MeTeoR: Practical Reasoning in Datalog with Metric Temporal Operators

• URL: http://arxiv.org/abs/2201.04596v1
• Date: Wed, 12 Jan 2022 17:46:18 GMT
• Title: MeTeoR: Practical Reasoning in Datalog with Metric Temporal Operators
• Authors: Dingmin Wang, Pan Hu, Przemys{\l}aw Andrzej Wa{\l}\k{e}ga, Bernardo Cuenca Grau
• Abstract summary: We present a novel approach for practical reasoning in DatalogMTL which combines materialisation (a.k.a. forward chaining) with automata-based techniques. MeTeoR is a scalable system which enables reasoning over complex temporal rules and involving datasets of millions of temporal facts.
• Score: 12.145849273069627
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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