The introduction of novel Datalog +/- fragments with good theoretical properties, together with the growing use of enterprise knowledge graphs motivated the development of Vadalog, a knowledge graph management system developed at the University of Oxford. It adopts Warded Datalog +/- as the core of its language for knowledge representation and reasoning, which exhibits a very good tradeoff between computational complexity of reasoning and expressive power, capturing PTIME data complexity while allowing ontological reasoning and full recursion. In this paper, we provide a detailed illustration of the Vadalog system, presenting: the essentials of the first implementation of Warded Datalog +/-; a comprehensive overview of the architecture with specific focus on runtime execution model, memory management, graph traversal strategies and join algorithms; and a detailed experimental evaluation.

This paper is a substantially expanded version of the AMW 2019 paper “Datalog-based reasoning for Knowledge Graphs”. To stand apart from previous works on the topic, our focus in this work shall be a comprehensive presentation of the architecture of the Vadalog system and showing how our techniques work together to provide a full-fledged KGMS. In particular, roughly half of this paper is new material created particularly for this comprehensive architectural view. This includes a new series of experiments designed to shed light on architectural choices and alternatives.

具有良好理论特性的新型Datalog +/-片段的引入，以及企业知识图的日益广泛使用，促使了Vadalog的发展，Vadalog是牛津大学开发的知识图管理系统。它采用Warded Datalog +/-作为其语言的核心，用于知识表示和推理，在推理的计算复杂度和表达能力之间展现出很好的折衷，既可以捕获PTIME数据复杂性，又可以进行本体论推理和完全递归。在本文中，我们提供了Vadalog系统的详细说明，并提出了：Warded Datalog +/-的第一个实现的要点；全面的体系结构概述，特别关注运行时执行模型，内存管理，图形遍历策略和联接算法；

本文是AMW 2019年论文``知识图的基于数据记录的推理''的实质性扩展版本。为了与以前在该主题上的作品脱颖而出，我们在这项工作中的重点应该是对Vadalog系统的体系结构的全面介绍，并展示我们的技术如何协同工作以提供完整的KGMS。特别是，本文的大约一半是专为这种全面的架构视图而创建的新材料。这包括一系列旨在阐明架构选择和替代方案的新实验。