Goal-Driven Reasoning in DatalogMTL with Magic Sets
This addresses efficiency challenges in temporal reasoning for industrial and financial applications, representing an incremental improvement.
The paper tackled the high computational complexity of DatalogMTL, a rule-based language for temporal reasoning, by introducing a new reasoning method based on magic sets, which significantly outperformed state-of-the-art techniques on benchmarks.
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.