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  1. Guidi, F.; Sacerdoti Coen, C.: ¬A survey on retrieval of mathematical knowledge (2015) 0.05
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    Date
    22. 2.2017 12:51:57
  2. Popper, K.R.: Three worlds : the Tanner lecture on human values. Deliverd at the University of Michigan, April 7, 1978 (1978) 0.04
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    Source
    https%3A%2F%2Ftannerlectures.utah.edu%2F_documents%2Fa-to-z%2Fp%2Fpopper80.pdf&usg=AOvVaw3f4QRTEH-OEBmoYr2J_c7H
  3. Soergel, D.; Lauser, B.; Liang, A.; Fisseha, F.; Keizer, J.; Katz, S.: Reengineering thesauri for new applications : the AGROVOC example (2004) 0.03
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  4. Beppler, F.D.; Fonseca, F.T.; Pacheco, R.C.S.: Hermeneus: an architecture for an ontology-enabled information retrieval (2008) 0.03
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    Date
    28.11.2016 12:43:22
    Source
    http://www.personal.psu.edu/faculty/f/u/fuf1/hermeneus/Hermeneus_architecture.pdf
  5. Qin, J.; Paling, S.: Converting a controlled vocabulary into an ontology : the case of GEM (2001) 0.03
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    Date
    24. 8.2005 19:20:22
  6. Assem, M. van; Rijgersberg, H.; Wigham, M.; Top, J.: Converting and annotating quantitative data tables (2010) 0.02
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    Abstract
    Companies, governmental agencies and scientists produce a large amount of quantitative (research) data, consisting of measurements ranging from e.g. the surface temperatures of an ocean to the viscosity of a sample of mayonnaise. Such measurements are stored in tables in e.g. spreadsheet files and research reports. To integrate and reuse such data, it is necessary to have a semantic description of the data. However, the notation used is often ambiguous, making automatic interpretation and conversion to RDF or other suitable format diffiult. For example, the table header cell "f(Hz)" refers to frequency measured in Hertz, but the symbol "f" can also refer to the unit farad or the quantities force or luminous flux. Current annotation tools for this task either work on less ambiguous data or perform a more limited task. We introduce new disambiguation strategies based on an ontology, which allows to improve performance on "sloppy" datasets not yet targeted by existing systems.
    Pages
    S.16-31
    Source
    The Semantic Web - ISWC 2010. 9th International Semantic Web Conference, ISWC 2010, Shanghai, China, November 7-11, 2010, Revised Selected Papers, Part I. Eds.: Peter F. Patel-Schneider et al
  7. Monireh, E.; Sarker, M.K.; Bianchi, F.; Hitzler, P.; Doran, D.; Xie, N.: Reasoning over RDF knowledge bases using deep learning (2018) 0.02
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    Date
    16.11.2018 14:22:01
  8. Assem, M. van; Menken, M.R.; Schreiber, G.; Wielemaker, J.; Wielinga, B.: ¬A method for converting thesauri to RDF/OWL (2004) 0.02
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    Abstract
    This paper describes a method for converting existing thesauri and related resources from their native format to RDF(S) and OWL. The method identifies four steps in the conversion process. In each step, decisions have to be taken with respect to the syntax or semantics of the resulting representation. Each step is supported through a number of guidelines. The method is illustrated through conversions of two large thesauri: MeSH and WordNet.
    Pages
    S.17-31
    Source
    Proceedings of the 3rd International Semantic Web Conference (ISWC'04). Eds. D. Plexousakis and F. van Harmelen
  9. Bartol, W.; Pióro, K.; Rosselló, F.: On the coverings by tolerance classes (2003) 0.02
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    Source
    Information sciences: informatics and computer science. 166(2004) nos.1-4, S.193-211
  10. Frey, J.; Streitmatter, D.; Götz, F.; Hellmann, S.; Arndt, N.: DBpedia Archivo : a Web-Scale interface for ontology archiving under consumer-oriented aspects (2020) 0.02
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  11. Pinto, F.; Fraser, M.: Access management, the key to a Portal (2003) 0.02
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  12. Buckland, M.; Chen, A.; Chen, H.M.; Kim, Y.; Lam, B.; Larson, R.; Norgard, B.; Purat, J.; Gey, F.: Mapping entry vocabulary to unfamiliar metadata vocabularies (1999) 0.02
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    Source
    D-Lib magazine. 5(1999) no.1, xx S
  13. Halpin, H.; Hayes, P.J.; McCusker, J.P.; McGuinness, D.L.; Thompson, H.S.: When owl:sameAs isn't the same : an analysis of identity in linked data (2010) 0.02
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    Pages
    S.305-320
    Source
    The Semantic Web - ISWC 2010. 9th International Semantic Web Conference, ISWC 2010, Shanghai, China, November 7-11, 2010, Revised Selected Papers, Part I. Eds.: Peter F. Patel-Schneider et al
  14. Priss, U.: Faceted knowledge representation (1999) 0.02
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    Abstract
    Faceted Knowledge Representation provides a formalism for implementing knowledge systems. The basic notions of faceted knowledge representation are "unit", "relation", "facet" and "interpretation". Units are atomic elements and can be abstract elements or refer to external objects in an application. Relations are sequences or matrices of 0 and 1's (binary matrices). Facets are relational structures that combine units and relations. Each facet represents an aspect or viewpoint of a knowledge system. Interpretations are mappings that can be used to translate between different representations. This paper introduces the basic notions of faceted knowledge representation. The formalism is applied here to an abstract modeling of a faceted thesaurus as used in information retrieval.
    Date
    22. 1.2016 17:30:31
  15. Van der Veer Martens, B.: Do citation systems represent theories of truth? (2001) 0.01
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    Date
    22. 7.2006 15:22:28
  16. Dunning, A.: Do we still need search engines? (1999) 0.01
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    Source
    Ariadne. 1999, no.22
  17. Momeni, F.; Mayr, P.: Analyzing the research output presented at European Networked Knowledge Organization Systems workshops (2000-2015) (2016) 0.01
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    Pages
    S.7-14
  18. DeSilva, J.M.; Traniello, J.F.A.; Claxton, A.G.; Fannin, L.D.: When and why did human brains decrease in size? : a new change-point analysis and insights from brain evolution in ants (2021) 0.01
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    Footnote
    Vgl. auch: Rötzer, F.: Warum schrumpft das Gehirn des Menschen seit ein paar Tausend Jahren? Unter: https://krass-und-konkret.de/wissenschaft-technik/warum-schrumpft-das-gehirn-des-menschen-seit-ein-paar-tausend-jahren/. "... seit einigen tausend Jahren - manche sagen seit 10.000 Jahren -, also nach dem Beginn der Landwirtschaft, der Sesshaftigkeit und der Stadtgründungen sowie der Erfindung der Schrift schrumpfte das menschliche Gehirn überraschenderweise wieder. ... Allgemein wird davon ausgegangen, dass mit den ersten Werkzeugen und vor allem beginnend mit der Erfindung der Schrift kognitive Funktionen, vor allem das Gedächtnis externalisiert wurden, allerdings um den Preis, neue Kapazitäten entwickeln zu müssen, beispielsweise Lesen und Schreiben. Gedächtnis beinhaltet individuelle Erfahrungen, aber auch kollektives Wissen, an dem alle Mitglieder einer Gemeinschaft mitwirken und in das das Wissen sowie die Erfahrungen der Vorfahren eingeschrieben sind. Im digitalen Zeitalter ist die Externalisierung und Entlastung der Gehirne noch sehr viel weitgehender, weil etwa mit KI nicht nur Wissensinhalte, sondern auch kognitive Fähigkeiten wie das Suchen, Sammeln, Analysieren und Auswerten von Informationen zur Entscheidungsfindung externalisiert werden, während die externalisierten Gehirne wie das Internet kollektiv in Echtzeit lernen und sich erweitern. Über Neuimplantate könnten schließlich Menschen direkt an die externalisierten Gehirne angeschlossen werden, aber auch direkt ihre kognitiven Kapazitäten erweitern, indem Prothesen, neue Sensoren oder Maschinen/Roboter auch in der Ferne in den ergänzten Körper der Gehirne aufgenommen werden.
    Source
    Frontiers in ecology and evolution, 22 October 2021 [https://www.frontiersin.org/articles/10.3389/fevo.2021.742639/full]
  19. Zanibbi, R.; Yuan, B.: Keyword and image-based retrieval for mathematical expressions (2011) 0.01
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    Abstract
    Two new methods for retrieving mathematical expressions using conventional keyword search and expression images are presented. An expression-level TF-IDF (term frequency-inverse document frequency) approach is used for keyword search, where queries and indexed expressions are represented by keywords taken from LATEX strings. TF-IDF is computed at the level of individual expressions rather than documents to increase the precision of matching. The second retrieval technique is a form of Content-Base Image Retrieval (CBIR). Expressions are segmented into connected components, and then components in the query expression and each expression in the collection are matched using contour and density features, aspect ratios, and relative positions. In an experiment using ten randomly sampled queries from a corpus of over 22,000 expressions, precision-at-k (k= 20) for the keyword-based approach was higher (keyword: µ= 84.0,s= 19.0, image-based:µ= 32.0,s= 30.7), but for a few of the queries better results were obtained using a combination of the two techniques.
    Date
    22. 2.2017 12:53:49
  20. Bittner, T.; Donnelly, M.; Winter, S.: Ontology and semantic interoperability (2006) 0.01
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    Date
    3.12.2016 18:39:22