Year: 2024
Type: conference-paper
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 explanation and construction. 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.
DOI: 10.24963/kr.2024/46
Language: English
Topic: Semantic Web and Ontologies
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4403792995