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  • × theme_ss:"Semantic Web"
  • × theme_ss:"Wissensrepräsentation"
  1. Stojanovic, N.: Ontology-based Information Retrieval : methods and tools for cooperative query answering (2005) 0.19 
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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.
  2. Weller, K.: Anforderungen an die Wissensrepräsentation im Social Semantic Web (2010) 0.03 
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    Abstract
    Dieser Artikel gibt einen Einblick in die aktuelle Verschmelzung von Web 2.0-und Semantic Web-Ansätzen, die als Social Semantic Web beschrieben werden kann. Die Grundidee des Social Semantic Web wird beschrieben und einzelne erste Anwendungsbeispiele vorgestellt. Ein wesentlicher Schwerpunkt dieser Entwicklung besteht in der Umsetzung neuer Methoden und Herangehensweisen im Bereich der Wissensrepräsentation. Dieser Artikel stellt vier Schwerpunkte vor, in denen sich die Wissensrepräsentationsmethoden im Social Semantic Web weiterentwickeln müssen und geht dabei jeweils auf den aktuellen Stand ein.
    Series
    Tagungen der Deutschen Gesellschaft für Informationswissenschaft und Informationspraxis ; Bd. 14) (DGI-Konferenz ; 1
    Source
    Semantic web & linked data: Elemente zukünftiger Informationsinfrastrukturen ; 1. DGI-Konferenz ; 62. Jahrestagung der DGI ; Frankfurt am Main, 7. - 9. Oktober 2010 ; Proceedings / Deutsche Gesellschaft für Informationswissenschaft und Informationspraxis. Hrsg.: M. Ockenfeld
  3. Meyer, A.: Begriffsrelationen im Kategoriensystem der Wikipedia : Entwicklung eines Relationeninventars zur kollaborativen Anwendung (2010) 0.01 
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    Abstract
    Neben den bekannten Hyperlinks in Artikeltexten verfügt die Online-Enzyklopädie Wikipedia mit ihrem Kategoriensystem über ein weiteres Mittel zur Herstellung von Relationen zwischen Artikeln. Jeder Artikel ist einer oder mehreren Kategorien zugeordnet, die ihrerseits anderen Kategorien zugeordnet sind. Auf diese Weise entsteht eine systematische Ordnung von Artikeln und Kategorien. Betrachtet man nur die Artikel- und Kategoriennamen sowie diese Relationen, so stellt das Kategoriensystem ein gemeinschaftlich erstelltes Begriffssystem dar, das sämtliche von der Wikipedia abgedeckten Themenbereiche umfasst, jedoch - technisch betrachtet - ausschließlich hierarchische Relationen enthält. Aufgrund des Fehlens eines differenzierten Regelwerks zur Kategorisierung ist das Kategoriensystem derzeit jedoch inkonsistent, daneben sind, bedingt durch das Vorhandensein lediglich eines Relationstyps, viele Relationen wenig aussagekräftig. Dennoch besteht das Potenzial zur Schaffung eines stark und differenziert relationierten Begriffssystems aus dem bestehenden Kategoriensystem heraus. Die vorliegende Arbeit diskutiert die Anwendungsmöglichkeiten eines solchen Begriffssystems und die Option seiner gemeinschaftlichen Entwicklung aus dem bestehenden Vokabular des Kategoriensystems, mithin also der gemeinschaftlichen Relationierung von Begriffen anhand eines differenzierten Relationeninventars. Ausgehend von den Kategorien "Theater" und "Jagd" der deutschsprachigen Wikipedia wird ein hierarchisch strukturiertes Relationeninventar definiert, das sowohl spezifische als auch allgemeine Relationstypen enthält und damit die Möglichkeit zur Übertragung auf andere Gegenstandsbereiche bietet. Sämtliche Artikel- und Kategoriennamen, die unterhalb jener Kategorien erscheinen, werden unter Verwendung der neu entwickelten Relationstypen als Deskriptoren relationiert.
  4. Studer, R.; Studer, H.-P.; Studer, A.: Semantisches Knowledge Retrieval (2001) 0.01 
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    Abstract
    Dieses Whitepaper befasst sich mit der Integration semantischer Technologien in bestehende Ansätze des Information Retrieval und die damit verbundenen weitreichenden Auswirkungen auf Effizienz und Effektivität von Suche und Navigation in Dokumenten. Nach einer Einbettung in die Problematik des Wissensmanagement aus Sicht der Informationstechnik folgt ein Überblick zu den Methoden des Information Retrieval. Anschließend werden die semantischen Technologien "Wissen modellieren - Ontologie" und "Neues Wissen ableiten - Inferenz" vorgestellt. Ein Integrationsansatz wird im Folgenden diskutiert und die entstehenden Mehrwerte präsentiert. Insbesondere ergeben sich Erweiterungen hinsichtlich einer verfeinerten Suchunterstützung und einer kontextbezogenen Navigation sowie die Möglichkeiten der Auswertung von regelbasierten Zusammenhängen und einfache Integration von strukturierten Informationsquellen. Das Whitepaper schließt mit einem Ausblick auf die zukünftige Entwicklung des WWW hin zu einem Semantic Web und die damit verbundenen Implikationen für semantische Technologien.
  5. Synak, M.; Dabrowski, M.; Kruk, S.R.: Semantic Web and ontologies (2009) 0.00 
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    Date
    31. 7.2010 16:58:22
  6. OWL Web Ontology Language Test Cases (2004) 0.00 
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    Date
    14. 8.2011 13:33:22
  7. Mayfield, J.; Finin, T.: Information retrieval on the Semantic Web : integrating inference and retrieval 0.00 
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    Date
    12. 2.2011 17:35:22
  8. Gendt, M. van; Isaac, I.; Meij, L. van der; Schlobach, S.: Semantic Web techniques for multiple views on heterogeneous collections : a case study (2006) 0.00 
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    Source
    Research and advanced technology for digital libraries : 10th European conference, proceedings / ECDL 2006, Alicante, Spain, September 17 - 22, 2006
  9. Hollink, L.; Assem, M. van: Estimating the relevance of search results in the Culture-Web : a study of semantic distance measures (2010) 0.00 
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    Date
    26.12.2011 13:40:22
  10. Prud'hommeaux, E.; Gayo, E.: RDF ventures to boldly meet your most pedestrian needs (2015) 0.00 
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    Source
    Bulletin of the Association for Information Science and Technology. 41(2015) no.4, S.18-22
  11. Zeng, M.L.; Fan, W.; Lin, X.: SKOS for an integrated vocabulary structure (2008) 0.00 
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    Abstract
    In order to transfer the Chinese Classified Thesaurus (CCT) into a machine-processable format and provide CCT-based Web services, a pilot study has been conducted in which a variety of selected CCT classes and mapped thesaurus entries are encoded with SKOS. OWL and RDFS are also used to encode the same contents for the purposes of feasibility and cost-benefit comparison. CCT is a collected effort led by the National Library of China. It is an integration of the national standards Chinese Library Classification (CLC) 4th edition and Chinese Thesaurus (CT). As a manually created mapping product, CCT provides for each of the classes the corresponding thesaurus terms, and vice versa. The coverage of CCT includes four major clusters: philosophy, social sciences and humanities, natural sciences and technologies, and general works. There are 22 main-classes, 52,992 sub-classes and divisions, 110,837 preferred thesaurus terms, 35,690 entry terms (non-preferred terms), and 59,738 pre-coordinated headings (Chinese Classified Thesaurus, 2005) Major challenges of encoding this large vocabulary comes from its integrated structure. CCT is a result of the combination of two structures (illustrated in Figure 1): a thesaurus that uses ISO-2788 standardized structure and a classification scheme that is basically enumerative, but provides some flexibility for several kinds of synthetic mechanisms Other challenges include the complex relationships caused by differences of granularities of two original schemes and their presentation with various levels of SKOS elements; as well as the diverse coordination of entries due to the use of auxiliary tables and pre-coordinated headings derived from combining classes, subdivisions, and thesaurus terms, which do not correspond to existing unique identifiers. The poster reports the progress, shares the sample SKOS entries, and summarizes problems identified during the SKOS encoding process. Although OWL Lite and OWL Full provide richer expressiveness, the cost-benefit issues and the final purposes of encoding CCT raise questions of using such approaches.
    Source
    Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas
  12. Monireh, E.; Sarker, M.K.; Bianchi, F.; Hitzler, P.; Doran, D.; Xie, N.: Reasoning over RDF knowledge bases using deep learning (2018) 0.00 
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    Date
    16.11.2018 14:22:01
  13. Zhitomirsky-Geffet, M.; Bar-Ilan, J.: Towards maximal unification of semantically diverse ontologies for controversial domains (2014) 0.00 
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    Date
    20. 1.2015 18:30:22

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