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.

翻译：从示例中学习数据库查询的算法以及通过示例对查询的唯一表征，是开发查询构建与解释自动化支持的重要起点。我们研究了近期关于通过本体中介的非时间查询的可学习性与唯一可表征性的成果与方法，在多大程度上可扩展至时间数据与时间查询。基于对非时间情形下相关方法的系统性梳理，我们获得了通用的迁移结果，识别出由非时间查询组成的时间查询何时（以多项式复杂度）可学习且可唯一表征的条件。