<div class="csl-bib-body">
<div class="csl-entry">Lanzinger, M., Nissl, M., Sallinger, E., & Wałęga, P. (2023). Temporal Datalog with Existential Quantification. In E. Elkind (Ed.), <i>Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI 2023)</i> (pp. 3277–3285). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2023/365</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/192202
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dc.description.abstract
Existential rules, also known as tuple-generating dependencies (TGDs) or Datalog+/- rules, are heavily studied in the communities of Knowledge Representation and Reasoning, Semantic Web, and Databases, due to their rich modelling capabilities. In this paper we consider TGDs in the temporal setting, by introducing and studying DatalogMTLE---an extension of metric temporal Datalog (DatalogMTL) obtained by allowing for existential rules in programs. We show that DatalogMTLE is undecidable even in the restricted cases of guarded and weakly-acyclic programs. To address this issue we introduce uniform semantics which, on the one hand, is well-suited for modelling temporal knowledge as it prevents from unintended value invention and, on the other hand, provides decidability of reasoning; in particular, it becomes 2-EXPSPACE-complete for weakly-acyclic programs but remains undecidable for guarded programs. We provide an implementation for the decidable case and demonstrate its practical feasibility. Thus we obtain an expressive, yet decidable, rule-language and a system which is suitable for complex temporal reasoning with existential rules.
en
dc.language.iso
en
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dc.relation.ispartofseries
IJCAI
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dc.subject
Temporal Datalog
en
dc.subject
Knowledge Representation
en
dc.subject
Logic Programming
en
dc.title
Temporal Datalog with Existential Quantification
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.relation.publication
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI 2023)
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dc.contributor.affiliation
University of Oxford, United Kingdom of Great Britain and Northern Ireland (the)
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dc.relation.isbn
978-1-956792-03-4
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dc.relation.issn
1045-0823
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dc.description.startpage
3277
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dc.description.endpage
3285
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI-23)
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tuw.book.ispartofseries
IJCAI
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tuw.relation.publisher
International Joint Conferences on Artificial Intelligence
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tuw.researchTopic.id
I1
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tuw.researchTopic.name
Logic and Computation
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tuw.researchTopic.value
100
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tuw.publication.orgunit
E192-02 - Forschungsbereich Databases and Artificial Intelligence
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tuw.publisher.doi
10.24963/ijcai.2023/365
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dc.description.numberOfPages
9
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tuw.author.orcid
0000-0002-7601-3727
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tuw.author.orcid
0000-0003-2922-0472
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tuw.event.name
32nd International Joint Conference on Artificial Intelligence (IJCAI 2023)
en
tuw.event.startdate
19-08-2023
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tuw.event.enddate
25-08-2023
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tuw.event.online
On Site
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tuw.event.type
Event for scientific audience
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tuw.event.place
Macao
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tuw.event.country
CN
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tuw.event.presenter
Lanzinger, Matthias
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wb.sciencebranch
Informatik
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wb.sciencebranch
Mathematik
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wb.sciencebranch.oefos
1020
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wb.sciencebranch.oefos
1010
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wb.sciencebranch.value
80
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wb.sciencebranch.value
20
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item.languageiso639-1
en
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item.openairetype
conference paper
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item.grantfulltext
restricted
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item.fulltext
no Fulltext
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item.cerifentitytype
Publications
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item.openairecristype
http://purl.org/coar/resource_type/c_5794
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crisitem.author.dept
E192-02 - Forschungsbereich Databases and Artificial Intelligence
-
crisitem.author.dept
E192-02 - Forschungsbereich Databases and Artificial Intelligence
-
crisitem.author.dept
E192-02 - Forschungsbereich Databases and Artificial Intelligence