Unique Characterisability and Learnability of Temporal Queries Mediated by an Ontology
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866909271108616192 |
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| author | Jung, Jean Christoph Ryzhikov, Vladislav Wolter, Frank Zakharyaschev, Michael |
| author_facet | Jung, Jean Christoph Ryzhikov, Vladislav Wolter, Frank Zakharyaschev, Michael |
| contents | Algorithms for learning database queries from examples and unique characterisations of queries by examples are prominent starting points for developing automated support for query construction and explanation. We investigate how far recent results and techniques on learning and unique characterisations of atemporal queries mediated by an ontology can be extended to temporal data and queries. Based on a systematic review of the relevant approaches in the atemporal case, we obtain general transfer results identifying conditions under which temporal queries composed of atemporal ones are (polynomially) learnable and uniquely characterisable. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_07662 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Unique Characterisability and Learnability of Temporal Queries Mediated by an Ontology Jung, Jean Christoph Ryzhikov, Vladislav Wolter, Frank Zakharyaschev, Michael Artificial Intelligence Databases Logic in Computer Science I.2.4; F.4.1 Algorithms for learning database queries from examples and unique characterisations of queries by examples are prominent starting points for developing automated support for query construction and explanation. We investigate how far recent results and techniques on learning and unique characterisations of atemporal queries mediated by an ontology can be extended to temporal data and queries. Based on a systematic review of the relevant approaches in the atemporal case, we obtain general transfer results identifying conditions under which temporal queries composed of atemporal ones are (polynomially) learnable and uniquely characterisable. |
| title | Unique Characterisability and Learnability of Temporal Queries Mediated by an Ontology |
| topic | Artificial Intelligence Databases Logic in Computer Science I.2.4; F.4.1 |
| url | https://arxiv.org/abs/2306.07662 |