Evolving phenomena, often complex, can be represented using knowledge graphs, which have the capability to model heterogeneous data from multiple sources. Nowadays, a considerable amount of sources delivering periodic updates to knowledge graphs in various domains is openly available. The evolution of data is of interest to knowledge graph management systems, and therefore it is crucial to organize these constantly evolving data to make them easily accessible and exploitable for analysis. In this article, we will present and formalize the condensed representation of these evolving graphs and propose a new solution called QuaQue that allows querying across multiple versions of graphs and we also present the results of our benchmark comparing our solution against existing approaches.

翻译：演化现象通常具有复杂性，可借助知识图谱进行表示，此类图谱具备建模多来源异构数据的能力。当前，各领域中提供知识图谱周期性更新的数据源已大量公开。数据的演化过程对知识图谱管理系统具有重要意义，因此如何有效组织这些持续演变的数据，使其易于访问并适用于分析至关重要。本文将系统阐述并正式定义此类演化图的压缩表示方法，提出名为QuaQue的新方案，该方案支持跨多版本图数据的高效查询。同时，我们将呈现基准测试结果，通过对比现有方案验证所提出方法的性能优势。