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  1. McCorduck, P.: Denkmaschinen : die Geschichte der künstlichen Intelligenz (1987) 0.24 
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    Classification
    Mat T 1091 / Künstliche Intelligenz
    RSWK
    Künstliche Intelligenz / Geschichte
    SBB
    Mat T 1091 / Künstliche Intelligenz
    Subject
    Künstliche Intelligenz / Geschichte
  2. Kleineberg, M.: Context analysis and context indexing : formal pragmatics in knowledge organization (2014) 0.22 
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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. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.18 
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    Content
    Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.
    Date
    8. 1.2013 10:22:32
  4. Hutchins, W.J.; Somers, H.L.: ¬An introduction to machine translation (1992) 0.14 
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    Classification
    ST 306 Informatik / Monographien / Künstliche Intelligenz / Natürliche Sprachverarbeitung
    Mat T 1091 / Automatische Übersetzung
    RVK
    ST 306 Informatik / Monographien / Künstliche Intelligenz / Natürliche Sprachverarbeitung
    SBB
    Mat T 1091 / Automatische Übersetzung
  5. Indexing techniques for advanced database systems (1997) 0.11 
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    Classification
    Mat T 1091 / Index
    Mat T 1091 / Datenbanksystem
    SBB
    Mat T 1091 / Index
    Mat T 1091 / Datenbanksystem
  6. Popper, K.R.: Three worlds : the Tanner lecture on human values. Deliverd at the University of Michigan, April 7, 1978 (1978) 0.10 
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    Source
    https%3A%2F%2Ftannerlectures.utah.edu%2F_documents%2Fa-to-z%2Fp%2Fpopper80.pdf&usg=AOvVaw3f4QRTEH-OEBmoYr2J_c7H
  7. Li, L.; Shang, Y.; Zhang, W.: Improvement of HITS-based algorithms on Web documents 0.10 
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    Content
    Vgl.: http%3A%2F%2Fdelab.csd.auth.gr%2F~dimitris%2Fcourses%2Fir_spring06%2Fpage_rank_computing%2Fp527-li.pdf. Vgl. auch: http://www2002.org/CDROM/refereed/643/.
  8. Vetere, G.; Lenzerini, M.: Models for semantic interoperability in service-oriented architectures (2005) 0.09 
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    Content
    Vgl.: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5386707&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5386707.
  9. Stonier, T.: Beyond information : the natural history of intelligence (1992) 0.09 
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    RSWK
    Intelligenz / Künstliche Intelligenz (21)
    Subject
    Intelligenz / Künstliche Intelligenz (21)
  10. Mas, S.; Marleau, Y.: Proposition of a faceted classification model to support corporate information organization and digital records management (2009) 0.08 
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    Footnote
    Vgl.: http://ieeexplore.ieee.org/Xplore/login.jsp?reload=true&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel5%2F4755313%2F4755314%2F04755480.pdf%3Farnumber%3D4755480&authDecision=-203.
  11. 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) 0.08 
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    Content
    Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.
  12. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.08 
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    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  13. Xiong, C.: Knowledge based text representations for information retrieval (2016) 0.07 
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    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.
  14. Farazi, M.: Faceted lightweight ontologies : a formalization and some experiments (2010) 0.06 
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    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.
  15. Malsburg, C. von der: ¬The correlation theory of brain function (1981) 0.06 
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    http%3A%2F%2Fcogprints.org%2F1380%2F1%2FvdM_correlation.pdf&usg=AOvVaw0g7DvZbQPb2U7dYb49b9v_
  16. Cronin, B.: Metrics à la mode (2013) 0.06 
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    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.6, S.1091
  17. Huo, W.: Automatic multi-word term extraction and its application to Web-page summarization (2012) 0.06 
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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
  18. Zhang, L.; Pan, Y.; Zhang, T.: Focused named entity recognition using machine learning (2004) 0.06 
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    Date
    15.10.2005 19:57:22
    Source
    SIGIR'04: Proceedings of the 27th Annual International ACM-SIGIR Conference an Research and Development in Information Retrieval. Ed.: K. Järvelin, u.a
  19. Efron, M.; Winget, M.: Query polyrepresentation for ranking retrieval systems without relevance judgments (2010) 0.06 
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    Abstract
    Ranking information retrieval (IR) systems with respect to their effectiveness is a crucial operation during IR evaluation, as well as during data fusion. This article offers a novel method of approaching the system-ranking problem, based on the widely studied idea of polyrepresentation. The principle of polyrepresentation suggests that a single information need can be represented by many query articulations-what we call query aspects. By skimming the top k (where k is small) documents retrieved by a single system for multiple query aspects, we collect a set of documents that are likely to be relevant to a given test topic. Labeling these skimmed documents as putatively relevant lets us build pseudorelevance judgments without undue human intervention. We report experiments where using these pseudorelevance judgments delivers a rank ordering of IR systems that correlates highly with rankings based on human relevance judgments.
    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.6, S.1081-1091
  20. Steele, R.; Min, K.; Lo, A.: Personal health record architectures : technology infrastructure implications and dependencies (2012) 0.06 
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    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.6, S.1079-1091

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