ML Anthology

Goal-Driven Reasoning in DatalogMTL with Magic Sets

Shaoyu Wang, Kaiyue Zhao, Dongliang Wei, Przemyslaw Andrzej Walega, Dingmin Wang, Hongming Cai, Pan Hu
AAAI 2025 pp. 15203-15211
doi:10.1609/AAAI.V39I14.33668 /aaai/2025/wang2025aaai-goal/

Abstract

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.

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Cite

Text

Wang et al. "Goal-Driven Reasoning in DatalogMTL with Magic Sets." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I14.33668

Markdown

[Wang et al. "Goal-Driven Reasoning in DatalogMTL with Magic Sets." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/wang2025aaai-goal/) doi:10.1609/AAAI.V39I14.33668

BibTeX

@inproceedings{wang2025aaai-goal,
  title     = {{Goal-Driven Reasoning in DatalogMTL with Magic Sets}},
  author    = {Wang, Shaoyu and Zhao, Kaiyue and Wei, Dongliang and Walega, Przemyslaw Andrzej and Wang, Dingmin and Cai, Hongming and Hu, Pan},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2025},
  pages     = {15203-15211},
  doi       = {10.1609/AAAI.V39I14.33668},
  url       = {https://mlanthology.org/aaai/2025/wang2025aaai-goal/}
}