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  • × year_i:[2010 TO 2020}
  1. Verwer, K.: Freiheit und Verantwortung bei Hans Jonas (2011) 0.20 
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    Content
    Vgl.: http%3A%2F%2Fcreativechoice.org%2Fdoc%2FHansJonas.pdf&usg=AOvVaw1TM3teaYKgABL5H9yoIifA&opi=89978449.
  2. Kleineberg, M.: Context analysis and context indexing : formal pragmatics in knowledge organization (2014) 0.17 
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    Source
    http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=5&ved=0CDQQFjAE&url=http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F3131107&ei=HzFWVYvGMsiNsgGTyoFI&usg=AFQjCNE2FHUeR9oQTQlNC4TPedv4Mo3DaQ&sig2=Rlzpr7a3BLZZkqZCXXN_IA&bvm=bv.93564037,d.bGg&cad=rja
  3. Suchenwirth, L.: Sacherschliessung in Zeiten von Corona : neue Herausforderungen und Chancen (2019) 0.14 
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    Footnote
    https%3A%2F%2Fjournals.univie.ac.at%2Findex.php%2Fvoebm%2Farticle%2Fdownload%2F5332%2F5271%2F&usg=AOvVaw2yQdFGHlmOwVls7ANCpTii.
  4. Gödert, W.; Lepsky, K.: Informationelle Kompetenz : ein humanistischer Entwurf (2019) 0.12 
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    Footnote
    Rez. in: Philosophisch-ethische Rezensionen vom 09.11.2019 (Jürgen Czogalla), Unter: https://philosophisch-ethische-rezensionen.de/rezension/Goedert1.html. In: B.I.T. online 23(2020) H.3, S.345-347 (W. Sühl-Strohmenger) [Unter: https%3A%2F%2Fwww.b-i-t-online.de%2Fheft%2F2020-03-rezensionen.pdf&usg=AOvVaw0iY3f_zNcvEjeZ6inHVnOK]. In: Open Password Nr. 805 vom 14.08.2020 (H.-C. Hobohm) [Unter: https://www.password-online.de/?mailpoet_router&endpoint=view_in_browser&action=view&data=WzE0MywiOGI3NjZkZmNkZjQ1IiwwLDAsMTMxLDFd].
  5. Xiong, C.: Knowledge based text representations for information retrieval (2016) 0.11 
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    Abstract
    The successes of information retrieval (IR) in recent decades were built upon bag-of-words representations. Effective as it is, bag-of-words is only a shallow text understanding; there is a limited amount of information for document ranking in the word space. This dissertation goes beyond words and builds knowledge based text representations, which embed the external and carefully curated information from knowledge bases, and provide richer and structured evidence for more advanced information retrieval systems. This thesis research first builds query representations with entities associated with the query. Entities' descriptions are used by query expansion techniques that enrich the query with explanation terms. Then we present a general framework that represents a query with entities that appear in the query, are retrieved by the query, or frequently show up in the top retrieved documents. A latent space model is developed to jointly learn the connections from query to entities and the ranking of documents, modeling the external evidence from knowledge bases and internal ranking features cooperatively. To further improve the quality of relevant entities, a defining factor of our query representations, we introduce learning to rank to entity search and retrieve better entities from knowledge bases. In the document representation part, this thesis research also moves one step forward with a bag-of-entities model, in which documents are represented by their automatic entity annotations, and the ranking is performed in the entity space.
    This proposal includes plans to improve the quality of relevant entities with a co-learning framework that learns from both entity labels and document labels. We also plan to develop a hybrid ranking system that combines word based and entity based representations together with their uncertainties considered. At last, we plan to enrich the text representations with connections between entities. We propose several ways to infer entity graph representations for texts, and to rank documents using their structure representations. This dissertation overcomes the limitation of word based representations with external and carefully curated information from knowledge bases. We believe this thesis research is a solid start towards the new generation of intelligent, semantic, and structured information retrieval.
    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. 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) 0.10 
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    Content
    Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.
  7. Huo, W.: Automatic multi-word term extraction and its application to Web-page summarization (2012) 0.09 
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    Content
    A Thesis presented to The University of Guelph In partial fulfilment of requirements for the degree of Master of Science in Computer Science. Vgl. Unter: http://www.inf.ufrgs.br%2F~ceramisch%2Fdownload_files%2Fpublications%2F2009%2Fp01.pdf.
    Date
    10. 1.2013 19:22:47
  8. Farazi, M.: Faceted lightweight ontologies : a formalization and some experiments (2010) 0.09 
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    Content
    PhD Dissertation at International Doctorate School in Information and Communication Technology. Vgl.: https%3A%2F%2Fcore.ac.uk%2Fdownload%2Fpdf%2F150083013.pdf&usg=AOvVaw2n-qisNagpyT0lli_6QbAQ.
  9. Shala, E.: ¬Die Autonomie des Menschen und der Maschine : gegenwärtige Definitionen von Autonomie zwischen philosophischem Hintergrund und technologischer Umsetzbarkeit (2014) 0.09 
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    Footnote
    Vgl. unter: https://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=2ahUKEwizweHljdbcAhVS16QKHXcFD9QQFjABegQICRAB&url=https%3A%2F%2Fwww.researchgate.net%2Fpublication%2F271200105_Die_Autonomie_des_Menschen_und_der_Maschine_-_gegenwartige_Definitionen_von_Autonomie_zwischen_philosophischem_Hintergrund_und_technologischer_Umsetzbarkeit_Redigierte_Version_der_Magisterarbeit_Karls&usg=AOvVaw06orrdJmFF2xbCCp_hL26q.
  10. Piros, A.: Az ETO-jelzetek automatikus interpretálásának és elemzésének kérdései (2018) 0.09 
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    Content
    Vgl. auch: New automatic interpreter for complex UDC numbers. Unter: <https%3A%2F%2Fudcc.org%2Ffiles%2FAttilaPiros_EC_36-37_2014-2015.pdf&usg=AOvVaw3kc9CwDDCWP7aArpfjrs5b>
  11. Becker, R.: Machine / Deep Learning : wie lernen künstliche neuronale Netze? (2017) 0.06 
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    Abstract
    Ein wichtiges Merkmal von Systemen mit künstlicher Intelligenz ist die Fähigkeit, selbständig zu lernen. Anders als bei klassischer Software, die Probleme und Fragen auf Basis von vorher festgelegten Regeln abarbeitet, können selbstlernende Machine Learning Algorithmen die besten Regeln für die Lösung bestimmter Aufgaben selber lernen. In diesem Artikel wird am Beispiel von künstlichen neuronalen Netzen erklärt, was "lernen" in diesem Zusammenhang heißt, und wie es funktioniert. Ein künstliches neuronales Netz besteht aus vielen einzelnen Neuronen, die meistens in mehreren miteinander verbundenen Schichten (Layern) angeordnet sind. Die Zahl der Layer bestimmt unter anderem den Grad der Komplexität, den ein künstliches neuronales Netz abbilden kann. Viele Layer machen ein neuronales Netz "tief" - daher spricht man in diesem Zusammenhang auch vom Deep Learning als einer Unterkategorie des Machine Learning (Maschinenlernen bzw. maschinelles lernen).
    Source
    https://jaai.de/machine-deep-learning-529/
  12. Laßmann, G.: Asimovs Robotergesetze : was leisten sie wirklich? (2017) 0.06 
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    Abstract
    In Simulationen treten bei Robotern innere Zustände immer dann auf, wenn Katastrophen wie der eigene Totalausfall drohen. Die bekannten Robotergesetze von Isaac Asimov sind schon mehr als 75 Jahre alt. Systematisch untersucht wurden sie bislang nicht, obgleich durch die technische Entwicklung in der Computertechnik das Erstellen von Regeln für Roboter immer drängender wird: Autonome Fahrzeug fahren auf unseren Straßen, das EU-Parlament diskutiert Roboterrechte und nicht nur der Deutsche Ethikrat befasst sich mit Pflegerobotern. Gunter Laßmann hat in seinem eBook Asimovs Robotergesetze philosophisch und technisch untersucht: Asimovs Robotergesetze. Was leisten sie wirklich?" Er erörtert, was ein Roboter ist und wie ein solcher Roboter nach dem heutigen Stand der Technik "denken" würde. Diskutiert wird, inwieweit Roboter individuell sein könnten und wer die Verantwortung für den Roboter trägt. Alle drei Robotergesetze werden erstmals simuliert, was teilweise zu unerwarteten Ergebnissen führt. Die Roboter werden durch Hinzunahme von Lernen weiterentwickelt, es werden die Unterschiede zwischen regelbasiertem Lernen und Deep Learning dargestellt und am aktuellen Beispiel erläutert.
    Date
    21. 1.2018 19:02:22
  13. Herb, U.; Beucke, D.: ¬Die Zukunft der Impact-Messung : Social Media, Nutzung und Zitate im World Wide Web (2013) 0.05 
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    Content
    Vgl. unter: https://www.leibniz-science20.de%2Fforschung%2Fprojekte%2Faltmetrics-in-verschiedenen-wissenschaftsdisziplinen%2F&ei=2jTgVaaXGcK4Udj1qdgB&usg=AFQjCNFOPdONj4RKBDf9YDJOLuz3lkGYlg&sig2=5YI3KWIGxBmk5_kv0P_8iQ.
  14. Nielsen, M.: Neuronale Netze : Alpha Go - Computer lernen Intuition (2018) 0.05 
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    Content
    Vgl. auch den Beitrag: Sokol, J.: Spielend lernen. In: Spektrum der Wissenschaft. 2018, H.11, S.72-76.
    Source
    Spektrum der Wissenschaft. 2018, H.1, S.22-27
  15. Neunzert, H.: Mathematische Modellierung : ein "curriculum vitae" (2012) 0.05 
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    Vortrag auf der Tagung "Geschichte und Modellierung", Jena, 3. Februar 2012. Vgl. unter: http://www.fmi.uni-jena.de/Fakult%C3%A4t/Institute+und+Abteilungen/Abteilung+f%C3%BCr+Didaktik/Kolloquien.html?highlight=neunzert.
  16. Sieglerschmidt, J.: Wissensordnungen im analogen und im digitalen Zeitalter (2017) 0.05 
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    Vgl. unter: https://books.google.de/books?hl=de&lr=&id=0rtGDwAAQBAJ&oi=fnd&pg=PA35&dq=inhaltserschlie%C3%9Fung+OR+sacherschlie%C3%9Fung&ots=5u0TwCbFqE&sig=GGw3Coc21CINkone-6Lx8LaSAjY#v=onepage&q=inhaltserschlie%C3%9Fung%20OR%20sacherschlie%C3%9Fung&f=false.
  17. Höbarth, U.: Konstruktivistisches Lernen mit Moodle : praktische Einsatzmöglichkeiten in Bildungsinstitutionen (2010) 0.04 
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    Abstract
    Die Unterstützung des Lehr-/Lernprozesses durch Lernplattformen und Web-2.0-Anwendungen wird immer stärker zu einem wesentlichen Faktor im Bildungsbereich. Aus dem breit gefächerten Angebot von Lernplattformen hat sich Moodle in den letzten Jahren als einer der Favoriten herauskristallisiert. Bedienungsanleitungen und Erklärungen zur Verwendung einzelner Module aus technischer Sicht gibt es bereits - kaum aufbereitet sind hingegen die methodisch-didaktischen Einsatzmöglichkeiten der unterschiedlichen Werkzeuge, angereichert mit praktischen Tipps aus der Praxis für die Praxis. Dies möchte das vorliegende Buch leisten. Die Autorin beleuchtet zunächst die verschiedenen Lernparadigmen, wobei der Fokus auf den Konstruktivismus gerichtet ist. Ergänzend werden die Gestaltungsanforderungen und -möglichkeiten des Lehr-/Lernprozesses in Lernplattformen thematisiert. Wie sehen sinnvolle Einsatzszenarien aus? Welche Funktion können Moodle-Online-Kurse in Blended-Learning-Szenarien einnehmen? Wo liegen mögliche Mehrwerte (und Probleme) gegenüber konventionellen Präsenzveranstaltungen? Praktische Beispiele ergänzen die theoretischen Grundlagen. Die Fallstudien und ihre Evaluation können sowohl als Impuls für die verstärkte Nutzung der Lernplattform Moodle im Unterricht gesehen werden, vermitteln den Lehrenden aber auch grundsätzliche methodische Ansätze des Prinzips der Prozessorientierung unter Verwendung webbasierter Software. -- Dieser Text bezieht sich auf eine vergriffene oder nicht verfügbare Ausgabe dieses Titels.
    Footnote
    Zugl.: Krems, Univ., Masterarb. u.d.T.: Höbarth, Ulrike: Konstruktivistisches und kollaboratives Lernen in der Lernplattform Moodle
    RSWK
    Computerunterstütztes Lernen / Moodle / Konstruktive Didaktik / Selbstgesteuertes Lernen / Fallstudiensammlung
    Series
    E-Learning
    Subject
    Computerunterstütztes Lernen / Moodle / Konstruktive Didaktik / Selbstgesteuertes Lernen / Fallstudiensammlung
  18. Rötzer, F.: Psychologen für die Künstliche Intelligenz (2018) 0.04 
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    Abstract
    Künstliche Intelligenz kann lernen anhand von Erfahrungen und Vorgaben. Das Problem ist aber, dass sich maschinelles Lernen zwar in eine bestimmte Richtung steuern lässt, um bestimmte Probleme zu lösen, dass aber der Programmierer mitunter nicht mehr weiß, was in dem "künstlichen Gehirn" bzw. dem neuronalen Netzwerk vor sich geht, das mit zunehmender Komplexität und Lernerfahrung für den Menschen zur Black Box wird, wo man nur sehen kann, was an Daten einfließt und was herauskommt.
    Date
    22. 1.2018 11:08:27
  19. Wissensspeicher in digitalen Räumen : Nachhaltigkeit, Verfügbarkeit, semantische Interoperabilität. Proceedings der 11. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation, Konstanz, 20. bis 22. Februar 2008 (2010) 0.04 
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    Content
    C. Begriffsarbeit in der Wissensorganisation Ingetraut Dahlberg: Begriffsarbeit in der Wissensorganisation Claudio Gnoli, Gabriele Merli, Gianni Pavan, Elisabetta Bernuzzi, and Marco Priano: Freely faceted classification for a Web-based bibliographic archive The BioAcoustic Reference Database Stefan Hauser: Terminologiearbeit im Bereich Wissensorganisation - Vergleich dreier Publikationen anhand der Darstellung des Themenkomplexes Thesaurus Daniel Kless: Erstellung eines allgemeinen Standards zur Wissensorganisation: Nutzen, Möglichkeiten, Herausforderungen, Wege D. Kommunikation und Lernen Gerald Beck und Simon Meissner: Strukturierung und Vermittlung von heterogenen (Nicht-)Wissensbeständen in der Risikokommunikation Angelo Chianese, Francesca Cantone, Mario Caropreso, and Vincenzo Moscato: ARCHAEOLOGY 2.0: Cultural E-Learning tools and distributed repositories supported by SEMANTICA, a System for Learning Object Retrieval and Adaptive Courseware Generation for e-learning environments Sonja Hierl, Lydia Bauer, Nadja Böller und Josef Herget: Kollaborative Konzeption von Ontologien in der Hochschullehre: Theorie, Chancen und mögliche Umsetzung Marc Wilhelm Küster, Christoph Ludwig, Yahya Al-Haff und Andreas Aschenbrenner: TextGrid: eScholarship und der Fortschritt der Wissenschaft durch vernetzte Angebote
  20. Lembke, G.; Leipner, I.: ¬Die Lüge der digitalen Bildung : warum unsere Kinder das Lernen verlernen (2018) 0.03 
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    Classification
    MBL 22
    RSWK
    Kind / Lernen / Lüge / Säugling (maschinell ermittelt)
    SSD
    MBL 22
    Subject
    Kind / Lernen / Lüge / Säugling (maschinell ermittelt)

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