知识组织系统（KOS）的表达力与机器可处理性：概念与关系的分析
Expressiveness and machine processability of Knowledge Organization Systems (KOS): An analysis of concepts and relations
AI总结：
本文通过比较Eurovoc、LCSH、DDC与CIDOC-CRM在概念和关系定义上的差异，揭示KOS表达力与机器可处理性受结构规则制约，并指出从概念建模转向关系建模的范式转变。
摘要：
本研究考察不同类型知识组织系统（KOS）的表达力（即表达能力或表达性），并讨论其在语义网背景下被机器处理的潜力。为此，基于主题规范数据功能需求（FRSAD）和简单知识组织系统（SKOS）引入的概念化，回顾了KOS的理论基础；同时实现了自然语言处理技术。通过比较分析，数据集包括一部叙词表（Eurovoc）、一个主题标目系统（LCSH）和一个分类法（DDC）。将它们与一个本体（CIDOC-CRM）进行比较，重点关注它们如何定义和处理概念与关系。观察到LCSH和DDC侧重于字符串（nomens）的形式化，而不是语义的建模；它们对什么构成概念的定义相当模糊，并且包含大量复杂概念。相比之下，叙词表对什么构成概念有连贯的定义，并对关系的建模采用系统化方法。本体明确地定义了多种类型的关系，并且本质上具有机器可处理性。论文得出结论：每种KOS的表达力和机器可处理性的潜力都在很大程度上受其结构规则的制约。将主题标目和分类法表示为具有节点和弧的语义网络更加困难，而叙词表更适合这种表示。此外，揭示了一种范式转变，即关注概念之间关系的建模，而不是概念本身。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
