Search (157 results, page 1 of 8)

  • × theme_ss:"Wissensrepräsentation"
  1. Aizawa, A.; Kohlhase, M.: Mathematical information retrieval (2021) 0.02
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    Field
    Mathematik
  2. Collard, J.; Paiva, V. de; Fong, B.; Subrahmanian, E.: Extracting mathematical concepts from text (2022) 0.02
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    Field
    Mathematik
  3. Sperber, W.; Ion, P.D.F.: Content analysis and classification in mathematics (2011) 0.02
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    Field
    Mathematik
  4. Nevzorova, O.; Nevzorov, V.; Kirillovich, A.: ¬A syntactic method of extracting terms from special texts for replenishing domain ontologies (2017) 0.02
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    Field
    Mathematik
  5. Lange, C.: Ontologies and languages for representing mathematical knowledge on the Semantic Web (2011) 0.01
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    Field
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  6. Boteram, F.; Gödert, W.; Hubrich, J.: Semantic interoperability and retrieval paradigms (2010) 0.01
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    Source
    Paradigms and conceptual systems in knowledge organization: Proceedings of the Eleventh International ISKO Conference, 23-26 February 2010 Rome, Italy. Edited by Claudio Gnoli and Fulvio Mazzocchi
  7. Schöndorf, P.: Nicht-konventionelle Thesaurusrelationen als Orientierungshilfen für Indexierung und Recherche: Analyse ausgewählter Beispiele (1988) 0.01
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    Date
    10.11.2015 10:23:01
  8. Boteram, F.: Semantische Relationen in Dokumentationssprachen : vom Thesaurus zum semantischen Netz (2008) 0.01
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    Date
    23. 2.2005 10:27:09
  9. Haller, S.H.M.: Mappingverfahren zur Wissensorganisation (2002) 0.01
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    Date
    30. 5.2010 16:22:35
    Pages
    100 S
  10. Müller, T.: Wissensrepräsentation mit semantischen Netzen im Bereich Luftfahrt (2006) 0.01
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    Abstract
    Es ist ein semantisches Netz für den Gegenstandsbereich Luftfahrt modelliert worden, welches Unternehmensinformationen, Organisationen, Fluglinien, Flughäfen, etc. enthält, Diese sind 10 Hauptkategorien zugeordnet worden, die untergliedert nach Facetten sind. Die Begriffe des Gegenstandsbereiches sind mit 23 unterschiedlichen Relationen verknüpft worden (Z. B.: 'hat Standort in', bietet an, 'ist Homebase von', etc). Der Schwerpunkt der Betrachtung liegt auf dem Unterschied zwischen den drei klassischen Standardrelationen und den zusätzlich eingerichteten Relationen, bezüglich ihrem Nutzen für ein effizientes Retrieval. Die angelegten Kategorien und Relationen sind sowohl für eine kognitive als auch für eine maschinelle Verarbeitung geeignet.
    Date
    23. 2.2005 10:27:09
    26. 9.2006 21:00:22
  11. Giunchiglia, F.; Zaihrayeu, I.; Farazi, F.: Converting classifications into OWL ontologies (2009) 0.01
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    Date
    23. 8.2011 19:43:39
  12. Boteram, F.: Semantische Relationen in Dokumentationssprachen : vom Thesaurus zum semantischen Netz (2008) 0.01
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    Date
    23. 2.2005 10:27:09
  13. Fonseca, F.: ¬The double role of ontologies in information science research (2007) 0.01
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    Date
    30. 4.2007 14:23:54
  14. Girju, R.; Beamer, B.; Rozovskaya, A.; Fister, A.; Bhat, S.: ¬A knowledge-rich approach to identifying semantic relations between nominals (2010) 0.01
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    Abstract
    This paper describes a state-of-the-art supervised, knowledge-intensive approach to the automatic identification of semantic relations between nominals in English sentences. The system employs a combination of rich and varied sets of new and previously used lexical, syntactic, and semantic features extracted from various knowledge sources such as WordNet and additional annotated corpora. The system ranked first at the third most popular SemEval 2007 Task - Classification of Semantic Relations between Nominals and achieved an F-measure of 72.4% and an accuracy of 76.3%. We also show that some semantic relations are better suited for WordNet-based models than other relations. Additionally, we make a distinction between out-of-context (regular) examples and those that require sentence context for relation identification and show that contextual data are important for the performance of a noun-noun semantic parser. Finally, learning curves show that the task difficulty varies across relations and that our learned WordNet-based representation is highly accurate so the performance results suggest the upper bound on what this representation can do.
    Date
    23. 1.2011 17:01:06
  15. Gödert, W.; Hubrich, J.; Nagelschmidt, M.: Semantic knowledge representation for information retrieval (2014) 0.01
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    Date
    23. 7.2017 13:49:22
  16. Stock, W.G.: Wissensrepräsentation (2014) 0.01
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    Date
    3. 2.2015 19:23:02
  17. SKOS2OWL : Online tool for deriving OWL ontologies from SKOS categorization schemas (2007) 0.01
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    Date
    23. 8.2011 19:52:27
  18. Baião Salgado Silva, G.; Lima, G.Â. Borém de Oliveira: Using topic maps in establishing compatibility of semantically structured hypertext contents (2012) 0.01
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    Date
    22. 2.2013 11:39:23
  19. Hoang, H.H.; Tjoa, A.M: ¬The state of the art of ontology-based query systems : a comparison of existing approaches (2006) 0.01
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    Date
    28.11.2016 18:23:55
  20. Mustafa El Hadi, W.: Terminologies, ontologies and information access (2006) 0.01
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    Date
    29. 2.2008 16:25:23

Years

Languages

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  • d 29
  • f 1
  • sp 1
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Types

  • a 121
  • el 38
  • m 7
  • x 7
  • n 4
  • s 4
  • r 2
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Subjects