Unique Characterisability and Learnability of Temporal Queries Mediated by an Ontology
Artificial Intelligence
2024-07-30 v3 Databases
Logic in Computer Science
Abstract
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.
Keywords
Cite
@article{arxiv.2306.07662,
title = {Unique Characterisability and Learnability of Temporal Queries Mediated by an Ontology},
author = {Jean Christoph Jung and Vladislav Ryzhikov and Frank Wolter and Michael Zakharyaschev},
journal= {arXiv preprint arXiv:2306.07662},
year = {2024}
}
Comments
Full version of a KR'2024 paper