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  • × theme_ss:"Wissensrepräsentation"
  • × type_ss:"el"
  1. Köstlbacher, A. (Übers.): OWL Web Ontology Language Überblick (2004) 0.02
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    Source
    http://www.semaweb.org/dokumente/w3/TR/2004/REC-owl-features-20040210-DE.html
  2. Endres-Niggemeyer, B.; Ziegert, C.: SummIt-BMT : (Summarize It in BMT) in Diagnose und Therapie, Abschlussbericht (2002) 0.02
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    Abstract
    SummIt-BMT (Summarize It in Bone Marrow Transplantation) - das Zielsystem des Projektes - soll Ärzten in der Knochenmarktransplantation durch kognitiv fundiertes Zusammenfassen (Endres-Niggemeyer, 1998) aus dem WWW eine schnelle Informationsaufnahme ermöglichen. Im bmbffinanzierten Teilprojekt, über das hier zu berichten ist, liegt der Schwerpunkt auf den klinischen Fragestellungen. SummIt-BMT hat als zentrale Komponente eine KMT-Ontologie. Den Systemablauf veranschaulicht Abb. 1: Benutzer geben ihren Informationsbedarf in ein strukturiertes Szenario ein. Sie ziehen dazu Begriffe aus der Ontologie heran. Aus dem Szenario werden Fragen an Suchmaschinen abgeleitet. Die Summit-BMT-Metasuchmaschine stößt Google an und sucht in Medline, der zentralen Literaturdatenbank der Medizin. Das Suchergebnis wird aufbereitet. Dabei werden Links zu Volltexten verfolgt und die Volltexte besorgt. Die beschafften Dokumente werden mit einem Schlüsselwortretrieval auf Passagen untersucht, in denen sich Suchkonzepte aus der Frage / Ontologie häufen. Diese Passagen werden zum Zusammenfassen vorgeschlagen. In ihnen werden die Aussagen syntaktisch analysiert. Die Systemagenten untersuchen sie. Lassen Aussagen sich mit einer semantischen Relation an die Frage anbinden, tragen also zur deren Beantwortung bei, werden sie in die Zusammenfassung aufgenommen, es sei denn, andere Agenten machen Hinderungsgründe geltend, z.B. Redundanz. Das Ergebnis der Zusammenfassung wird in das Frage/Antwort-Szenario integriert. Präsentiert werden Exzerpte aus den Quelldokumenten. Mit einem Link vermitteln sie einen sofortigen Rückgriff auf die Quelle. SummIt-BMT ist zum nächsten Durchgang von Informationssuche und Zusammenfassung bereit, sobald der Benutzer dies wünscht.
  3. 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
  4. Tudhope, D.; Hodge, G.: Terminology registries (2007) 0.02
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    Date
    26.12.2011 13:22:07
  5. OWL Web Ontology Language Test Cases (2004) 0.01
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    Date
    14. 8.2011 13:33:22
  6. 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
  7. 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
  8. Mayfield, J.; Finin, T.: Information retrieval on the Semantic Web : integrating inference and retrieval 0.01
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    Date
    12. 2.2011 17:35:22
  9. Priss, U.: Faceted knowledge representation (1999) 0.01
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    Date
    22. 1.2016 17:30:31
  10. Definition of the CIDOC Conceptual Reference Model (2003) 0.01
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    Date
    6. 8.2010 14:22:28
  11. Hollink, L.; Assem, M. van: Estimating the relevance of search results in the Culture-Web : a study of semantic distance measures (2010) 0.01
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    Date
    26.12.2011 13:40:22
  12. Bittner, T.; Donnelly, M.; Winter, S.: Ontology and semantic interoperability (2006) 0.01
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    Date
    3.12.2016 18:39:22
  13. Priss, U.: Description logic and faceted knowledge representation (1999) 0.01
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    Date
    22. 1.2016 17:30:31
  14. Beppler, F.D.; Fonseca, F.T.; Pacheco, R.C.S.: Hermeneus: an architecture for an ontology-enabled information retrieval (2008) 0.01
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    Date
    28.11.2016 12:43:22
  15. Monireh, E.; Sarker, M.K.; Bianchi, F.; Hitzler, P.; Doran, D.; Xie, N.: Reasoning over RDF knowledge bases using deep learning (2018) 0.01
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    Date
    16.11.2018 14:22:01