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：演化现象通常较为复杂，可通过知识图谱进行表示，其具备对多源异构数据进行建模的能力。当前，大量公开可用的数据源持续为各领域知识图谱提供周期性更新。数据演化是知识图谱管理系统关注的核心问题，因此如何有效组织这些持续演化的数据，使其易于访问并可用于分析至关重要。本文提出并形式化定义了演化图谱的压缩表示方法，同时提出名为QuaQue的新解决方案，支持跨多版本图谱的查询操作。此外，我们还通过基准测试将本方案与现有方法进行了对比，并展示了实验结果。