This study considers the expressiveness (that is the expressive power or expressivity) of different types of Knowledge Organization Systems (KOS) and discusses its potential to be machine-processable in the context of the Semantic Web. For this purpose, the theoretical foundations of KOS are reviewed based on conceptualizations introduced by the Functional Requirements for Subject Authority Data (FRSAD) and the Simple Knowledge Organization System (SKOS); natural language processing techniques are also implemented. Applying a comparative analysis, the dataset comprises a thesaurus (Eurovoc), a subject headings system (LCSH) and a classification scheme (DDC). These are compared with an ontology (CIDOC-CRM) by focusing on how they define and handle concepts and relations. It was observed that LCSH and DDC focus on the formalism of character strings (nomens) rather than on the modelling of semantics; their definition of what constitutes a concept is quite fuzzy, and they comprise a large number of complex concepts. By contrast, thesauri have a coherent definition of what constitutes a concept, and apply a systematic approach to the modelling of relations. Ontologies explicitly define diverse types of relations, and are by their nature machine-processable. The paper concludes that the potential of both the expressiveness and machine processability of each KOS is extensively regulated by its structural rules. It is harder to represent subject headings and classification schemes as semantic networks with nodes and arcs, while thesauri are more suitable for such a representation. In addition, a paradigm shift is revealed which focuses on the modelling of relations between concepts, rather than the concepts themselves.

中文翻译：

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知识组织系统 (KOS) 的表现力和机器可加工性：概念和关系分析

本研究考虑了不同类型知识组织系统 (KOS) 的表达能力（即表达能力或表现力），并讨论了其在语义网环境中可被机器处理的潜力。为此，基于主题规范数据功能要求 (FRSAD) 和简单知识组织系统 (SKOS) 引入的概念，回顾了 KOS 的理论基础；还实现了自然语言处理技术。应用比较分析，数据集包括同义词库 (Eurovoc)、主题词系统 (LCSH) 和分类方案 (DDC)。通过关注它们如何定义和处理概念和关系，将它们与本体 (CIDOC-CRM) 进行比较。据观察，LCSH 和 DDC 侧重于字符串（nomens）的形式，而不是语义的建模；他们对概念的定义相当模糊，包含大量复杂的概念。相比之下，叙词表对概念的构成有一个连贯的定义，并将系统方法应用于关系建模。本体明确定义了不同类型的关系，并且本质上是机器可处理的。该论文得出的结论是，每个 KOS 的表现力和机器可加工性的潜力都受到其结构规则的广泛调节。将主题词和分类方案表示为具有节点和弧的语义网络比较困难，而叙词表更适合这种表示。此外，