Search (5 results, page 1 of 1)

0.03935625 = product of:
  0.1836625 = sum of:
    0.053833168 = weight(_text_:classification in 3130) [ClassicSimilarity], result of:
      0.053833168 = score(doc=3130,freq=8.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.5629819 = fieldWeight in 3130, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0625 = fieldNorm(doc=3130)
    0.07599616 = product of:
      0.15199232 = sum of:
        0.15199232 = weight(_text_:schemes in 3130) [ClassicSimilarity], result of:
          0.15199232 = score(doc=3130,freq=8.0), product of:
            0.16067243 = queryWeight, product of:
              5.3512506 = idf(docFreq=569, maxDocs=44218)
              0.03002521 = queryNorm
            0.9459764 = fieldWeight in 3130, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              5.3512506 = idf(docFreq=569, maxDocs=44218)
              0.0625 = fieldNorm(doc=3130)
      0.5 = coord(1/2)
    0.053833168 = weight(_text_:classification in 3130) [ClassicSimilarity], result of:
      0.053833168 = score(doc=3130,freq=8.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.5629819 = fieldWeight in 3130, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0625 = fieldNorm(doc=3130)
  0.21428572 = coord(3/14)

Abstract

Based on the research on three general classification schemes, this paper discusses issues encountered when expressing classification schemes in SKOS and explores opportunities of resolving major issues using OWL 2 Web Ontology Language.

0.028953599 = product of:
  0.10133759 = sum of:
    0.030006537 = weight(_text_:subject in 3183) [ClassicSimilarity], result of:
      0.030006537 = score(doc=3183,freq=4.0), product of:
        0.10738805 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.03002521 = queryNorm
        0.27942157 = fieldWeight in 3183, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3183)
    0.023791125 = weight(_text_:classification in 3183) [ClassicSimilarity], result of:
      0.023791125 = score(doc=3183,freq=4.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.24880521 = fieldWeight in 3183, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3183)
    0.0237488 = product of:
      0.0474976 = sum of:
        0.0474976 = weight(_text_:schemes in 3183) [ClassicSimilarity], result of:
          0.0474976 = score(doc=3183,freq=2.0), product of:
            0.16067243 = queryWeight, product of:
              5.3512506 = idf(docFreq=569, maxDocs=44218)
              0.03002521 = queryNorm
            0.2956176 = fieldWeight in 3183, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.3512506 = idf(docFreq=569, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3183)
      0.5 = coord(1/2)
    0.023791125 = weight(_text_:classification in 3183) [ClassicSimilarity], result of:
      0.023791125 = score(doc=3183,freq=4.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.24880521 = fieldWeight in 3183, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3183)
  0.2857143 = coord(4/14)

Abstract

Division is obviously important to Knowledge Organization. Typically, an organizational infrastructure might acknowledge three types of connecting relationships: class hierarchies, where some classes are subclasses of others, partitive hierarchies, where some items are parts of others, and instantiation, where some items are members of some classes (see Z39.19 ANSI/NISO 2005 as an example). The first two of these involve division (the third, instantiation, does not involve division). Logical division would usually be a part of hierarchical classification systems, which, in turn, are central to shelving in libraries, to subject classification schemes, to controlled vocabularies, and to thesauri. Partitive hierarchies, and partitive division, are often essential to controlled vocabularies, thesauri, and subject tagging systems. Partitive hierarchies also relate to the bearers of information; for example, a journal would typically have its component articles as parts and, in turn, they might have sections as their parts, and, of course, components might be arrived at by partitive division (see Tillett 2009 as an illustration). Finally, verbal division, disambiguating homographs, is basic to controlled vocabularies. Thus Division is a broad and relevant topic. This article, though, is going to focus on Logical Division.

0.0121596735 = product of:
  0.08511771 = sum of:
    0.042558856 = weight(_text_:classification in 311) [ClassicSimilarity], result of:
      0.042558856 = score(doc=311,freq=20.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.4450763 = fieldWeight in 311, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03125 = fieldNorm(doc=311)
    0.042558856 = weight(_text_:classification in 311) [ClassicSimilarity], result of:
      0.042558856 = score(doc=311,freq=20.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.4450763 = fieldWeight in 311, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03125 = fieldNorm(doc=311)
  0.14285715 = coord(2/14)

Abstract

This paper looks at Simple Knowledge Organization System (SKOS) to investigate how a faceted classification can be expressed in RDF and shared on the Semantic Web. Statement of the Problem Faceted classification outlines facets as well as subfacets and facet values. Hierarchical relationships and associative relationships are established in a faceted classification. RDF is used to describe how a specific URI has a relationship to a facet value. Not only does RDF decompose "information into pieces," but by incorporating facet values RDF also given the URI the hierarchical and associative relationships expressed in the faceted classification. Combining faceted classification and RDF creates more knowledge than if the two stood alone. An application understands the subjectpredicate-object relationship in RDF and can display hierarchical and associative relationships based on the object (facet) value. This paper continues to investigate if the above idea is indeed useful, used, and applicable. If so, how can a faceted classification be expressed in RDF? What would this expression look like? Literature Review This paper used the same articles as the paper A Survey of Faceted Classification: History, Uses, Drawbacks and the Semantic Web (Putkey, 2010). In that paper, appropriate resources were discovered by searching in various databases for "faceted classification" and "faceted search," either in the descriptor or title fields. Citations were also followed to find more articles as well as searching the Internet for the same terms. To retrieve the documents about RDF, searches combined "faceted classification" and "RDF, " looking for these words in either the descriptor or title.

0.009516451 = product of:
  0.06661515 = sum of:
    0.033307575 = weight(_text_:classification in 3530) [ClassicSimilarity], result of:
      0.033307575 = score(doc=3530,freq=4.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.34832728 = fieldWeight in 3530, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0546875 = fieldNorm(doc=3530)
    0.033307575 = weight(_text_:classification in 3530) [ClassicSimilarity], result of:
      0.033307575 = score(doc=3530,freq=4.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.34832728 = fieldWeight in 3530, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0546875 = fieldNorm(doc=3530)
  0.14285715 = coord(2/14)

Abstract

Classes in the Dewey Decimal Classification (DDC) system function as neighborhoods around focal topics in captions and notes. Topical neighborhoods are generated through specialization and instantiation, complex topic synthesis, index terms and mapped headings, hierarchical force, rules for choosing between numbers, development of the DDC over time, and use of the system in classifying resources. Implications of representation using a formal knowledge representation language are explored.

0.0021217826 = product of:
  0.029704956 = sum of:
    0.029704956 = weight(_text_:subject in 166) [ClassicSimilarity], result of:
      0.029704956 = score(doc=166,freq=2.0), product of:
        0.10738805 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.03002521 = queryNorm
        0.27661324 = fieldWeight in 166, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.0546875 = fieldNorm(doc=166)
  0.071428575 = coord(1/14)

Abstract

The recent experiences in the building, maintenance and reuse of ontologies has shown that the most efficient approach is the collaborative one. However, communication between collaborators such as IT professionals, librarians, web designers and subject matter experts is difficult and time consuming. This is because there are different reasoning strategies, different logics and different kinds of knowledge representation in the applications of Semantic Web. This article intends to be a reference scheme. It uses concise and simple explanations that can be used in common by specialists of different backgrounds working together in an application of Semantic Web.