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  • × year_i:[2010 TO 2020}
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  1. Kleineberg, M.: Context analysis and context indexing : formal pragmatics in knowledge organization (2014) 0.07 
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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
  2. ISKO Encyclopedia of Knowledge Organization (2016) 0.05 
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    Abstract
    This is an online, open access encyclopedia of knowledge organization hosted by ISKO and produced by ISKO Scientific Advisory Council. It will contain entries about concepts, disciplines, organizations, systems, standards, and theories etc. including important domain specific systems. The encyclopedia is peer reviewed, all articles are signed by author(s), dated and versioned (new versions may be distinguished as, for example, Version 1.1 for minor changes of version 1 and Version 2 for major changes). We will try to get articles by the best experts in the subject of each article and to maintain a high scholarly standard. The first accepted version of each article will also be published in a new section of Knowledge Organization: Reviews of concepts in knowledge organization.
  3. Shala, E.: ¬Die Autonomie des Menschen und der Maschine : gegenwärtige Definitionen von Autonomie zwischen philosophischem Hintergrund und technologischer Umsetzbarkeit (2014) 0.03 
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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.
  4. Wolchover, N.: Wie ein Aufsehen erregender Beweis kaum Beachtung fand (2017) 0.02 
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    Date
    22. 4.2017 10:42:05
    22. 4.2017 10:48:38
  5. Hafner, R.; Schelling, B.: Automatisierung der Sacherschließung mit Semantic Web Technologie (2015) 0.02 
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    Date
    22. 6.2015 16:08:38
  6. Röthler, D.: "Lehrautomaten" oder die MOOC-Vision der späten 60er Jahre (2014) 0.02 
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    Date
    22. 6.2018 11:04:35
  7. Hjoerland, B.: Subject (of documents) (2016) 0.02 
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    Source
    ISKO Encyclopedia of Knowledge Organization, ed. by B. Hjoerland. [http://www.isko.org/cyclo/logical_division]
  8. Frické, M.: Logical division (2016) 0.02 
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    Source
    ISKO Encyclopedia of Knowledge Organization, ed. by B. Hjoerland. [http://www.isko.org/cyclo/logical_division]
  9. Schultz, S.: ¬Die eine App für alles : Mobile Zukunft in China (2016) 0.02 
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    Date
    22. 6.2018 14:22:02
  10. Guidi, F.; Sacerdoti Coen, C.: ¬A survey on retrieval of mathematical knowledge (2015) 0.02 
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    Date
    22. 2.2017 12:51:57
  11. 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
  12. Schönherr, M.: Bestechend brillant : die Schönheit der Algorithmen (2016) 0.02 
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    Date
    10. 2.2016 17:22:23
  13. Häring, N.; Hensinger, P.: "Digitale Bildung" : Der abschüssige Weg zur Konditionierungsanstalt (2019) 0.02 
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    Date
    22. 2.2019 11:45:19
  14. Sojka, P.; Liska, M.: ¬The art of mathematics retrieval (2011) 0.02 
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    Content
    Vgl.: DocEng2011, September 19-22, 2011, Mountain View, California, USA Copyright 2011 ACM 978-1-4503-0863-2/11/09
    Date
    22. 2.2017 13:00:42
  15. Spitkovsky, V.; Norvig, P.: From words to concepts and back : dictionaries for linking text, entities and ideas (2012) 0.02 
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    Abstract
    Human language is both rich and ambiguous. When we hear or read words, we resolve meanings to mental representations, for example recognizing and linking names to the intended persons, locations or organizations. Bridging words and meaning - from turning search queries into relevant results to suggesting targeted keywords for advertisers - is also Google's core competency, and important for many other tasks in information retrieval and natural language processing. We are happy to release a resource, spanning 7,560,141 concepts and 175,100,788 unique text strings, that we hope will help everyone working in these areas. How do we represent concepts? Our approach piggybacks on the unique titles of entries from an encyclopedia, which are mostly proper and common noun phrases. We consider each individual Wikipedia article as representing a concept (an entity or an idea), identified by its URL. Text strings that refer to concepts were collected using the publicly available hypertext of anchors (the text you click on in a web link) that point to each Wikipedia page, thus drawing on the vast link structure of the web. For every English article we harvested the strings associated with its incoming hyperlinks from the rest of Wikipedia, the greater web, and also anchors of parallel, non-English Wikipedia pages. Our dictionaries are cross-lingual, and any concept deemed too fine can be broadened to a desired level of generality using Wikipedia's groupings of articles into hierarchical categories. The data set contains triples, each consisting of (i) text, a short, raw natural language string; (ii) url, a related concept, represented by an English Wikipedia article's canonical location; and (iii) count, an integer indicating the number of times text has been observed connected with the concept's url. Our database thus includes weights that measure degrees of association. For example, the top two entries for football indicate that it is an ambiguous term, which is almost twice as likely to refer to what we in the US call soccer. Vgl. auch: Spitkovsky, V.I., A.X. Chang: A cross-lingual dictionary for english Wikipedia concepts. In: http://nlp.stanford.edu/pubs/crosswikis.pdf.
  16. Mitchell, J.S.; Zeng, M.L.; Zumer, M.: Modeling classification systems in multicultural and multilingual contexts (2012) 0.01 
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    Abstract
    This paper reports on the second part of an initiative of the authors on researching classification systems with the conceptual model defined by the Functional Requirements for Subject Authority Data (FRSAD) final report. In an earlier study, the authors explored whether the FRSAD conceptual model could be extended beyond subject authority data to model classification data. The focus of the current study is to determine if classification data modeled using FRSAD can be used to solve real-world discovery problems in multicultural and multilingual contexts. The paper discusses the relationships between entities (same type or different types) in the context of classification systems that involve multiple translations and /or multicultural implementations. Results of two case studies are presented in detail: (a) two instances of the DDC (DDC 22 in English, and the Swedish-English mixed translation of DDC 22), and (b) Chinese Library Classification. The use cases of conceptual models in practice are also discussed.
  17. Hartmann, S.; Haffner, A.: Linked-RDA-Data in der Praxis (2010) 0.01 
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    Date
    13. 2.2011 20:22:23
  18. Eckert, K.: SKOS: eine Sprache für die Übertragung von Thesauri ins Semantic Web (2011) 0.01 
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    Date
    15. 3.2011 19:21:22
  19. Scheven, E.: Geokoordinaten in Bibliotheksdaten : Grundlage für innovative Nachnutzung (2015) 0.01 
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    Date
    16.11.2015 18:22:47
  20. Bensman, S.J.: Eugene Garfield, Francis Narin, and PageRank : the theoretical bases of the Google search engine (2013) 0.01 
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    Date
    17.12.2013 11:02:22

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