Goal-Driven Reasoning in DatalogMTL with Magic Sets

TL;DR

This paper introduces a new reasoning method for DatalogMTL using magic sets, significantly improving performance on benchmarks by combining temporal reasoning with efficient rewriting techniques.

Contribution

It adapts the magic sets rewriting approach to DatalogMTL, enabling more efficient reasoning in high-complexity temporal logic systems.

Findings

Significant performance improvements over existing methods

Effective handling of complex temporal reasoning tasks

Validated on publicly available benchmarks

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 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.