Recent years witnessed a rising interest towards Datalog-based ontological reasoning systems, both in academia and industry. These systems adopt languages, often shared under the collective name of Datalog$+/-$, that extend Datalog with the essential feature of existential quantification, while introducing syntactic limitations to sustain reasoning decidability and achieve a good trade-off between expressive power and computational complexity. From an implementation perspective, modern reasoners borrow the vast experience of the database community in developing streaming-based data processing systems, such as volcano-iterator architectures, that sustain a limited memory footprint and good scalability. In this paper, we focus on two extremely promising, expressive, and tractable languages, namely, Shy and Warded Datalog$+/-$. We leverage their theoretical underpinnings to introduce novel reasoning techniques, technically, "chase variants", that are particularly fit for efficient reasoning in streaming-based architectures. We then implement them in Vadalog, our reference streaming-based engine, to efficiently solve ontological reasoning tasks over real-world settings.

翻译：近年来，学术界和工业界对基于Datalog的本体推理系统兴趣日益增长。这类系统采用的语言通常统称为Datalog$+/-$，在扩展Datalog引入存在量词这一核心特性的同时，通过施加语法限制来维持推理可判定性，并在表达能力与计算复杂度之间实现良好平衡。从实现角度来看，现代推理器借鉴了数据库社区在开发流式数据处理系统（如火山迭代器架构）方面的丰富经验，这些系统具有内存占用小、可扩展性强的特点。本文聚焦两种极具前景、表达能力强且易于处理的语言——Shy与Warded Datalog$+/-$，利用其理论基础引入新型推理技术（技术上称为"chase变体"），这些技术特别适合在流式架构中进行高效推理。我们随后在参考流式引擎Vadalog中实现这些技术，以高效解决真实场景中的本体推理任务。