Unique Characterisability and Learnability of Temporal Instance Queries Article Swipe
Marie Fortin
,
Boris Konev
,
Vladislav Ryzhikov
,
Yury Savateev
,
Frank Wolter
,
Michael Zakharyaschev
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.24963/kr.2022/17
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.24963/kr.2022/17
We aim to determine which temporal instance queries can be uniquely characterised by a (polynomial-size) set of positive and negative temporal data examples. We start by considering queries formulated in fragments of propositional linear temporal logic LTL that correspond to conjunctive queries (CQs) or extensions thereof induced by the until operator. Not all of these queries admit polynomial characterisations, but by imposing a further restriction to path-shaped queries we identify natural classes that do. We then investigate how far the obtained characterisations can be lifted to temporal knowledge graphs queried by 2D languages combining LTL with concepts in description logics EL or ELI (i.e., tree-shaped CQs). While temporal operators in the scope of description logic constructors can destroy polynomial characterisability, we obtain general transfer results for the case when description logic constructors are within the scope of temporal operators. Finally, we apply our characterisations to establish (polynomial) learnability of temporal instance queries using membership queries in the active learning framework.
Related Topics
Concepts
Learnability
Temporal logic
Theoretical computer science
Computer science
Description logic
Linear temporal logic
Scope (computer science)
Computation tree logic
Path (computing)
Operator (biology)
Tree (set theory)
Time complexity
Set (abstract data type)
Propositional calculus
Polynomial
Mathematics
Artificial intelligence
Algorithm
Programming language
Combinatorics
Gene
Repressor
Chemistry
Mathematical analysis
Transcription factor
Biochemistry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.24963/kr.2022/17
- https://proceedings.kr.org/2022/17/kr2022-0017-fortin-et-al.pdf
- OA Status
- gold
- Cited By
- 6
- References
- 51
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4288046561
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4288046561Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.24963/kr.2022/17Digital Object Identifier
- Title
-
Unique Characterisability and Learnability of Temporal Instance QueriesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-07-01Full publication date if available
- Authors
-
Marie Fortin, Boris Konev, Vladislav Ryzhikov, Yury Savateev, Frank Wolter, Michael ZakharyaschevList of authors in order
- Landing page
-
https://doi.org/10.24963/kr.2022/17Publisher landing page
- PDF URL
-
https://proceedings.kr.org/2022/17/kr2022-0017-fortin-et-al.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://proceedings.kr.org/2022/17/kr2022-0017-fortin-et-al.pdfDirect OA link when available
- Concepts
-
Learnability, Temporal logic, Theoretical computer science, Computer science, Description logic, Linear temporal logic, Scope (computer science), Computation tree logic, Path (computing), Operator (biology), Tree (set theory), Time complexity, Set (abstract data type), Propositional calculus, Polynomial, Mathematics, Artificial intelligence, Algorithm, Programming language, Combinatorics, Gene, Repressor, Chemistry, Mathematical analysis, Transcription factor, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 4, 2023: 1, 2022: 1Per-year citation counts (last 5 years)
- References (count)
-
51Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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