In today’s landscape of data management, the importance of knowledge graphs and ontologies is escalating as critical mechanisms aligned with the FAIR Guiding Principles—ensuring data and metadata are Findable, Accessible, Interoperable, and Reusable. We discuss three challenges that may hinder the effective exploitation of the full potential of FAIR knowledge graphs. We introduce “semantic units” as a conceptual solution, although currently exemplified only in a limited prototype. Semantic units structure a knowledge graph into identifiable and semantically meaningful subgraphs by adding another layer of triples on top of the conventional data layer. Semantic units and their subgraphs are represented by their own resource that instantiates a corresponding semantic unit class. We distinguish statement and compound units as basic categories of semantic units. A statement unit is the smallest, independent proposition that is semantically meaningful for a human reader. Depending on the relation of its underlying proposition, it consists of one or more triples. Organizing a knowledge graph into statement units results in a partition of the graph, with each triple belonging to exactly one statement unit. A compound unit, on the other hand, is a semantically meaningful collection of statement and compound units that form larger subgraphs. Some semantic units organize the graph into different levels of representational granularity, others orthogonally into different types of granularity trees or different frames of reference, structuring and organizing the knowledge graph into partially overlapping, partially enclosed subgraphs, each of which can be referenced by its own resource. Semantic units, applicable in RDF/OWL and labeled property graphs, offer support for making statements about statements and facilitate graph-alignment, subgraph-matching, knowledge graph profiling, and for management of access restrictions to sensitive data. Additionally, we argue that organizing the graph into semantic units promotes the differentiation of ontological and discursive information, and that it also supports the differentiation of multiple frames of reference within the graph.

中文翻译：

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语义单元：将知识图组织成语义上有意义的表示单元


在当今的数据管理领域，知识图和本体的重要性正在不断提升，因为关键机制与公平指导原则保持一致——确保数据和元数据可查找、可访问、可互操作和可重用。我们讨论了可能阻碍有效利用 FAIR 知识图的全部潜力的三个挑战。我们引入“语义单元”作为概念解决方案，尽管目前仅在有限的原型中进行举例说明。语义单元通过在传统数据层之上添加另一层三元组，将知识图构建为可识别且语义上有意义的子图。语义单元及其子图由它们自己的资源表示，该资源实例化相应的语义单元类。我们将陈述单元和复合单元区分为语义单元的基本类别。语句单元是对人类读者来说具有语义意义的最小的独立命题。根据其基本命题的关系，它由一个或多个三元组组成。将知识图组织为语句单元会导致图的分区，每个三元组恰好属于一个语句单元。另一方面，复合单元是形成更大子图的语句和复合单元的语义上有意义的集合。一些语义单元将图组织成不同级别的表示粒度，其他语义单元将图组织成不同类型的粒度树或不同的参考框架，将知识图结构化和组织成部分重叠、部分封闭的子图，每个子图都可以被自己的引用资源。 语义单元适用于 RDF/OWL 和标记属性图，为有关语句的陈述提供支持，并促进图对齐、子图匹配、知识图分析以及敏感数据访问限制的管理。此外，我们认为将图组织成语义单元可以促进本体和话语信息的区分，并且它还支持图中多个参考框架的区分。