A knowledge graph (KG) represents real-world entities and their relationships. The represented knowledge is often context-dependent, leading to the construction of contextualized KGs. The multidimensional and hierarchical nature of context invites comparison with the OLAP cube model from multidimensional data analysis. Traditional systems for online analytical processing (OLAP) employ multidimensional models to represent numeric values for further analysis using dedicated query operations. In this paper, along with an adaptation of the OLAP cube model for KGs, we introduce an adaptation of the traditional OLAP query operations for the purposes of performing analysis over KGs. In particular, we decompose the roll-up operation from traditional OLAP into a merge and an abstraction operation. The merge operation corresponds to the selection of knowledge from different contexts whereas abstraction replaces entities with more general entities. The result of such a query is a more abstract, high-level view – a management summary – of the knowledge.

中文翻译：

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知识图OLAP

知识图（KG）代表现实世界中的实体及其关系。所代表的知识通常是上下文相关的，从而导致上下文化的KG的构建。上下文的多维性和层次结构性质要求通过多维数据分析与OLAP多维数据集模型进行比较。传统的在线分析处理（OLAP）系统使用多维模型来表示数值，以便使用专用查询操作进行进一步分析。在本文中，除了针对KG的OLAP多维数据集模型的改编外，我们还介绍了对传统OLAP查询操作的改编，目的是对KG进行分析。特别是，我们将汇总操作从传统的OLAP分解为合并和抽象操作。合并操作对应于从不同上下文中选择知识，而抽象将实体替换为更通用的实体。这样的查询结果是对知识的更抽象的高级视图（管理摘要）。