MeTeoR: Practical Reasoning in Datalog with Metric Temporal Operators
This addresses a practical bottleneck for applications in temporal ontology-based query answering and stream processing, representing an incremental improvement.
The paper tackles the high computational complexity of reasoning in DatalogMTL, a temporal extension of Datalog, by introducing MeTeoR, a scalable reasoner that combines materialisation with automata-based techniques, enabling reasoning over tens of millions of temporal facts.
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.