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Abstract

Im Zuge der immer größeren Verfügbarkeit von Daten (Big Data) und rasanter Fortschritte im Daten-basierten maschinellen Lernen haben wir in den letzten Jahren Durchbrüche in der künstlichen Intelligenz erlebt. Dieser Vortrag beleuchtet diese Entwicklungen insbesondere im Hinblick auf die automatische Analyse von Textdaten. Anhand einfacher Beispiele illustrieren wir, wie moderne Textanalyse abläuft und zeigen wiederum anhand von Beispielen, welche praktischen Anwendungsmöglichkeiten sich heutzutage in Branchen wie dem Verlagswesen, der Finanzindustrie oder dem Consulting ergeben.

Content

Folien der Präsentation anlässlich des GENIOS Datenbankfrühstücks 2016, 19. Oktober 2016.

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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.