  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.
