DatalogMTL is a powerful rule-based language for temporal reasoning. Due to its high expressive power and flexible modeling capabilities, it is suitable for a wide range of applications, including tasks from industrial and financial sectors. However, due to its high computational complexity, practical reasoning in DatalogMTL is highly challenging. To address this difficulty, we introduce a new reasoning method for DatalogMTL which exploits the magic sets technique -- a rewriting approach developed for (non-temporal) Datalog to simulate top-down evaluation with bottom-up reasoning. We have implemented this approach and evaluated it on publicly available benchmarks, showing that the proposed approach significantly and consistently outperformed state-of-the-art reasoning techniques.
@article{arxiv.2412.07259,
title = {Goal-Driven Reasoning in DatalogMTL with Magic Sets},
author = {Shaoyu Wang and Kaiyue Zhao and Dongliang Wei and Przemysław Andrzej Wałęga and Dingmin Wang and Hongming Cai and Pan Hu},
journal= {arXiv preprint arXiv:2412.07259},
year = {2025}
}