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  1. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.05
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    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  2. Rahmstorf, G.: Methoden und Formate für mehrsprachige Begriffssysteme (1996) 0.03
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    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  3. Yitzhaki, M.: ¬A draft version of a consolidated thesaurus for the rapidly growing field of alternative medicine (2000) 0.03
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  4. Großjohann, K.: Gathering-, Harvesting-, Suchmaschinen (1996) 0.02
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    Date
    7. 2.1996 22:38:41
    Pages
    22 S
  5. Wätjen, H.-J.: Mensch oder Maschine? : Auswahl und Erschließung vonm Informationsressourcen im Internet (1996) 0.01
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    Date
    2. 2.1996 15:40:22
  6. Peponakis, M.; Mastora, A.; Kapidakis, S.; Doerr, M.: Expressiveness and machine processability of Knowledge Organization Systems (KOS) : an analysis of concepts and relations (2020) 0.01
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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.
  7. Luo, L.; Ju, J.; Li, Y.-F.; Haffari, G.; Xiong, B.; Pan, S.: ChatRule: mining logical rules with large language models for knowledge graph reasoning (2023) 0.01
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    Date
    23.11.2023 19:07:22