Checking chase termination over ontologies of existential rules with equality

David Carral, Jacopo Urbani

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The chase is a sound and complete algorithm for conjunctive query answering over ontologies of existential rules with equality. To enable its effective use, we can apply acyclicity notions; that is, sufficient conditions that guarantee chase termination. Unfortunately, most of these notions have only been defined for existential rule sets without equality. A proposed solution to circumvent this issue is to treat equality as an ordinary predicate with an explicit axiomatisation. We empirically show that this solution is not efficient in practice and propose an alternative approach. More precisely, we show that, if the chase terminates for any equality axiomatisation of an ontology, then it terminates for the original ontology (which may contain equality). Therefore, one can apply existing acyclicity notions to check chase termination over an axiomatisation of an ontology and then use the original ontology for reasoning. We show that, in practice, doing so results in a more efficient reasoning procedure. Furthermore, we present equality model-faithful acyclicity, a general acyclicity notion that can be directly applied to ontologies with equality.

Original languageEnglish
Title of host publicationThe Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20)
Subtitle of host publication[Proceedings]
PublisherAAAI Press
Number of pages8
ISBN (Electronic)9781577358350
Publication statusPublished - 2020
Event34th AAAI Conference on Artificial Intelligence, AAAI 2020 - New York, United States
Duration: 7 Feb 202012 Feb 2020


Conference34th AAAI Conference on Artificial Intelligence, AAAI 2020
Country/TerritoryUnited States
CityNew York

Bibliographical note

Vol. 34 No. 03: AAAI-20 Technical Tracks 3.

Funding Information:
Acknowledgments This work is funded by Deutsche Forschungsgemeinschaft (DFG) grant 389792660 as part of TRR 248 (see and by the NWO research programme 400.17.605 (VWData). We also thank Irina Dragoste for her useful comments.

Publisher Copyright:
Copyright © 2020, Association for the Advancement of Artificial Intelligence ( All rights reserved.

Copyright 2021 Elsevier B.V., All rights reserved.


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