Institut für Informatik, Germany.
2021 (English)In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence: Yokohama / [ed] Christian Bessiere, International Joint Conferences on Artificial Intelligence , 2021, p. 1658-1666Conference paper, Published paper (Refereed)
Abstract [en]

Counting answers to a query is an operation supported by virtually all database management systems. In this paper we focus on counting answers over a Knowledge Base (KB), which may be viewed as a database enriched with background knowledge about the domain under consideration. In particular, we place our work in the context of Ontology-Mediated Query Answering/Ontology-based Data Access (OMQA/OBDA), where the language used for the ontology is a member of the DL-Lite family and the data is a (usually virtual) set of assertions. We study the data complexity of query answering, for different members of the DL-Lite family that include number restrictions, and for variants of conjunctive queries with counting that differ with respect to their shape (connected, branching, rooted). We improve upon existing results by providing P and coNP lower bounds, and upper bounds in P and LogSpace. For the LogSpace case, we have devised a novel query rewriting technique into first-order logic with counting.

Place, publisher, year, edition, pages
International Joint Conferences on Artificial Intelligence , 2021. p. 1658-1666
Series
Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), ISSN 1045-0823
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:umu:diva-197972Scopus ID: 2-s2.0-85091463722ISBN: 9780999241165 (electronic)OAI: oai:DiVA.org:umu-197972DiVA, id: diva2:1682795
Conference
IJCAI 2020, 29th International Joint Conference on Artificial Intelligence, Yokohama, Japan, January 7-15, 2021
Available from: 2022-07-12 Created: 2022-07-12 Last updated: 2022-07-12Bibliographically approved

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Calvanese, Diego

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • de-DE
  • en-GB
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Output format
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