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  • × theme_ss:"Wissensrepräsentation"
  1. Finke, M.; Risch, J.: "Match Me If You Can" : Sammeln und semantisches Aufbereiten von Fußballdaten (2017) 0.04
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    Abstract
    Interviews, Spielstatistiken oder Videoaufzeichnungen sind für Fußballfans zwar zahlreich im Internet verfügbar, aber auf viele verschiedene Websites verstreut. "Semantic Media Mining" verknüpft nun Fußballdaten aus unterschiedlichen Quellen, bereitet sie semantisch auf und führt sie auf einer einzigen Website zusammen. Dadurch dokumentieren und visualisieren wir mehr als 50 Jahre Fußballgeschichte mit über 500 Mannschaften und 40.000 Spielern der Champions League, sowie der 1. und 2. Bundesliga.
  2. Zeng, Q.; Yu, M.; Yu, W.; Xiong, J.; Shi, Y.; Jiang, M.: Faceted hierarchy : a new graph type to organize scientific concepts and a construction method (2019) 0.04
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    Content
    Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.
  3. Stuckenschmidt, H.; Harmelen, F. van: Information sharing on the semantic web (2005) 0.04
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    Classification
    QH 500 (BVB)
    RVK
    QH 500 (BVB)
  4. Stojanovic, N.: Ontology-based Information Retrieval : methods and tools for cooperative query answering (2005) 0.03
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    Content
    Vgl.: http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F1627&ei=tAtYUYrBNoHKtQb3l4GYBw&usg=AFQjCNHeaxKkKU3-u54LWxMNYGXaaDLCGw&sig2=8WykXWQoDKjDSdGtAakH2Q&bvm=bv.44442042,d.Yms.
  5. Xiong, C.: Knowledge based text representations for information retrieval (2016) 0.03
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    Content
    Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Language and Information Technologies. Vgl.: https%3A%2F%2Fwww.cs.cmu.edu%2F~cx%2Fpapers%2Fknowledge_based_text_representation.pdf&usg=AOvVaw0SaTSvhWLTh__Uz_HtOtl3.
  6. Alvers, M.R.: Semantische wissensbasierte Suche in den Life Sciences am Beispiel von GoPubMed (2010) 0.02
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    Content
    "Im Vortrag werden Aspekte der Suche nach Informationen und Antworten aus dem breiten Spektrum von der Suche nach einer Telefonnummer bis zur Frage nach dem "Sinn des Lebens" (deren Antwort auch im Vortrag leider nicht gegeben werden kann) angesprochen. Die Verwendung von Hintergrundwissen in Form von semantischen Begriffsnetzwerken, sogenannten Ontologien, hilft enorm, der Beantwortung von Fragen näher zu kommen. Sie garantieren Vollständigkeit der Suchergebnisse und schnelles Fokussieren auf Relevantes. Das bedeutungs-getriebene Einsortieren oder das Klassifizieren von Informationen aus Dokumenten oder von Internetinhalten, ermöglicht die Disambiguierung von Begriffen wie Salz und 01, " Cyclooxygenase Inhibitoren" - eher bekannt als Aspirin - oder das Auffinden aller Dokumente, die zum Thema CO2 - Sequestrierung gehören - also auch solcher, in den Begriff CO2 - Sequestrierung nicht direkt enthalten aber solche, die für das Thema relevant sind. Die Technologie hinter GoPubMed automatisiert schwierige Analysen, die normaler Weise von Wissenschaftlern getätigt werden. Dabei werden die notwendigen Informationen, wie sie von ausgefeilten Algorithmen ([DS05]) vorausgesagt wurden, mit einer deutlich höheren Genauigkeit bereitgestellt. Transinsights semantische Suchtechnologien wurde als erstes Beispiel der nächsten Generation der Suche entwickelt. Die Stärke liegt in der Fähigkeit, große Textkorpora (> 500 Millionen Dokumente) mit großen Ontologien (> 15 Millionen Konzepte) zu verknüpfen. In GoPubMed wird die Gene Ontology und MeSH verwendet, die zusammen mit einer Geo-Ontologie und allen Autoren ca. 15 Millionen Konzepte beinhalten.m"
  7. Schmitz-Esser, W.: Language of general communication and concept compatibility (1996) 0.02
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    Pages
    S.11-22
  8. Drewer, P.; Massion, F; Pulitano, D: Was haben Wissensmodellierung, Wissensstrukturierung, künstliche Intelligenz und Terminologie miteinander zu tun? (2017) 0.02
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    Date
    13.12.2017 14:17:22
  9. Tudhope, D.; Hodge, G.: Terminology registries (2007) 0.02
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    Date
    26.12.2011 13:22:07
  10. Haller, S.H.M.: Mappingverfahren zur Wissensorganisation (2002) 0.02
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    Date
    30. 5.2010 16:22:35
  11. 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
  12. 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
  13. 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
  14. OWL Web Ontology Language Test Cases (2004) 0.01
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    Date
    14. 8.2011 13:33:22
  15. Giunchiglia, F.; Villafiorita, A.; Walsh, T.: Theories of abstraction (1997) 0.01
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    Date
    1.10.2018 14:13:22
  16. 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
  17. Priss, U.: Faceted information representation (2000) 0.01
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    Date
    22. 1.2016 17:47:06
  18. Knorz, G.; Rein, B.: Semantische Suche in einer Hochschulontologie (2005) 0.01
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    Date
    11. 2.2011 18:22:58
  19. Deokattey, S.; Neelameghan, A.; Kumar, V.: ¬A method for developing a domain ontology : a case study for a multidisciplinary subject (2010) 0.01
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    Date
    22. 7.2010 19:41:16
  20. Knorz, G.; Rein, B.: Semantische Suche in einer Hochschulontologie : Ontologie-basiertes Information-Filtering und -Retrieval mit relationalen Datenbanken (2005) 0.01
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    Date
    11. 2.2011 18:22:25

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