Search (260 results, page 1 of 13)

  • × theme_ss:"Semantisches Umfeld in Indexierung u. Retrieval"
  1. Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983) 0.05
    0.05037771 = product of:
      0.10075542 = sum of:
        0.10075542 = sum of:
          0.01339476 = weight(_text_:a in 2134) [ClassicSimilarity], result of:
            0.01339476 = score(doc=2134,freq=4.0), product of:
              0.053105544 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046056706 = queryNorm
              0.25222903 = fieldWeight in 2134, product of:
                2.0 = tf(freq=4.0), with freq of:
                  4.0 = termFreq=4.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.109375 = fieldNorm(doc=2134)
          0.087360665 = weight(_text_:22 in 2134) [ClassicSimilarity], result of:
            0.087360665 = score(doc=2134,freq=2.0), product of:
              0.16128273 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046056706 = queryNorm
              0.5416616 = fieldWeight in 2134, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.109375 = fieldNorm(doc=2134)
      0.5 = coord(1/2)
    
    Date
    30. 3.2001 13:32:22
    Type
    a
  2. Boyack, K.W.; Wylie,B.N.; Davidson, G.S.: Information Visualization, Human-Computer Interaction, and Cognitive Psychology : Domain Visualizations (2002) 0.05
    0.047506485 = product of:
      0.09501297 = sum of:
        0.09501297 = sum of:
          0.006765375 = weight(_text_:a in 1352) [ClassicSimilarity], result of:
            0.006765375 = score(doc=1352,freq=2.0), product of:
              0.053105544 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046056706 = queryNorm
              0.12739488 = fieldWeight in 1352, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.078125 = fieldNorm(doc=1352)
          0.0882476 = weight(_text_:22 in 1352) [ClassicSimilarity], result of:
            0.0882476 = score(doc=1352,freq=4.0), product of:
              0.16128273 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046056706 = queryNorm
              0.54716086 = fieldWeight in 1352, product of:
                2.0 = tf(freq=4.0), with freq of:
                  4.0 = termFreq=4.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.078125 = fieldNorm(doc=1352)
      0.5 = coord(1/2)
    
    Date
    22. 2.2003 17:25:39
    22. 2.2003 18:17:40
    Type
    a
  3. Kopácsi, S. et al.: Development of a classification server to support metadata harmonization in a long term preservation system (2016) 0.04
    0.037059225 = product of:
      0.07411845 = sum of:
        0.07411845 = sum of:
          0.011717974 = weight(_text_:a in 3280) [ClassicSimilarity], result of:
            0.011717974 = score(doc=3280,freq=6.0), product of:
              0.053105544 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046056706 = queryNorm
              0.22065444 = fieldWeight in 3280, product of:
                2.4494898 = tf(freq=6.0), with freq of:
                  6.0 = termFreq=6.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.078125 = fieldNorm(doc=3280)
          0.06240048 = weight(_text_:22 in 3280) [ClassicSimilarity], result of:
            0.06240048 = score(doc=3280,freq=2.0), product of:
              0.16128273 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046056706 = queryNorm
              0.38690117 = fieldWeight in 3280, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.078125 = fieldNorm(doc=3280)
      0.5 = coord(1/2)
    
    Source
    Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou
    Type
    a
  4. Sacco, G.M.: Dynamic taxonomies and guided searches (2006) 0.04
    0.036181405 = product of:
      0.07236281 = sum of:
        0.07236281 = sum of:
          0.010589487 = weight(_text_:a in 5295) [ClassicSimilarity], result of:
            0.010589487 = score(doc=5295,freq=10.0), product of:
              0.053105544 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046056706 = queryNorm
              0.19940455 = fieldWeight in 5295, product of:
                3.1622777 = tf(freq=10.0), with freq of:
                  10.0 = termFreq=10.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.0546875 = fieldNorm(doc=5295)
          0.061773323 = weight(_text_:22 in 5295) [ClassicSimilarity], result of:
            0.061773323 = score(doc=5295,freq=4.0), product of:
              0.16128273 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046056706 = queryNorm
              0.38301262 = fieldWeight in 5295, product of:
                2.0 = tf(freq=4.0), with freq of:
                  4.0 = termFreq=4.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.0546875 = fieldNorm(doc=5295)
      0.5 = coord(1/2)
    
    Abstract
    A new search paradigm, in which the primary user activity is the guided exploration of a complex information space rather than the retrieval of items based on precise specifications, is proposed. The author claims that this paradigm is the norm in most practical applications, and that solutions based on traditional search methods are not effective in this context. He then presents a solution based on dynamic taxonomies, a knowledge management model that effectively guides users to reach their goal while giving them total freedom in exploring the information base. Applications, benefits, and current research are discussed.
    Date
    22. 7.2006 17:56:22
    Type
    a
  5. Rekabsaz, N. et al.: Toward optimized multimodal concept indexing (2016) 0.03
    0.034582928 = product of:
      0.069165856 = sum of:
        0.069165856 = sum of:
          0.006765375 = weight(_text_:a in 2751) [ClassicSimilarity], result of:
            0.006765375 = score(doc=2751,freq=2.0), product of:
              0.053105544 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046056706 = queryNorm
              0.12739488 = fieldWeight in 2751, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.078125 = fieldNorm(doc=2751)
          0.06240048 = weight(_text_:22 in 2751) [ClassicSimilarity], result of:
            0.06240048 = score(doc=2751,freq=2.0), product of:
              0.16128273 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046056706 = queryNorm
              0.38690117 = fieldWeight in 2751, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.078125 = fieldNorm(doc=2751)
      0.5 = coord(1/2)
    
    Date
    1. 2.2016 18:25:22
    Type
    a
  6. Kozikowski, P. et al.: Support of part-whole relations in query answering (2016) 0.03
    0.034582928 = product of:
      0.069165856 = sum of:
        0.069165856 = sum of:
          0.006765375 = weight(_text_:a in 2754) [ClassicSimilarity], result of:
            0.006765375 = score(doc=2754,freq=2.0), product of:
              0.053105544 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046056706 = queryNorm
              0.12739488 = fieldWeight in 2754, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.078125 = fieldNorm(doc=2754)
          0.06240048 = weight(_text_:22 in 2754) [ClassicSimilarity], result of:
            0.06240048 = score(doc=2754,freq=2.0), product of:
              0.16128273 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046056706 = queryNorm
              0.38690117 = fieldWeight in 2754, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.078125 = fieldNorm(doc=2754)
      0.5 = coord(1/2)
    
    Date
    1. 2.2016 18:25:22
    Type
    a
  7. Marx, E. et al.: Exploring term networks for semantic search over RDF knowledge graphs (2016) 0.03
    0.034582928 = product of:
      0.069165856 = sum of:
        0.069165856 = sum of:
          0.006765375 = weight(_text_:a in 3279) [ClassicSimilarity], result of:
            0.006765375 = score(doc=3279,freq=2.0), product of:
              0.053105544 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046056706 = queryNorm
              0.12739488 = fieldWeight in 3279, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.078125 = fieldNorm(doc=3279)
          0.06240048 = weight(_text_:22 in 3279) [ClassicSimilarity], result of:
            0.06240048 = score(doc=3279,freq=2.0), product of:
              0.16128273 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046056706 = queryNorm
              0.38690117 = fieldWeight in 3279, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.078125 = fieldNorm(doc=3279)
      0.5 = coord(1/2)
    
    Source
    Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou
    Type
    a
  8. Efthimiadis, E.N.: End-users' understanding of thesaural knowledge structures in interactive query expansion (1994) 0.03
    0.02964738 = product of:
      0.05929476 = sum of:
        0.05929476 = sum of:
          0.009374379 = weight(_text_:a in 5693) [ClassicSimilarity], result of:
            0.009374379 = score(doc=5693,freq=6.0), product of:
              0.053105544 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046056706 = queryNorm
              0.17652355 = fieldWeight in 5693, product of:
                2.4494898 = tf(freq=6.0), with freq of:
                  6.0 = termFreq=6.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.0625 = fieldNorm(doc=5693)
          0.04992038 = weight(_text_:22 in 5693) [ClassicSimilarity], result of:
            0.04992038 = score(doc=5693,freq=2.0), product of:
              0.16128273 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046056706 = queryNorm
              0.30952093 = fieldWeight in 5693, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.0625 = fieldNorm(doc=5693)
      0.5 = coord(1/2)
    
    Abstract
    The process of term selection for query expansion by end-users is discussed within the context of a study of interactive query expansion in a relevance feedback environment. This user study focuses on how users' perceive and understand term relationships, such as hierarchical and associative relationships, in their searches
    Date
    30. 3.2001 13:35:22
    Type
    a
  9. Fieldhouse, M.; Hancock-Beaulieu, M.: ¬The design of a graphical user interface for a highly interactive information retrieval system (1996) 0.03
    0.027640268 = product of:
      0.055280536 = sum of:
        0.055280536 = sum of:
          0.011600202 = weight(_text_:a in 6958) [ClassicSimilarity], result of:
            0.011600202 = score(doc=6958,freq=12.0), product of:
              0.053105544 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046056706 = queryNorm
              0.21843673 = fieldWeight in 6958, product of:
                3.4641016 = tf(freq=12.0), with freq of:
                  12.0 = termFreq=12.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.0546875 = fieldNorm(doc=6958)
          0.043680333 = weight(_text_:22 in 6958) [ClassicSimilarity], result of:
            0.043680333 = score(doc=6958,freq=2.0), product of:
              0.16128273 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046056706 = queryNorm
              0.2708308 = fieldWeight in 6958, 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=6958)
      0.5 = coord(1/2)
    
    Abstract
    Reports on the design of a GUI for the Okapi 'best match' retrieval system developed at the Centre for Interactive Systems Research, City University, UK, for online library catalogues. The X-Windows interface includes an interactive query expansion (IQE) facilty which involves the user in the selection of query terms to reformulate a search. Presents the design rationale, based on a game board metaphor, and describes the features of each of the stages of the search interaction. Reports on the early operational field trial and discusses relevant evaluation issues and objectives
    Source
    Information retrieval: new systems and current research. Proceedings of the 16th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, Drymen, Scotland, 22-23 Mar 94. Ed.: R. Leon
    Type
    a
  10. Faaborg, A.; Lagoze, C.: Semantic browsing (2003) 0.03
    0.027640268 = product of:
      0.055280536 = sum of:
        0.055280536 = sum of:
          0.011600202 = weight(_text_:a in 1026) [ClassicSimilarity], result of:
            0.011600202 = score(doc=1026,freq=12.0), product of:
              0.053105544 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046056706 = queryNorm
              0.21843673 = fieldWeight in 1026, product of:
                3.4641016 = tf(freq=12.0), with freq of:
                  12.0 = termFreq=12.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.0546875 = fieldNorm(doc=1026)
          0.043680333 = weight(_text_:22 in 1026) [ClassicSimilarity], result of:
            0.043680333 = score(doc=1026,freq=2.0), product of:
              0.16128273 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046056706 = queryNorm
              0.2708308 = fieldWeight in 1026, 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=1026)
      0.5 = coord(1/2)
    
    Abstract
    We have created software applications that allow users to both author and use Semantic Web metadata. To create and use a layer of semantic content on top of the existing Web, we have (1) implemented a user interface that expedites the task of attributing metadata to resources on the Web, and (2) augmented a Web browser to leverage this semantic metadata to provide relevant information and tasks to the user. This project provides a framework for annotating and reorganizing existing files, pages, and sites on the Web that is similar to Vannevar Bushrsquos original concepts of trail blazing and associative indexing.
    Source
    Research and advanced technology for digital libraries : 7th European Conference, proceedings / ECDL 2003, Trondheim, Norway, August 17-22, 2003
    Type
    a
  11. Lund, K.; Burgess, C.; Atchley, R.A.: Semantic and associative priming in high-dimensional semantic space (1995) 0.03
    0.027640268 = product of:
      0.055280536 = sum of:
        0.055280536 = sum of:
          0.011600202 = weight(_text_:a in 2151) [ClassicSimilarity], result of:
            0.011600202 = score(doc=2151,freq=12.0), product of:
              0.053105544 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046056706 = queryNorm
              0.21843673 = fieldWeight in 2151, product of:
                3.4641016 = tf(freq=12.0), with freq of:
                  12.0 = termFreq=12.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.0546875 = fieldNorm(doc=2151)
          0.043680333 = weight(_text_:22 in 2151) [ClassicSimilarity], result of:
            0.043680333 = score(doc=2151,freq=2.0), product of:
              0.16128273 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046056706 = queryNorm
              0.2708308 = fieldWeight in 2151, 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=2151)
      0.5 = coord(1/2)
    
    Abstract
    We present a model of semantic memory that utilizes a high dimensional semantic space constructed from a co-occurrence matrix. This matrix was formed by analyzing a lot) million word corpus. Word vectors were then obtained by extracting rows and columns of this matrix, These vectors were subjected to multidimensional scaling. Words were found to cluster semantically. suggesting that interword distance may be interpretable as a measure of semantic similarity, In attempting to replicate with our simulation the semantic and ...
    Source
    Proceedings of the Seventeenth Annual Conference of the Cognitive Science Society: July 22 - 25, 1995, University of Pittsburgh / ed. by Johanna D. Moore and Jill Fain Lehmann
    Type
    a
  12. Mlodzka-Stybel, A.: Towards continuous improvement of users' access to a library catalogue (2014) 0.03
    0.026575929 = product of:
      0.053151857 = sum of:
        0.053151857 = sum of:
          0.009471525 = weight(_text_:a in 1466) [ClassicSimilarity], result of:
            0.009471525 = score(doc=1466,freq=8.0), product of:
              0.053105544 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046056706 = queryNorm
              0.17835285 = fieldWeight in 1466, product of:
                2.828427 = tf(freq=8.0), with freq of:
                  8.0 = termFreq=8.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.0546875 = fieldNorm(doc=1466)
          0.043680333 = weight(_text_:22 in 1466) [ClassicSimilarity], result of:
            0.043680333 = score(doc=1466,freq=2.0), product of:
              0.16128273 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046056706 = queryNorm
              0.2708308 = fieldWeight in 1466, 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=1466)
      0.5 = coord(1/2)
    
    Abstract
    The paper discusses the issue of increasing users' access to library records by their publication in Google. Data from the records, converted into html format, have been indexed by Google. The process covered basic formal description fields of the records, description of the content, supported with a thesaurus, as well as an abstract, if present in the record. In addition to monitoring the end users' statistics, the pilot testing covered visibility of library records in Google search results.
    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
    Type
    a
  13. Chang, C.-H.; Hsu, C.-C.: Integrating query expansion and conceptual relevance feedback for personalized Web information retrieval (1998) 0.03
    0.025941458 = product of:
      0.051882915 = sum of:
        0.051882915 = sum of:
          0.008202582 = weight(_text_:a in 1319) [ClassicSimilarity], result of:
            0.008202582 = score(doc=1319,freq=6.0), product of:
              0.053105544 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046056706 = queryNorm
              0.1544581 = fieldWeight in 1319, product of:
                2.4494898 = tf(freq=6.0), with freq of:
                  6.0 = termFreq=6.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.0546875 = fieldNorm(doc=1319)
          0.043680333 = weight(_text_:22 in 1319) [ClassicSimilarity], result of:
            0.043680333 = score(doc=1319,freq=2.0), product of:
              0.16128273 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046056706 = queryNorm
              0.2708308 = fieldWeight in 1319, 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=1319)
      0.5 = coord(1/2)
    
    Abstract
    Keyword based querying has been an immediate and efficient way to specify and retrieve related information that the user inquired. However, conventional document ranking based on an automatic assessment of document relevance to the query may not be the best approach when little information is given. Proposes an idea to integrate 2 existing techniques, query expansion and relevance feedback to achieve a concept-based information search for the Web
    Date
    1. 8.1996 22:08:06
    Footnote
    Contribution to a special issue devoted to the Proceedings of the 7th International World Wide Web Conference, held 14-18 April 1998, Brisbane, Australia
    Type
    a
  14. Salaba, A.; Zeng, M.L.: Extending the "Explore" user task beyond subject authority data into the linked data sphere (2014) 0.03
    0.025941458 = product of:
      0.051882915 = sum of:
        0.051882915 = sum of:
          0.008202582 = weight(_text_:a in 1465) [ClassicSimilarity], result of:
            0.008202582 = score(doc=1465,freq=6.0), product of:
              0.053105544 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046056706 = queryNorm
              0.1544581 = fieldWeight in 1465, product of:
                2.4494898 = tf(freq=6.0), with freq of:
                  6.0 = termFreq=6.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.0546875 = fieldNorm(doc=1465)
          0.043680333 = weight(_text_:22 in 1465) [ClassicSimilarity], result of:
            0.043680333 = score(doc=1465,freq=2.0), product of:
              0.16128273 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046056706 = queryNorm
              0.2708308 = fieldWeight in 1465, 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=1465)
      0.5 = coord(1/2)
    
    Abstract
    "Explore" is a user task introduced in the Functional Requirements for Subject Authority Data (FRSAD) final report. Through various case scenarios, the authors discuss how structured data, presented based on Linked Data principles and using knowledge organisation systems (KOS) as the backbone, extend the explore task within and beyond subject authority data.
    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
    Type
    a
  15. Knorz, G.; Rein, B.: Semantische Suche in einer Hochschulontologie (2005) 0.02
    0.024208048 = product of:
      0.048416097 = sum of:
        0.048416097 = sum of:
          0.0047357627 = weight(_text_:a in 1852) [ClassicSimilarity], result of:
            0.0047357627 = score(doc=1852,freq=2.0), product of:
              0.053105544 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046056706 = queryNorm
              0.089176424 = fieldWeight in 1852, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.0546875 = fieldNorm(doc=1852)
          0.043680333 = weight(_text_:22 in 1852) [ClassicSimilarity], result of:
            0.043680333 = score(doc=1852,freq=2.0), product of:
              0.16128273 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046056706 = queryNorm
              0.2708308 = fieldWeight in 1852, 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=1852)
      0.5 = coord(1/2)
    
    Date
    11. 2.2011 18:22:58
    Type
    a
  16. Klas, C.-P.; Fuhr, N.; Schaefer, A.: Evaluating strategic support for information access in the DAFFODIL system (2004) 0.02
    0.023258494 = product of:
      0.04651699 = sum of:
        0.04651699 = sum of:
          0.009076704 = weight(_text_:a in 2419) [ClassicSimilarity], result of:
            0.009076704 = score(doc=2419,freq=10.0), product of:
              0.053105544 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046056706 = queryNorm
              0.1709182 = fieldWeight in 2419, product of:
                3.1622777 = tf(freq=10.0), with freq of:
                  10.0 = termFreq=10.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046875 = fieldNorm(doc=2419)
          0.037440285 = weight(_text_:22 in 2419) [ClassicSimilarity], result of:
            0.037440285 = score(doc=2419,freq=2.0), product of:
              0.16128273 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046056706 = queryNorm
              0.23214069 = fieldWeight in 2419, 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=2419)
      0.5 = coord(1/2)
    
    Abstract
    The digital library system Daffodil is targeted at strategic support of users during the information search process. For searching, exploring and managing digital library objects it provides user-customisable information seeking patterns over a federation of heterogeneous digital libraries. In this paper evaluation results with respect to retrieval effectiveness, efficiency and user satisfaction are presented. The analysis focuses on strategic support for the scientific work-flow. Daffodil supports the whole work-flow, from data source selection over information seeking to the representation, organisation and reuse of information. By embedding high level search functionality into the scientific work-flow, the user experiences better strategic system support due to a more systematic work process. These ideas have been implemented in Daffodil followed by a qualitative evaluation. The evaluation has been conducted with 28 participants, ranging from information seeking novices to experts. The results are promising, as they support the chosen model.
    Date
    16.11.2008 16:22:48
    Type
    a
  17. Zeng, M.L.; Gracy, K.F.; Zumer, M.: Using a semantic analysis tool to generate subject access points : a study using Panofsky's theory and two research samples (2014) 0.02
    0.022779368 = product of:
      0.045558736 = sum of:
        0.045558736 = sum of:
          0.008118451 = weight(_text_:a in 1464) [ClassicSimilarity], result of:
            0.008118451 = score(doc=1464,freq=8.0), product of:
              0.053105544 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046056706 = queryNorm
              0.15287387 = fieldWeight in 1464, product of:
                2.828427 = tf(freq=8.0), with freq of:
                  8.0 = termFreq=8.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046875 = fieldNorm(doc=1464)
          0.037440285 = weight(_text_:22 in 1464) [ClassicSimilarity], result of:
            0.037440285 = score(doc=1464,freq=2.0), product of:
              0.16128273 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046056706 = queryNorm
              0.23214069 = fieldWeight in 1464, 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=1464)
      0.5 = coord(1/2)
    
    Abstract
    This paper attempts to explore an approach of using an automatic semantic analysis tool to enhance the "subject" access to materials that are not included in the usual library subject cataloging process. Using two research samples the authors analyzed the access points supplied by OpenCalais, a semantic analysis tool. As an aid in understanding how computerized subject analysis might be approached, this paper suggests using the three-layer framework that has been accepted and applied in image analysis, developed by Erwin Panofsky.
    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
    Type
    a
  18. Järvelin, K.; Kristensen, J.; Niemi, T.; Sormunen, E.; Keskustalo, H.: ¬A deductive data model for query expansion (1996) 0.02
    0.022779368 = product of:
      0.045558736 = sum of:
        0.045558736 = sum of:
          0.008118451 = weight(_text_:a in 2230) [ClassicSimilarity], result of:
            0.008118451 = score(doc=2230,freq=8.0), product of:
              0.053105544 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046056706 = queryNorm
              0.15287387 = fieldWeight in 2230, product of:
                2.828427 = tf(freq=8.0), with freq of:
                  8.0 = termFreq=8.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046875 = fieldNorm(doc=2230)
          0.037440285 = weight(_text_:22 in 2230) [ClassicSimilarity], result of:
            0.037440285 = score(doc=2230,freq=2.0), product of:
              0.16128273 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046056706 = queryNorm
              0.23214069 = fieldWeight in 2230, 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=2230)
      0.5 = coord(1/2)
    
    Abstract
    We present a deductive data model for concept-based query expansion. It is based on three abstraction levels: the conceptual, linguistic and occurrence levels. Concepts and relationships among them are represented at the conceptual level. The expression level represents natural language expressions for concepts. Each expression has one or more matching models at the occurrence level. Each model specifies the matching of the expression in database indices built in varying ways. The data model supports a concept-based query expansion and formulation tool, the ExpansionTool, for environments providing heterogeneous IR systems. Expansion is controlled by adjustable matching reliability.
    Source
    Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR '96), Zürich, Switzerland, August 18-22, 1996. Eds.: H.P. Frei et al
    Type
    a
  19. Song, D.; Bruza, P.D.: Towards context sensitive information inference (2003) 0.02
    0.021209672 = product of:
      0.042419344 = sum of:
        0.042419344 = sum of:
          0.011219106 = weight(_text_:a in 1428) [ClassicSimilarity], result of:
            0.011219106 = score(doc=1428,freq=22.0), product of:
              0.053105544 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046056706 = queryNorm
              0.21126054 = fieldWeight in 1428, product of:
                4.690416 = tf(freq=22.0), with freq of:
                  22.0 = termFreq=22.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.0390625 = fieldNorm(doc=1428)
          0.03120024 = weight(_text_:22 in 1428) [ClassicSimilarity], result of:
            0.03120024 = score(doc=1428,freq=2.0), product of:
              0.16128273 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046056706 = queryNorm
              0.19345059 = fieldWeight in 1428, 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=1428)
      0.5 = coord(1/2)
    
    Abstract
    Humans can make hasty, but generally robust judgements about what a text fragment is, or is not, about. Such judgements are termed information inference. This article furnishes an account of information inference from a psychologistic stance. By drawing an theories from nonclassical logic and applied cognition, an information inference mechanism is proposed that makes inferences via computations of information flow through an approximation of a conceptual space. Within a conceptual space information is represented geometrically. In this article, geometric representations of words are realized as vectors in a high dimensional semantic space, which is automatically constructed from a text corpus. Two approaches were presented for priming vector representations according to context. The first approach uses a concept combination heuristic to adjust the vector representation of a concept in the light of the representation of another concept. The second approach computes a prototypical concept an the basis of exemplar trace texts and moves it in the dimensional space according to the context. Information inference is evaluated by measuring the effectiveness of query models derived by information flow computations. Results show that information flow contributes significantly to query model effectiveness, particularly with respect to precision. Moreover, retrieval effectiveness compares favorably with two probabilistic query models, and another based an semantic association. More generally, this article can be seen as a contribution towards realizing operational systems that mimic text-based human reasoning.
    Date
    22. 3.2003 19:35:46
    Type
    a
  20. Brandão, W.C.; Santos, R.L.T.; Ziviani, N.; Moura, E.S. de; Silva, A.S. da: Learning to expand queries using entities (2014) 0.02
    0.020074995 = product of:
      0.04014999 = sum of:
        0.04014999 = sum of:
          0.00894975 = weight(_text_:a in 1343) [ClassicSimilarity], result of:
            0.00894975 = score(doc=1343,freq=14.0), product of:
              0.053105544 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046056706 = queryNorm
              0.1685276 = fieldWeight in 1343, product of:
                3.7416575 = tf(freq=14.0), with freq of:
                  14.0 = termFreq=14.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.0390625 = fieldNorm(doc=1343)
          0.03120024 = weight(_text_:22 in 1343) [ClassicSimilarity], result of:
            0.03120024 = score(doc=1343,freq=2.0), product of:
              0.16128273 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046056706 = queryNorm
              0.19345059 = fieldWeight in 1343, 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=1343)
      0.5 = coord(1/2)
    
    Abstract
    A substantial fraction of web search queries contain references to entities, such as persons, organizations, and locations. Recently, methods that exploit named entities have been shown to be more effective for query expansion than traditional pseudorelevance feedback methods. In this article, we introduce a supervised learning approach that exploits named entities for query expansion using Wikipedia as a repository of high-quality feedback documents. In contrast with existing entity-oriented pseudorelevance feedback approaches, we tackle query expansion as a learning-to-rank problem. As a result, not only do we select effective expansion terms but we also weigh these terms according to their predicted effectiveness. To this end, we exploit the rich structure of Wikipedia articles to devise discriminative term features, including each candidate term's proximity to the original query terms, as well as its frequency across multiple article fields and in category and infobox descriptors. Experiments on three Text REtrieval Conference web test collections attest the effectiveness of our approach, with gains of up to 23.32% in terms of mean average precision, 19.49% in terms of precision at 10, and 7.86% in terms of normalized discounted cumulative gain compared with a state-of-the-art approach for entity-oriented query expansion.
    Date
    22. 8.2014 17:07:50
    Type
    a

Years

Languages

  • e 221
  • d 34
  • f 2
  • chi 1
  • More… Less…

Types

  • a 235
  • el 25
  • m 14
  • r 4
  • p 2
  • x 2
  • s 1
  • More… Less…