MeTeoR: Practical Reasoning in Datalog with Metric Temporal Operators


  • Dingmin Wang University of Oxford
  • Pan Hu University of Oxford Shanghai Jiao Tong University, China
  • Przemysław Andrzej Wałęga University of Oxford
  • Bernardo Cuenca Grau University of Oxford



Knowledge Representation And Reasoning (KRR)


DatalogMTL is an extension of Datalog with operators from metric temporal logic which has received significant attention in recent years. It is a highly expressive knowledge representation language that is well-suited for applications in temporal ontology-based query answering and stream processing. Reasoning in DatalogMTL is, however, of high computational complexity, making implementation challenging and hindering its adoption in applications. In this paper, we present a novel approach for practical reasoning in DatalogMTL which combines materialisation (a.k.a. forward chaining) with automata-based techniques. We have implemented this approach in a reasoner called MeTeoR and evaluated its performance using a temporal extension of the Lehigh University Benchmark and a benchmark based on real-world meteorological data. Our experiments show that MeTeoR is a scalable system which enables reasoning over complex temporal rules and datasets involving tens of millions of temporal facts.




How to Cite

Wang, D., Hu, P., Wałęga, P. A., & Grau, B. C. (2022). MeTeoR: Practical Reasoning in Datalog with Metric Temporal Operators. Proceedings of the AAAI Conference on Artificial Intelligence, 36(5), 5906-5913.



AAAI Technical Track on Knowledge Representation and Reasoning