Search (5 results, page 1 of 1)

  • × language_ss:"e"
  • × theme_ss:"Visualisierung"
  • × type_ss:"a"
  • × year_i:[2000 TO 2010}
  1. Spero, S.: LCSH is to thesaurus as doorbell is to mammal : visualizing structural problems in the Library of Congress Subject Headings (2008) 0.03
    0.02659677 = product of:
      0.05319354 = sum of:
        0.05319354 = sum of:
          0.02856791 = weight(_text_:i in 2659) [ClassicSimilarity], result of:
            0.02856791 = score(doc=2659,freq=2.0), product of:
              0.17138503 = queryWeight, product of:
                3.7717297 = idf(docFreq=2765, maxDocs=44218)
                0.045439374 = queryNorm
              0.16668847 = fieldWeight in 2659, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.7717297 = idf(docFreq=2765, maxDocs=44218)
                0.03125 = fieldNorm(doc=2659)
          0.024625631 = weight(_text_:22 in 2659) [ClassicSimilarity], result of:
            0.024625631 = score(doc=2659,freq=2.0), product of:
              0.15912095 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.045439374 = queryNorm
              0.15476047 = fieldWeight in 2659, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.03125 = fieldNorm(doc=2659)
      0.5 = coord(1/2)
    
    Abstract
    The Library of Congress Subject Headings (LCSH) has been developed over the course of more than a century, predating the semantic web by some time. Until the 1986, the only concept-toconcept relationship available was an undifferentiated "See Also" reference, which was used for both associative (RT) and hierarchical (BT/NT) connections. In that year, in preparation for the first release of the headings in machine readable MARC Authorities form, an attempt was made to automatically convert these "See Also" links into the standardized thesaural relations. Unfortunately, the rule used to determine the type of reference to generate relied on the presence of symmetric links to detect associatively related terms; "See Also" references that were only present in one of the related terms were assumed to be hierarchical. This left the process vulnerable to inconsistent use of references in the pre-conversion data, with a marked bias towards promoting relationships to hierarchical status. The Library of Congress was aware that the results of the conversion contained many inconsistencies, and intended to validate and correct the results over the course of time. Unfortunately, twenty years later, less than 40% of the converted records have been evaluated. The converted records, being the earliest encountered during the Library's cataloging activities, represent the most basic concepts within LCSH; errors in the syndetic structure for these records affect far more subordinate concepts than those nearer the periphery. Worse, a policy of patterning new headings after pre-existing ones leads to structural errors arising from the conversion process being replicated in these newer headings, perpetuating and exacerbating the errors. As the LCSH prepares for its second great conversion, from MARC to SKOS, it is critical to address these structural problems. As part of the work on converting the headings into SKOS, I have experimented with different visualizations of the tangled web of broader terms embedded in LCSH. This poster illustrates several of these renderings, shows how they can help users to judge which relationships might not be correct, and shows just exactly how Doorbells and Mammals are related.
    Source
    Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas
  2. Hajdu Barát, A.: Usability and the user interfaces of classical information retrieval languages (2006) 0.01
    0.012498461 = product of:
      0.024996921 = sum of:
        0.024996921 = product of:
          0.049993843 = sum of:
            0.049993843 = weight(_text_:i in 232) [ClassicSimilarity], result of:
              0.049993843 = score(doc=232,freq=2.0), product of:
                0.17138503 = queryWeight, product of:
                  3.7717297 = idf(docFreq=2765, maxDocs=44218)
                  0.045439374 = queryNorm
                0.29170483 = fieldWeight in 232, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.7717297 = idf(docFreq=2765, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=232)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    This paper examines some traditional information searching methods and their role in Hungarian OPACs. What challenges are there in the digital and online environment? How do users work with them and do they give users satisfactory results? What kinds of techniques are users employing? In this paper I examine the user interfaces of UDC, thesauri, subject headings etc. in the Hungarian library. The key question of the paper is whether a universal system or local solutions is the best approach for searching in the digital environment.
  3. Samoylenko, I.; Chao, T.-C.; Liu, W.-C.; Chen, C.-M.: Visualizing the scientific world and its evolution (2006) 0.01
    0.010712966 = product of:
      0.021425933 = sum of:
        0.021425933 = product of:
          0.042851865 = sum of:
            0.042851865 = weight(_text_:i in 5911) [ClassicSimilarity], result of:
              0.042851865 = score(doc=5911,freq=2.0), product of:
                0.17138503 = queryWeight, product of:
                  3.7717297 = idf(docFreq=2765, maxDocs=44218)
                  0.045439374 = queryNorm
                0.25003272 = fieldWeight in 5911, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.7717297 = idf(docFreq=2765, maxDocs=44218)
                  0.046875 = fieldNorm(doc=5911)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
  4. Chen, C.: CiteSpace II : detecting and visualizing emerging trends and transient patterns in scientific literature (2006) 0.01
    0.00769551 = product of:
      0.01539102 = sum of:
        0.01539102 = product of:
          0.03078204 = sum of:
            0.03078204 = weight(_text_:22 in 5272) [ClassicSimilarity], result of:
              0.03078204 = score(doc=5272,freq=2.0), product of:
                0.15912095 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.045439374 = queryNorm
                0.19345059 = fieldWeight in 5272, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=5272)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Date
    22. 7.2006 16:11:05
  5. Beagle, D.: Visualizing keyword distribution across multidisciplinary c-space (2003) 0.01
    0.005356483 = product of:
      0.010712966 = sum of:
        0.010712966 = product of:
          0.021425933 = sum of:
            0.021425933 = weight(_text_:i in 1202) [ClassicSimilarity], result of:
              0.021425933 = score(doc=1202,freq=2.0), product of:
                0.17138503 = queryWeight, product of:
                  3.7717297 = idf(docFreq=2765, maxDocs=44218)
                  0.045439374 = queryNorm
                0.12501636 = fieldWeight in 1202, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.7717297 = idf(docFreq=2765, maxDocs=44218)
                  0.0234375 = fieldNorm(doc=1202)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    But what happens to this awareness in a digital library? Can discursive formations be represented in cyberspace, perhaps through diagrams in a visualization interface? And would such a schema be helpful to a digital library user? To approach this question, it is worth taking a moment to reconsider what Radford is looking at. First, he looks at titles to see how the books cluster. To illustrate, I scanned one hundred books on the shelves of a college library under subclass HT 101-395, defined by the LCC subclass caption as Urban groups. The City. Urban sociology. Of the first 100 titles in this sequence, fifty included the word "urban" or variants (e.g. "urbanization"). Another thirty-five used the word "city" or variants. These keywords appear to mark their titles as the heart of this discursive formation. The scattering of titles not using "urban" or "city" used related terms such as "town," "community," or in one case "skyscrapers." So we immediately see some empirical correlation between keywords and classification. But we also see a problem with the commonly used search technique of title-keyword. A student interested in urban studies will want to know about this entire subclass, and may wish to browse every title available therein. A title-keyword search on "urban" will retrieve only half of the titles, while a search on "city" will retrieve just over a third. There will be no overlap, since no titles in this sample contain both words. The only place where both words appear in a common string is in the LCC subclass caption, but captions are not typically indexed in library Online Public Access Catalogs (OPACs). In a traditional library, this problem is mitigated when the student goes to the shelf looking for any one of the books and suddenly discovers a much wider selection than the keyword search had led him to expect. But in a digital library, the issue of non-retrieval can be more problematic, as studies have indicated. Micco and Popp reported that, in a study funded partly by the U.S. Department of Education, 65 of 73 unskilled users searching for material on U.S./Soviet foreign relations found some material but never realized they had missed a large percentage of what was in the database.