Search (5 results, page 1 of 1)

0.009274234 = product of:
  0.018548468 = sum of:
    0.018548468 = product of:
      0.037096936 = sum of:
        0.037096936 = weight(_text_:22 in 2239) [ClassicSimilarity], result of:
          0.037096936 = score(doc=2239,freq=2.0), product of:
            0.15980367 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045634337 = queryNorm
            0.23214069 = fieldWeight in 2239, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=2239)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

31. 5.2004 19:22:06

0.008998952 = product of:
  0.017997904 = sum of:
    0.017997904 = product of:
      0.035995807 = sum of:
        0.035995807 = weight(_text_:j in 1018) [ClassicSimilarity], result of:
          0.035995807 = score(doc=1018,freq=4.0), product of:
            0.14500295 = queryWeight, product of:
              3.1774964 = idf(docFreq=5010, maxDocs=44218)
              0.045634337 = queryNorm
            0.2482419 = fieldWeight in 1018, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.1774964 = idf(docFreq=5010, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1018)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

0.008743832 = product of:
  0.017487664 = sum of:
    0.017487664 = product of:
      0.034975328 = sum of:
        0.034975328 = weight(_text_:22 in 2654) [ClassicSimilarity], result of:
          0.034975328 = score(doc=2654,freq=4.0), product of:
            0.15980367 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045634337 = queryNorm
            0.21886435 = fieldWeight in 2654, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03125 = fieldNorm(doc=2654)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

In order to transfer the Chinese Classified Thesaurus (CCT) into a machine-processable format and provide CCT-based Web services, a pilot study has been conducted in which a variety of selected CCT classes and mapped thesaurus entries are encoded with SKOS. OWL and RDFS are also used to encode the same contents for the purposes of feasibility and cost-benefit comparison. CCT is a collected effort led by the National Library of China. It is an integration of the national standards Chinese Library Classification (CLC) 4th edition and Chinese Thesaurus (CT). As a manually created mapping product, CCT provides for each of the classes the corresponding thesaurus terms, and vice versa. The coverage of CCT includes four major clusters: philosophy, social sciences and humanities, natural sciences and technologies, and general works. There are 22 main-classes, 52,992 sub-classes and divisions, 110,837 preferred thesaurus terms, 35,690 entry terms (non-preferred terms), and 59,738 pre-coordinated headings (Chinese Classified Thesaurus, 2005) Major challenges of encoding this large vocabulary comes from its integrated structure. CCT is a result of the combination of two structures (illustrated in Figure 1): a thesaurus that uses ISO-2788 standardized structure and a classification scheme that is basically enumerative, but provides some flexibility for several kinds of synthetic mechanisms Other challenges include the complex relationships caused by differences of granularities of two original schemes and their presentation with various levels of SKOS elements; as well as the diverse coordination of entries due to the use of auxiliary tables and pre-coordinated headings derived from combining classes, subdivisions, and thesaurus terms, which do not correspond to existing unique identifiers. The poster reports the progress, shares the sample SKOS entries, and summarizes problems identified during the SKOS encoding process. Although OWL Lite and OWL Full provide richer expressiveness, the cost-benefit issues and the final purposes of encoding CCT raise questions of using such approaches.

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

0.0076358644 = product of:
  0.015271729 = sum of:
    0.015271729 = product of:
      0.030543458 = sum of:
        0.030543458 = weight(_text_:j in 415) [ClassicSimilarity], result of:
          0.030543458 = score(doc=415,freq=2.0), product of:
            0.14500295 = queryWeight, product of:
              3.1774964 = idf(docFreq=5010, maxDocs=44218)
              0.045634337 = queryNorm
            0.21064025 = fieldWeight in 415, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1774964 = idf(docFreq=5010, maxDocs=44218)
              0.046875 = fieldNorm(doc=415)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

0.0076358644 = product of:
  0.015271729 = sum of:
    0.015271729 = product of:
      0.030543458 = sum of:
        0.030543458 = weight(_text_:j in 549) [ClassicSimilarity], result of:
          0.030543458 = score(doc=549,freq=2.0), product of:
            0.14500295 = queryWeight, product of:
              3.1774964 = idf(docFreq=5010, maxDocs=44218)
              0.045634337 = queryNorm
            0.21064025 = fieldWeight in 549, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1774964 = idf(docFreq=5010, maxDocs=44218)
              0.046875 = fieldNorm(doc=549)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The spread of misinformation on social media has become a major societal issue during recent years. In this work, we used the ongoing COVID-19 pandemic as a case study to systematically investigate factors associated with the spread of multi-topic misinformation related to one event on social media based on the heuristic-systematic model. Among factors related to systematic processing of information, we discovered that the topics of a misinformation story matter, with conspiracy theories being the most likely to be retweeted. As for factors related to heuristic processing of information, such as when citizens look up to their leaders during such a crisis, our results demonstrated that behaviors of a political leader, former US President Donald J. Trump, may have nudged people's sharing of COVID-19 misinformation. Outcomes of this study help social media platform and users better understand and prevent the spread of misinformation on social media.