Knowledge graphs and ontologies are becoming increasingly vital as they align with the FAIR Guiding Principles (Findable, Accessible, Interoperable, Reusable). We address eleven challenges that may impede the full realization of the potential of FAIR knowledge graphs, as conventional solutions are perceived to be overly complex and lacking in cognitive interoperability. We extend the concept of "semantic units" as a conceptual solution by adding further subcategories. Semantic units structure a knowledge graph into identifiable and semantically meaningful subgraphs, with each subgraph being represented by a resource that instantiates a semantic unit class. We introduce some-instance, most-instances, every-instance, and all-instances resources as new types of representational entities in addition to named-individual, class, and property resources. We combine these new resource types with the concept of semantic units and introduce new subcategories of statement units and semantically meaningful collections of statement units (i.e., compound units) that provide solutions to the eleven challenges. These include, for instance, schemes for modelling assertional, contingent, prototypical, and universal statements, including class axioms, as well as absence statements, negations, and cardinality restrictions. The schemes are alternatives to existing OWL-based modelling schemes, and we provide corresponding representations for them that do not involve blank nodes. With question units we also introduce a way of representing questions in a knowledge graph that can be made readily executable as graph queries. We also provide schemes for directive statements, directive conditional statements, and logical arguments. We argue that semantic units provide a framework that increases the overall expressivity and cognitive interoperability of knowledge graphs compared to conventional OWL-based solutions.

翻译：知识图谱与本体正变得日益重要，因为它们符合FAIR指导原则（可发现、可访问、可互操作、可重用）。我们针对可能阻碍FAIR知识图谱潜力充分发挥的十一项挑战展开研究，因为传统解决方案被认为过于复杂且缺乏认知互操作性。我们通过扩展子类别来发展"语义单元"这一概念性解决方案。语义单元将知识图谱结构化为可识别且具有语义意义的子图，每个子图由实例化语义单元类的资源表示。我们在命名个体、类和属性资源之外，引入了部分实例、多数实例、每个实例和全部实例资源作为新型表示实体。我们将这些新型资源类型与语义单元概念相结合，提出了陈述单元及具有语义意义的陈述单元集合（即复合单元）的新子类别，为十一项挑战提供了解决方案。这些方案包括：用于建模断言性、偶然性、原型性和普遍性陈述（包括类公理）的方案，以及缺失陈述、否定陈述和基数约束的建模方案。这些方案是现有基于OWL的建模方案的替代方案，我们为其提供了不涉及空白节点的对应表示方法。通过问题单元，我们还引入了在知识图谱中表示问题的方法，这类表示可直接转化为可执行的图谱查询。我们还提供了指令性陈述、条件指令性陈述和逻辑论证的建模方案。我们认为，与传统的基于OWL的解决方案相比，语义单元提供了一个能整体提升知识图谱表达能力与认知互操作性的框架。