Search (5 results, page 1 of 1)

0.0120583065 = product of:
  0.024116613 = sum of:
    0.024116613 = product of:
      0.09646645 = sum of:
        0.09646645 = weight(_text_:authors in 3731) [ClassicSimilarity], result of:
          0.09646645 = score(doc=3731,freq=4.0), product of:
            0.22571123 = queryWeight, product of:
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.049510952 = queryNorm
            0.42738882 = fieldWeight in 3731, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.046875 = fieldNorm(doc=3731)
      0.25 = coord(1/4)
  0.5 = coord(1/2)

Abstract

Metadata applications have evolved in time into highly structured "islands of information" about digital resources, often bearing a strong semantic interpretation. Scarcely however are these semantics being communicated in machine readable and understandable ways. At the same time, the process for transforming the implied metadata knowledge into explicit Semantic Web descriptions can be problematic and is not always evident. In this article we take upon the well-established Dublin Core metadata standard as well as other metadata schemata, which often appear in digital repositories set-ups, and suggest a proper Semantic Web OWL ontology. In this process the authors cope with discrepancies and incompatibilities, indicative of such attempts, in novel ways. Moreover, we show the potential and necessity of this approach by demonstrating inferences on the resulting ontology, instantiated with actual metadata records. The authors conclude by presenting a working prototype that provides for inference-based querying on top of digital repositories.

0.011739088 = product of:
  0.023478176 = sum of:
    0.023478176 = product of:
      0.046956353 = sum of:
        0.046956353 = weight(_text_:22 in 4184) [ClassicSimilarity], result of:
          0.046956353 = score(doc=4184,freq=2.0), product of:
            0.17337891 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.049510952 = queryNorm
            0.2708308 = fieldWeight in 4184, 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=4184)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22. 1.2011 10:38:28

0.011739088 = product of:
  0.023478176 = sum of:
    0.023478176 = product of:
      0.046956353 = sum of:
        0.046956353 = weight(_text_:22 in 759) [ClassicSimilarity], result of:
          0.046956353 = score(doc=759,freq=2.0), product of:
            0.17337891 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.049510952 = queryNorm
            0.2708308 = fieldWeight in 759, 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=759)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

11. 5.2013 19:22:18

0.011739088 = product of:
  0.023478176 = sum of:
    0.023478176 = product of:
      0.046956353 = sum of:
        0.046956353 = weight(_text_:22 in 3283) [ClassicSimilarity], result of:
          0.046956353 = score(doc=3283,freq=2.0), product of:
            0.17337891 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.049510952 = queryNorm
            0.2708308 = fieldWeight in 3283, 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=3283)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

0.0071054255 = product of:
  0.014210851 = sum of:
    0.014210851 = product of:
      0.056843404 = sum of:
        0.056843404 = weight(_text_:authors in 1909) [ClassicSimilarity], result of:
          0.056843404 = score(doc=1909,freq=2.0), product of:
            0.22571123 = queryWeight, product of:
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.049510952 = queryNorm
            0.25184128 = fieldWeight in 1909, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1909)
      0.25 = coord(1/4)
  0.5 = coord(1/2)

Abstract

Purpose - The general science portal "vascoda" merges structured, high-quality information collections from more than 40 providers on the basis of search engine technology (FAST) and a concept which treats semantic heterogeneity between different controlled vocabularies. First experiences with the portal show some weaknesses of this approach which come out in most metadata-driven Digital Libraries (DLs) or subject specific portals. The purpose of the paper is to propose models to reduce the semantic complexity in heterogeneous DLs. The aim is to introduce value-added services (treatment of term vagueness and document re-ranking) that gain a certain quality in DLs if they are combined with heterogeneity components established in the project "Competence Center Modeling and Treatment of Semantic Heterogeneity". Design/methodology/approach - Two methods, which are derived from scientometrics and network analysis, will be implemented with the objective to re-rank result sets by the following structural properties: the ranking of the results by core journals (so-called Bradfordizing) and ranking by centrality of authors in co-authorship networks. Findings - The methods, which will be implemented, focus on the query and on the result side of a search and are designed to positively influence each other. Conceptually, they will improve the search quality and guarantee that the most relevant documents in result sets will be ranked higher. Originality/value - The central impact of the paper focuses on the integration of three structural value-adding methods, which aim at reducing the semantic complexity represented in distributed DLs at several stages in the information retrieval process: query construction, search and ranking and re-ranking.