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  1. Dextre Clarke, S.G.; Will, L.D.; Cochard, N.: ¬The BS8723 thesaurus data model and exchange format, and its relationship to SKOS (2008) 0.51
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    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  2. Will, L.D.: UML model : as given in British Standard Draft for Development DD8723-5:2008 (2008) 0.48
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    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  3. Tudhope, D.; Hodge, G.: Terminology registries (2007) 0.45
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    Date
    26.12.2011 13:22:07
    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  4. Quick Guide to Publishing a Thesaurus on the Semantic Web (2008) 0.28
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    Abstract
    This document describes in brief how to express the content and structure of a thesaurus, and metadata about a thesaurus, in RDF. Using RDF allows data to be linked to and/or merged with other RDF data by semantic web applications. The Semantic Web, which is based on the Resource Description Framework (RDF), provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries.
    Source
    http://www.w3.org/TR/2005/WD-swbp-thesaurus-pubguide-20050517/
    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  5. Gladun, A.; Rogushina, J.: Development of domain thesaurus as a set of ontology concepts with use of semantic similarity and elements of combinatorial optimization (2021) 0.27
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    Abstract
    We consider use of ontological background knowledge in intelligent information systems and analyze directions of their reduction in compliance with specifics of particular user task. Such reduction is aimed at simplification of knowledge processing without loss of significant information. We propose methods of generation of task thesauri based on domain ontology that contain such subset of ontological concepts and relations that can be used in task solving. Combinatorial optimization is used for minimization of task thesaurus. In this approach, semantic similarity estimates are used for determination of concept significance for user task. Some practical examples of optimized thesauri application for semantic retrieval and competence analysis demonstrate efficiency of proposed approach.
    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  6. Assem, M. van; Menken, M.R.; Schreiber, G.; Wielemaker, J.; Wielinga, B.: ¬A method for converting thesauri to RDF/OWL (2004) 0.24
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    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  7. Bandholtz, T.; Schulte-Coerne, T.; Glaser, R.; Fock, J.; Keller, T.: iQvoc - open source SKOS(XL) maintenance and publishing tool (2010) 0.24
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    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  8. Assem, M. van; Gangemi, A.; Schreiber, G.: Conversion of WordNet to a standard RDF/OWL representation (2006) 0.21
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    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  9. Assem, M. van; Malaisé, V.; Miles, A.; Schreiber, G.: ¬A method to convert thesauri to SKOS (2006) 0.21
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    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  10. Fischer, D.H.: Converting a thesaurus to OWL : Notes on the paper "The National Cancer Institute's Thesaurus and Ontology" (2004) 0.16
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    Abstract
    The paper analysed here is a kind of position paper. In order to get a better under-standing of the reported work I used the retrieval interface of the thesaurus, the so-called NCI DTS Browser accessible via the Web3, and I perused the cited OWL file4 with numerous "Find" and "Find next" string searches. In addition the file was im-ported into Protégé 2000, Release 2.0, with OWL Plugin 1.0 and Racer Plugin 1.7.14. At the end of the paper's introduction the authors say: "In the following sections, this paper will describe the terminology development process at NCI, and the issues associated with converting a description logic based nomenclature to a semantically rich OWL ontology." While I will not deal with the first part, i.e. the terminology development process at NCI, I do not see the thesaurus as a description logic based nomenclature, or its cur-rent state and conversion already result in a "rich" OWL ontology. What does "rich" mean here? According to my view there is a great quantity of concepts and links but a very poor description logic structure which enables inferences. And what does the fol-lowing really mean, which is said a few lines previously: "Although editors have defined a number of named ontologic relations to support the description-logic based structure of the Thesaurus, additional relation-ships are considered for inclusion as required to support dependent applications."
    According to my findings several relations available in the thesaurus query interface as "roles", are not used, i.e. there are not yet any assertions with them. And those which are used do not contribute to complete concept definitions of concepts which represent thesaurus main entries. In other words: The authors claim to already have a "description logic based nomenclature", where there is not yet one which deserves that title by being much more than a thesaurus with strict subsumption and additional inheritable semantic links. In the last section of the paper the authors say: "The most time consuming process in this conversion was making a careful analysis of the Thesaurus to understand the best way to translate it into OWL." "For other conversions, these same types of distinctions and decisions must be made. The expressive power of a proprietary encoding can vary widely from that in OWL or RDF. Understanding the original semantics and engineering a solution that most closely duplicates it is critical for creating a useful and accu-rate ontology." My question is: What decisions were made and are they exemplary, can they be rec-ommended as "the best way"? I raise strong doubts with respect to that, and I miss more profound discussions of the issues at stake. The following notes are dedicated to a critical description and assessment of the results of that conversion activity. They are written in a tutorial style more or less addressing students, but myself being a learner especially in the field of medical knowledge representation I do not speak "ex cathedra".
    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  11. Assem, M. van: Converting and integrating vocabularies for the Semantic Web (2010) 0.15
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    Object
    Art and architecture thesaurus
    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  12. Studer, R.; Studer, H.-P.; Studer, A.: Semantisches Knowledge Retrieval (2001) 0.10
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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.
    Content
    Inhalt: 1. Einführung - 2. Wissensmanagement - 3. Information Retrieval - 3.1. Methoden und Techniken - 3.2. Information Retrieval in der Anwendung - 4. Semantische Ansätze - 4.1. Wissen modellieren - Ontologie - 4.2. Neues Wissen inferieren - 5. Knowledge Retrieval in der Anwendung - 6. Zukunftsaussichten - 7. Fazit
  13. Hüsken, P.: Information Retrieval im Semantic Web (2006) 0.08
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    Abstract
    Das Semantic Web bezeichnet ein erweitertes World Wide Web (WWW), das die Bedeutung von präsentierten Inhalten in neuen standardisierten Sprachen wie RDF Schema und OWL modelliert. Diese Arbeit befasst sich mit dem Aspekt des Information Retrieval, d.h. es wird untersucht, in wie weit Methoden der Informationssuche sich auf modelliertes Wissen übertragen lassen. Die kennzeichnenden Merkmale von IR-Systemen wie vage Anfragen sowie die Unterstützung unsicheren Wissens werden im Kontext des Semantic Web behandelt. Im Fokus steht die Suche nach Fakten innerhalb einer Wissensdomäne, die entweder explizit modelliert sind oder implizit durch die Anwendung von Inferenz abgeleitet werden können. Aufbauend auf der an der Universität Duisburg-Essen entwickelten Retrievalmaschine PIRE wird die Anwendung unsicherer Inferenz mit probabilistischer Prädikatenlogik (pDatalog) implementiert.
  14. Knorz, G.; Rein, B.: Semantische Suche in einer Hochschulontologie : Ontologie-basiertes Information-Filtering und -Retrieval mit relationalen Datenbanken (2005) 0.05
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    Abstract
    Ontologien werden eingesetzt, um durch semantische Fundierung insbesondere für das Dokumentenretrieval eine grundlegend bessere Basis zu haben, als dies gegenwärtiger Stand der Technik ist. Vorgestellt wird eine an der FH Darmstadt entwickelte und eingesetzte Ontologie, die den Gegenstandsbereich Hochschule sowohl breit abdecken und gleichzeitig differenziert semantisch beschreiben soll. Das Problem der semantischen Suche besteht nun darin, dass sie für Informationssuchende so einfach wie bei gängigen Suchmaschinen zu nutzen sein soll, und gleichzeitig auf der Grundlage des aufwendigen Informationsmodells hochwertige Ergebnisse liefern muss. Es wird beschrieben, welche Möglichkeiten die verwendete Software K-Infinity bereitstellt und mit welchem Konzept diese Möglichkeiten für eine semantische Suche nach Dokumenten und anderen Informationseinheiten (Personen, Veranstaltungen, Projekte etc.) eingesetzt werden.
    Date
    11. 2.2011 18:22:25
  15. Ulrich, W.: Simple Knowledge Organisation System (2007) 0.04
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    Content
    Semantic Web - Taxonomie und Thesaurus - SKOS - Historie - Klassen und Eigenschaften - Beispiele - Generierung - automatisiert - per Folksonomie - Fazit und Ausblick
  16. Haenelt, K.: Semantik im Wiki : am Beispiel des MediaWiki und Semantic MediaWiki (2011) 0.03
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  17. Endres-Niggemeyer, B.: Bessere Information durch Zusammenfassen aus dem WWW (1999) 0.03
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    Abstract
    Am Beispiel der Knochenmarktransplantation, eines medizinischen Spezialgebietes, wird im folgenden dargelegt, wie man BenutzerInnen eine großen Teil des Aufwandes bei der Wissensbeschaffung abnehmen kann, indem man Suchergebnisse aus dem Netz fragebezogen zusammenfaßt. Dadurch wird in zeitkritischen Situationen, wie sie in Diagnose und Therapie alltäglich sind, die Aufnahme neuen Wissens ermöglicht. Auf einen Überblick über den Stand des Textzusammenfassens und der Ontologieentwicklung folgt eine Systemskizze, in der die Informationssuche im WWW durch ein kognitiv fundiertes Zusammenfassungssystem ergänzt wird. Dazu wird eine Fach-Ontologie vorgeschlagen, die das benötigte Wissen organisiert und repräsentiert.
  18. Borchers, D.: Missing Link : Wenn der Kasten denkt - Niklas Luhmann und die Folgen (2017) 0.03
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    Abstract
    Gerade haben die Soziologen den 90. Geburstag des Systemtheoretikers Niklas Luhmann gefeiert. Die Informatiker stecken mitten in einem anspruchsvollen Digitalisierungsprojekt, seinen Gedankenkasten, sein "hölzernes Privat-Internet", zu verdaten.
    Source
    https://www.heise.de/newsticker/meldung/Missing-Link-Wenn-der-Kasten-denkt-Niklas-Luhmann-und-die-Folgen-3919843.html
  19. Hesse, W.: Informationsbegriff in Natur- und Kulturwissenschaften : Vorlesung/Seminar im WS 2004/05 (2004) 0.03
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    Abstract
    Enthält eine Darstellung des semiotischen Tetraeders und das Konzept der FRISCO-Gruppe
  20. Drewer, P.; Massion, F; Pulitano, D: Was haben Wissensmodellierung, Wissensstrukturierung, künstliche Intelligenz und Terminologie miteinander zu tun? (2017) 0.02
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    Abstract
    Diese Publikation beschreibt die Zusammenhänge zwischen wissenshaltigen begriffsorientierten Terminologien, Ontologien, Big Data und künstliche Intelligenz.
    Date
    13.12.2017 14:17:22

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