Recent years have seen increasing popularity of logic-based reasoning systems, with research and industrial interest as well as many flourishing applications in the area of Knowledge Graphs. Despite that, one can observe a substantial lack of specific tools able to generate nontrivial reasoning settings and benchmark scenarios. As a consequence, evaluating, analysing and comparing reasoning systems is a complex task, especially when they embody sophisticated optimizations and execution techniques that leverage the theoretical underpinnings of the adopted logic fragment. In this paper, we aim at filling this gap by introducing iWarded, a system that can generate very large, complex, realistic reasoning settings to be used for the benchmarking of logic-based reasoning systems adopting Datalog+/-, a family of extensions of Datalog that has seen a resurgence in the last few years. In particular, iWarded generates reasoning settings for Warded Datalog+/-, a language with a very good tradeoff between computational complexity and expressive power. In the paper, we present the iWarded system and a set of novel theoretical results adopted to generate effective scenarios. As Datalog-based languages are of general interest and see increasing adoption, we believe that iWarded is a step forward in the empirical evaluation of current and future systems.
翻译:近些年来,基于逻辑的推理系统越来越受欢迎,其研究和产业利益以及知识图表领域的许多繁忙应用日益受到重视。尽管如此,人们仍可以看到,大量缺乏能够产生非三重推理设置和基准情景的具体工具,因此,评估、分析和比较推理系统是一项复杂的任务,特别是当这些系统体现了利用所采纳逻辑碎片理论基础的精密优化和执行技术时。在本文件中,我们的目标是通过引入iWarded来填补这一空白。 iWarded能够产生非常庞大、复杂和现实的推理环境,用于为采用Datalog+/-的逻辑推理系统制定基准。Datalog的扩展系列在过去几年中出现复苏。特别是, iWarded为dd Datalog+/ - 创造了推理环境,这是一种在计算复杂度和表达力之间有着良好权衡作用的语言。在本文中,我们介绍了iWardd系统和为产生有效情景而采用的一套新理论结果。由于基于数据图表的语言具有普遍兴趣,而且日益得到采用,我们认为,因此IWARd是未来系统的一个步骤。