Efficient Temporal Reasoning with Non-Temporal Engines: Embedding DatalogMTL into Datalog

We present a translation from DatalogMTL, a temporal extension of Datalog rule language with operators from metric temporal logic, into non-temporal Datalog with arithmetics. As we prove, the translation preserves key semantic properties such as entailment, consistency, and finiteness of materialisability. As a result, we obtain a faithful embedding of DatalogMTL into classical Datalog, enabling complex temporal reasoning tasks to be executed without the need for specialised temporal reasoning engines. We implement and evaluate this translation using three state-of-the-art Datalog systems: Nemo, EYE, and Eyelet. Remarkably, non-temporal Datalog engines are able to achieve comperable performance, and in some cases outperform dedicated temporal reasoning engines, demonstrating the practical viability and efficiency of the translation.