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  1. Kleineberg, M.: Context analysis and context indexing : formal pragmatics in knowledge organization (2014) 0.11 
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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. Sojka, P.; Liska, M.: ¬The art of mathematics retrieval (2011) 0.04 
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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
  3. Mitchell, J.S.; Zeng, M.L.; Zumer, M.: Modeling classification systems in multicultural and multilingual contexts (2012) 0.04 
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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.
  4. Hollink, L.; Assem, M. van: Estimating the relevance of search results in the Culture-Web : a study of semantic distance measures (2010) 0.03 
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    Date
    26.12.2011 13:40:22
  5. Open MIND (2015) 0.02 
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    Content
    Vgl. den Artikel: Lenzen, M.: Vor der Quadratwurzel steht die Quadratzahl. In: http://www.faz.net/aktuell/feuilleton/forschung-und-lehre/uni-mainz-stellt-publikationen-von-hirnforschern-online-13379697.html.
    Date
    27. 1.2015 11:48:22
  6. Dowding, H.; Gengenbach, M.; Graham, B.; Meister, S.; Moran, J.; Peltzman, S.; Seifert, J.; Waugh, D.: OSS4EVA: using open-source tools to fulfill digital preservation requirements (2016) 0.02 
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    Date
    28.10.2016 18:22:33
  7. 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
  8. 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
  9. Kuzma, M.: Are you able to find the maps you need? (2019) 0.01 
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  10. Neumann. M.: HAL: Hyperspace Analogue to Language (2012) 0.01 
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  11. Delsey, T.: ¬The Making of RDA (2016) 0.01 
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    Date
    17. 5.2016 19:22:40
  12. Voß, J.: Classification of knowledge organization systems with Wikidata (2016) 0.01 
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    Pages
    S.15-22
  13. Zanibbi, R.; Yuan, B.: Keyword and image-based retrieval for mathematical expressions (2011) 0.01 
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    Date
    22. 2.2017 12:53:49
  14. Mao, J.; Xu, W.; Yang, Y.; Wang, J.; Yuille, A.L.: Explain images with multimodal recurrent neural networks (2014) 0.01 
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    Abstract
    In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel sentence descriptions to explain the content of images. It directly models the probability distribution of generating a word given previous words and the image. Image descriptions are generated by sampling from this distribution. The model consists of two sub-networks: a deep recurrent neural network for sentences and a deep convolutional network for images. These two sub-networks interact with each other in a multimodal layer to form the whole m-RNN model. The effectiveness of our model is validated on three benchmark datasets (IAPR TC-12 [8], Flickr 8K [28], and Flickr 30K [13]). Our model outperforms the state-of-the-art generative method. In addition, the m-RNN model can be applied to retrieval tasks for retrieving images or sentences, and achieves significant performance improvement over the state-of-the-art methods which directly optimize the ranking objective function for retrieval.
  15. Riley, J.: Understanding metadata : what is metadata, and what is it for? (2017) 0.01 
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    Type
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  16. Roy, W.; Gray, C.: Preparing existing metadata for repository batch import : a recipe for a fickle food (2018) 0.01 
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    Date
    10.11.2018 16:27:22
  17. 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
  18. Sojka, P.; Lee, M.; Rehurek, R.; Hatlapatka, R.; Kucbel, M.; Bouche, T.; Goutorbe, C.; Anghelache, R.; Wojciechowski, K.: Toolset for entity and semantic associations : Final Release (2013) 0.01 
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  19. Gorman, M.: Revisiting enduring values (2015) 0.01 
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    Footnote
    Bezugnahme auf das Buch: Gorman, M.: Our enduring values: librarianship in the 21st century. Chicago [u.a.] ; American Library Ass. ; 2000 ; IX, 188 S.
  20. Manguinhas, H.; Charles, V.; Isaac, A.; Miles, T.; Lima, A.; Neroulidis, A.; Ginouves, V.; Atsidis, D.; Hildebrand, M.; Brinkerink, M.; Gordea, S.: Linking subject labels in cultural heritage metadata to MIMO vocabulary using CultuurLink (2016) 0.01 
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