Search (5 results, page 1 of 1)

0.0028433804 = product of:
  0.019903662 = sum of:
    0.008269517 = weight(_text_:information in 2092) [ClassicSimilarity], result of:
      0.008269517 = score(doc=2092,freq=4.0), product of:
        0.04306919 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.02453417 = queryNorm
        0.1920054 = fieldWeight in 2092, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2092)
    0.011634145 = product of:
      0.02326829 = sum of:
        0.02326829 = weight(_text_:22 in 2092) [ClassicSimilarity], result of:
          0.02326829 = score(doc=2092,freq=2.0), product of:
            0.085914485 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.02453417 = queryNorm
            0.2708308 = fieldWeight in 2092, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2092)
      0.5 = coord(1/2)
  0.14285715 = coord(2/14)

Abstract

The object of this literature review is to provide a historical perspective of R and D work in the area of Knowledge Organization (KO). This overview/summarization will provide information on major areas of KO. Journal articles published in core areas of KO: (Classification, Indexing, Thesauri and Taxonomies, Internet and Subject approach to information in the electronic era and Ontologies will be predominantly covered in this literature review. Coverage in this overview may not be completely exhaustive, but it succinctly showcases major developments in the area of KO. This review is a good source of additional reading material on KO apart from prescribed reading material on KO

Date

22. 6.2015 16:13:38

8.267796E-4 = product of:
  0.011574914 = sum of:
    0.011574914 = weight(_text_:information in 4743) [ClassicSimilarity], result of:
      0.011574914 = score(doc=4743,freq=6.0), product of:
        0.04306919 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.02453417 = queryNorm
        0.2687516 = fieldWeight in 4743, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0625 = fieldNorm(doc=4743)
  0.071428575 = coord(1/14)

Abstract

In this paper, we describe in detail the Information Content Evaluation Map (ICE-Map Visualization, formerly referred to as IC Difference Analysis). The ICE-Map Visualization is a visual data mining approach for all kinds of concept hierarchies that uses statistics about the concept usage to help a user in the evaluation and maintenance of the hierarchy. It consists of a statistical framework that employs the the notion of information content from information theory, as well as a visualization of the hierarchy and the result of the statistical analysis by means of a treemap.

5.167373E-4 = product of:
  0.0072343214 = sum of:
    0.0072343214 = weight(_text_:information in 4843) [ClassicSimilarity], result of:
      0.0072343214 = score(doc=4843,freq=6.0), product of:
        0.04306919 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.02453417 = queryNorm
        0.16796975 = fieldWeight in 4843, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4843)
  0.071428575 = coord(1/14)

Abstract

Formal Concept Analysis (FCA) is a well founded mathematical framework used for conceptual classication and knowledge management. Given a binary table describing a relation between objects and attributes, FCA consists in building a set of concepts organized by a subsumption relation within a concept lattice. Accordingly, FCA requires to transform complex data, e.g. numbers, intervals, graphs, into binary data leading to loss of information and poor interpretability of object classes. In this paper, we propose a pre-processing method producing binary data from complex data taking advantage of similarity between objects. As a result, the concept lattice is composed of classes being maximal sets of pairwise similar objects. This method is based on FCA and on a formalization of similarity as a tolerance relation (reexive and symmetric). It applies to complex object descriptions and especially here to interval data. Moreover, it can be applied to any kind of structured data for which a similarity can be dened (sequences, graphs, etc.). Finally, an application highlights that the resulting concept lattice plays an important role in information fusion problem, as illustrated with a real-world example in agronomy.

4.7734138E-4 = product of:
  0.006682779 = sum of:
    0.006682779 = weight(_text_:information in 3475) [ClassicSimilarity], result of:
      0.006682779 = score(doc=3475,freq=8.0), product of:
        0.04306919 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.02453417 = queryNorm
        0.1551638 = fieldWeight in 3475, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.03125 = fieldNorm(doc=3475)
  0.071428575 = coord(1/14)

Abstract

"Europeana.eu is about ideas and inspiration. It links you to 6 million digital items." This is the opening statement taken from the Europeana WWW-site (http://www.europeana.eu/portal/aboutus.html), and it clearly is concerned with the mission of Europeana - without, however, being over-explicit as to the precise nature of that mission. Europeana's current logo, too, has a programmatic aspect: the slogan "Think Culture" clearly again is related to Europeana's mission and at same time seems somewhat closer to the point: 'thinking' culture evokes notions like conceptualisation, reasoning, semantics and the like. Still, all this remains fragmentary and insufficient to actually clarify the functional scope and mission of Europeana. In fact, the author of the present contribution is convinced that Europeana has too often been described in terms of sheer quantity, as a high volume aggregation of digital representations of cultural heritage objects without sufficiently stressing the functional aspects of this endeavour. This conviction motivates the present contribution on some of the essential functional aspects of Europeana making clear that such a contribution - even if its author is deeply involved in building Europeana - should not be read as an official statement of the project or of the European Commission (which it is not!) - but as the personal statement from an information science perspective! From this perspective the opening statement is that Europeana is much more than a machine for mechanical accumulation of object representations but that one of its main characteristics should be to enable the generation of knowledge pertaining to cultural artefacts. The rest of the paper is about the implications of this initial statement in terms of information science, on the way we technically prepare to implement the necessary data structures and functionality and on the novel functionality Europeana will offer based on these elements and which go well beyond the 'traditional' digital library paradigm. However, prior to exploring these areas it may be useful to recall the notion of 'knowledge' that forms the basis of this contribution and which in turn is part of the well known continuum reaching from data via information and knowledge to wisdom.

4.176737E-4 = product of:
  0.0058474317 = sum of:
    0.0058474317 = weight(_text_:information in 5179) [ClassicSimilarity], result of:
      0.0058474317 = score(doc=5179,freq=2.0), product of:
        0.04306919 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.02453417 = queryNorm
        0.13576832 = fieldWeight in 5179, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5179)
  0.071428575 = coord(1/14)