论文标题
流星：与公制临时操作员数据的实用推理
MeTeoR: Practical Reasoning in Datalog with Metric Temporal Operators
论文作者
论文摘要
DatalogMTL是数据量的扩展，该数据量与公制时间逻辑的运营商近年来受到了极大的关注。这是一种高度表达的知识表示语言，非常适合基于时间本体的查询答案和流处理的应用。但是，DatalOgMTL中的推理具有很高的计算复杂性，这使得实施具有挑战性并阻碍其在应用程序中的采用。在本文中，我们提出了一种在DatalOgMTL中进行实践推理的新方法，该方法将物质化（又称向前链接）与基于自动机技术结合在一起。我们已经在称为Meteor的推理者中实现了这种方法，并使用Lehigh University基准的时间扩展和基于现实世界的气象数据的基准进行了时间扩展。我们的实验表明，流星是一个可扩展的系统，可以通过涉及数千万个时间事实的复杂时间规则和数据集进行推理。
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.
