Unique Characterisability and Learnability of Temporal Queries Mediated by an Ontology
This work addresses the challenge of automated query construction and explanation for temporal data in databases, representing an incremental extension of existing atemporal methods.
The paper tackles the problem of extending learnability and unique characterisability results from atemporal queries to temporal ones mediated by an ontology, obtaining general transfer conditions for polynomial learnability and unique characterisability.
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.