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  • × theme_ss:"Wissensrepräsentation"
  1. Tudhope, D.; Hodge, G.: Terminology registries (2007) 0.02 
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    Date
    26.12.2011 13:22:07
  2. Haller, S.H.M.: Mappingverfahren zur Wissensorganisation (2002) 0.02 
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    Date
    30. 5.2010 16:22:35
  3. Nielsen, M.: Neuronale Netze : Alpha Go - Computer lernen Intuition (2018) 0.02 
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    Source
    Spektrum der Wissenschaft. 2018, H.1, S.22-27
  4. Pepper, S.; Moore, G.; TopicMaps.Org Authoring Group: XML Topic Maps (XTM) 1.0 : TopicMaps.Org Specification (2001) 0.01 
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    Type
    n
  5. Kayed, A.; Hirzallah, N.; Al Shalabi, L.A.; Najjar, M.: Building ontological relationships : a new approach (2008) 0.01 
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  6. Suchanek, F.M.; Kasneci, G.; Weikum, G.: YAGO: a large ontology from Wikipedia and WordNet (2008) 0.01 
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    Abstract
    This article presents YAGO, a large ontology with high coverage and precision. YAGO has been automatically derived from Wikipedia and WordNet. It comprises entities and relations, and currently contains more than 1.7 million entities and 15 million facts. These include the taxonomic Is-A hierarchy as well as semantic relations between entities. The facts for YAGO have been extracted from the category system and the infoboxes of Wikipedia and have been combined with taxonomic relations from WordNet. Type checking techniques help us keep YAGO's precision at 95%-as proven by an extensive evaluation study. YAGO is based on a clean logical model with a decidable consistency. Furthermore, it allows representing n-ary relations in a natural way while maintaining compatibility with RDFS. A powerful query model facilitates access to YAGO's data.
  7. Soergel, D.: Towards a relation ontology for the Semantic Web (2011) 0.01 
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    Abstract
    The Semantic Web consists of data structured for use by computer programs, such as data sets made available under the Linked Open Data initiative. Much of this data is structured following the entity-relationship model encoded in RDF for syntactic interoperability. For semantic interoperability, the semantics of the relationships used in any given dataset needs to be made explicit. Ultimately this requires an inventory of these relationships structured around a relation ontology. This talk will outline a blueprint for such an inventory, including a format for the description/definition of binary and n-ary relations, drawing on ideas put forth in the classification and thesaurus community over the last 60 years, upper level ontologies, systems like FrameNet, the Buffalo Relation Ontology, and an analysis of linked data sets.
  8. Köstlbacher, A. (Übers.): OWL Web Ontology Language Überblick (2004) 0.01 
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  9. OWL Web Ontology Language Overview (2004) 0.01 
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  10. OWL Web Ontology Language Semantics and Abstract Syntax (2004) 0.01 
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  11. SKOS Core Guide (2005) 0.01 
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  12. SKOS Simple Knowledge Organization System Primer (2009) 0.01 
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  13. Bold, N.; Kim, W.-J.; Yang, J.-D.: Converting object-based thesauri into XML Topic Maps (2010) 0.01 
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  14. Amarger, F.; Chanet, J.-P.; Haemmerlé, O.; Hernandez, N.; Roussey, C.: SKOS sources transformations for ontology engineering : agronomical taxonomy use case (2014) 0.01 
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  15. Tzitzikas, Y.; Spyratos, N.; Constantopoulos, P.; Analyti, A.: Extended faceted ontologies (2002) 0.01 
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  16. Guizzardi, G.; Guarino, N.: Semantics, ontology and explanation (2023) 0.01 
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  17. Börner, K.: Atlas of knowledge : anyone can map (2015) 0.01 
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    Date
    22. 1.2017 16:54:03
    22. 1.2017 17:10:56
  18. Synak, M.; Dabrowski, M.; Kruk, S.R.: Semantic Web and ontologies (2009) 0.01 
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    Date
    31. 7.2010 16:58:22
  19. Giunchiglia, F.; Villafiorita, A.; Walsh, T.: Theories of abstraction (1997) 0.01 
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    Date
    1.10.2018 14:13:22
  20. Hauff-Hartig, S.: Wissensrepräsentation durch RDF: Drei angewandte Forschungsbeispiele : Bitte recht vielfältig: Wie Wissensgraphen, Disco und FaBiO Struktur in Mangas und die Humanities bringen (2021) 0.01 
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    Date
    22. 5.2021 12:43:05

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