Search (3 results, page 1 of 1)

  • × author_ss:"Soergel, D."
  • × theme_ss:"Semantische Interoperabilität"
  1. Soergel, D.: Towards a relation ontology for the Semantic Web (2011) 0.01 
    0.008156957 = product of:
  0.057098698 = sum of:
    0.028549349 = weight(_text_:classification in 4342) [ClassicSimilarity], result of:
      0.028549349 = score(doc=4342,freq=4.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.29856625 = fieldWeight in 4342, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.046875 = fieldNorm(doc=4342)
    0.028549349 = weight(_text_:classification in 4342) [ClassicSimilarity], result of:
      0.028549349 = score(doc=4342,freq=4.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.29856625 = fieldWeight in 4342, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.046875 = fieldNorm(doc=4342)
  0.14285715 = coord(2/14)
    Abstract
    The Semantic Web consists of data structured for use by computer programs, such as data sets made available under the Linked Open Data initiative. Much of this data is structured following the entity-relationship model encoded in RDF for syntactic interoperability. For semantic interoperability, the semantics of the relationships used in any given dataset needs to be made explicit. Ultimately this requires an inventory of these relationships structured around a relation ontology. This talk will outline a blueprint for such an inventory, including a format for the description/definition of binary and n-ary relations, drawing on ideas put forth in the classification and thesaurus community over the last 60 years, upper level ontologies, systems like FrameNet, the Buffalo Relation Ontology, and an analysis of linked data sets.
    Source
    Classification and ontology: formal approaches and access to knowledge: proceedings of the International UDC Seminar, 19-20 September 2011, The Hague, The Netherlands. Eds.: A. Slavic u. E. Civallero
  2. Soergel, D.: Conceptual foundations for semantic mapping and semantic search (2011) 0.01 
    0.00576784 = product of:
  0.04037488 = sum of:
    0.02018744 = weight(_text_:classification in 3939) [ClassicSimilarity], result of:
      0.02018744 = score(doc=3939,freq=2.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.21111822 = fieldWeight in 3939, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.046875 = fieldNorm(doc=3939)
    0.02018744 = weight(_text_:classification in 3939) [ClassicSimilarity], result of:
      0.02018744 = score(doc=3939,freq=2.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.21111822 = fieldWeight in 3939, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.046875 = fieldNorm(doc=3939)
  0.14285715 = coord(2/14)
    Abstract
    This article proposes an approach to mapping between Knowledge Organization Systems (KOS), including ontologies, classifications, taxonomies, and thesauri and even natural languages, that is based on deep semantics. In this approach, concepts in each KOS are expressed through canonical expressions, such as description logic formulas, that combine atomic (or elemental) concepts drawn from a core classification. Relationships between concepts within or across KOS can then be derived by reasoning over the canonical expressions. The canonical expressions can also be used to provide a facet-based query formulation front-end for free-text search. The article illustrates this approach through many examples. It presents methods for the efficient construction of canonical expressions (linguistic analysis, exploiting information in the KOS and their hierarchies, and crowdsourcing) that make this approach feasible.
  3. Ahn, J.-w.; Soergel, D.; Lin, X.; Zhang, M.: Mapping between ARTstor terms and the Getty Art and Architecture Thesaurus (2014) 0.00 
    8.7171455E-4 = product of:
  0.0122040035 = sum of:
    0.0122040035 = product of:
      0.024408007 = sum of:
        0.024408007 = weight(_text_:22 in 1421) [ClassicSimilarity], result of:
          0.024408007 = score(doc=1421,freq=2.0), product of:
            0.10514317 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03002521 = queryNorm
            0.23214069 = fieldWeight in 1421, 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=1421)
      0.5 = coord(1/2)
  0.071428575 = coord(1/14)
    Source
    Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik

Authors