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Abstract

The creation of the World Wide Web has had a profound impact an the ease with which information can be distributed and presented. Now with more and more information becoming available, there is an increasing demand for targeted global search, comparative studies, data transfer and data migration between heterogeneous sources of cultural and scholarly contents. This requires interoperability not only at the encoding level - a task solved well by XML for instance - but also at the more complex semantics level, where lie the characteristics of the domain. In the meanwhile, the reality of semantic interoperability is getting frustrating. In the cultural area alone, dozens of "standard" and hundreds of proprietary metadata and data structures exist, as well as hundreds of terminology systems. Core systems like the Dublin Core represent a common denominator by far too small to fulfil advanced requirements. Overstretching its already limited semantics in order to capture complex contents leads to further loss of meaning.

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Abstract

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.