Goal-Driven Reasoning in DatalogMTL with Magic Sets

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 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.

Authors

• Shaoyu Wang School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, China
• Kaiyue Zhao School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, China
• Dongliang Wei School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, China
• Przemysław Andrzej Wałęga
• Dingmin Wang Department of Computer Science, University of Oxford, UK
• Hongming Cai School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, China
• Pan Hu School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, China

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