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Content

Master thesis Master of Science (Library and Information Studies) (MSc), Universität Wien. Advisor: Christoph Steiner. Vgl.: https://www.researchgate.net/publication/371680244_Vergabe_von_DDC-Sachgruppen_mittels_eines_Schlagwort-Thesaurus. DOI: 10.25365/thesis.70030. Vgl. dazu die Präsentation unter: https://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=web&cd=&ved=0CAIQw7AJahcKEwjwoZzzytz_AhUAAAAAHQAAAAAQAg&url=https%3A%2F%2Fwiki.dnb.de%2Fdownload%2Fattachments%2F252121510%2FDA3%2520Workshop-Gabler.pdf%3Fversion%3D1%26modificationDate%3D1671093170000%26api%3Dv2&psig=AOvVaw0szwENK1or3HevgvIDOfjx&ust=1687719410889597&opi=89978449.

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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.

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Abstract

The research study as reported here is an attempt to explore the possibilities of an AI/ML-based semi-automated indexing system in a library setup to handle large volumes of documents. It uses the Python virtual environment to install and configure an open source AI environment (named Annif) to feed the LOD (Linked Open Data) dataset of Library of Congress Subject Headings (LCSH) as a standard KOS (Knowledge Organisation System). The framework deployed the Turtle format of LCSH after cleaning the file with Skosify, applied an array of backend algorithms (namely TF-IDF, Omikuji, and NN-Ensemble) to measure relative performance, and selected Snowball as an analyser. The training of Annif was conducted with a large set of bibliographic records populated with subject descriptors (MARC tag 650$a) and indexed by trained LIS professionals. The training dataset is first treated with MarcEdit to export it in a format suitable for OpenRefine, and then in OpenRefine it undergoes many steps to produce a bibliographic record set suitable to train Annif. The framework, after training, has been tested with a bibliographic dataset to measure indexing efficiencies, and finally, the automated indexing framework is integrated with data wrangling software (OpenRefine) to produce suggested headings on a mass scale. The entire framework is based on open-source software, open datasets, and open standards.

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Abstract

Normdaten spielen speziell im Hinblick auf die Qualität der Inhaltserschließung bibliografischer und archivalischer Ressourcen eine wichtige Rolle. Ein konkretes Ziel der Inhaltserschließung ist z. B., dass alle Werke über Hermann Hesse einheitlich zu finden sind. Hier bieten Normdaten eine Lösung, indem z. B. bei der Erschließung einheitlich die GND-Nummer 11855042X für Hermann Hesse verwendet wird. Das Ergebnis ist eine höhere Qualität der Inhaltserschließung vor allem im Sinne von Einheitlichkeit und Eindeutigkeit und, daraus resultierend, eine bessere Auffindbarkeit. Werden solche Entitäten miteinander verknüpft, z. B. Hermann Hesse mit einem seiner Werke, entsteht ein Knowledge Graph, wie ihn etwa Google bei der Inhaltserschließung des Web verwendet (Singhal 2012). Die Entwicklung des Google Knowledge Graph und das hier vorgestellte Protokoll sind historisch miteinander verbunden: OpenRefine wurde ursprünglich als Google Refine entwickelt, und die Funktionalität zum Abgleich mit externen Datenquellen (Reconciliation) wurde ursprünglich zur Einbindung von Freebase entwickelt, einer der Datenquellen des Google Knowledge Graph. Freebase wurde später in Wikidata integriert. Schon Google Refine wurde zum Abgleich mit Normdaten verwendet, etwa den Library of Congress Subject Headings (Hooland et al. 2013).

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Date

22. 6.2023 18:23:31

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Date

4. 3.2020 14:22:41