  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.
