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Abstract

The paper will look into different patterns of facet analysis used in the UDC schedules and how these affect the scheme presentation, the underlying data structure and the management of the classification scheme. From the very beginning, UDC was designed to represent the universe of knowledge as an integral whole allowing for subjects/concepts from all fields of knowledge to be combined, linked and the nature of their relationships made explicit. In Otlet's original design, the emphasis for his new type of classification was on the coordination of classmarks at the point of searching, i.e., post-coordination, which he firmly rooted in an expressive notational system. While some UDC classes exhibit various patterns of facet analytical theory proper, others, although used in an analytico-synthetic fashion, follow less canonical structural patterns. The authors highlight the lack of connection made throughout the various stages of UDC restructuring between: a) theoretical requirements of an overarching facet analytical theory as a founding principle guiding the construction of schedules; and, b) practical requirements for an analytico-synthetic classification in terms of notational presentation and data structure that enables its use in indexing and retrieval, as well as its management online.

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Abstract

In the 21st century, multilingual tools are gaining importance as increasingly diverse user groups from different cultural and linguistic backgrounds seek access to equally diverse pieces of information. The authors of this paper believe that most current forms of multilingual information access are inadequate for this role, and that a new form of multilingual thesaurus is required. The core of this paper introduces their pilot thesaurus InfoDEFT as a possible model for new online thesauri, which are semantically structured, encyclopedic and multilingual. The authors conclude that while the manual construction of such thesauri is labour intensive and hence costly, pilot thesauri can be used as training sets for artificial learning programmes, thus increasing their volume considerably at relatively little extra cost.