Search (51 results, page 1 of 3)

  • × theme_ss:"Semantisches Umfeld in Indexierung u. Retrieval"
  1. Boyack, K.W.; Wylie,B.N.; Davidson, G.S.: Information Visualization, Human-Computer Interaction, and Cognitive Psychology : Domain Visualizations (2002) 0.08
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    Date
    22. 2.2003 17:25:39
    22. 2.2003 18:17:40
    Source
    Visual Interfaces to Digital Libraries. Eds.: Börner, K. u. C. Chen
  2. Knorz, G.; Rein, B.: Semantische Suche in einer Hochschulontologie (2005) 0.05
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    Abstract
    Ontologien werden eingesetzt, um durch semantische Fundierung insbesondere für das Dokumentenretrieval eine grundlegend bessere Basis zu haben, als dies gegenwärtiger Stand der Technik ist. Vorgestellt wird eine an der FH Darmstadt entwickelte und eingesetzte Ontologie, die den Gegenstandsbereich Hochschule sowohl breit abdecken und gleichzeitig differenziert semantisch beschreiben soll. Das Problem der semantischen Suche besteht nun darin, dass sie für Informationssuchende so einfach wie bei gängigen Suchmaschinen zu nutzen sein soll, und gleichzeitig auf der Grundlage des aufwendigen Informationsmodells hochwertige Ergebnisse liefern muss. Es wird beschrieben, welche Möglichkeiten die verwendete Software K-Infinity bereitstellt und mit welchem Konzept diese Möglichkeiten für eine semantische Suche nach Dokumenten und anderen Informationseinheiten (Personen, Veranstaltungen, Projekte etc.) eingesetzt werden.
    Date
    11. 2.2011 18:22:58
    Object
    K-Infinity
  3. Knorz, G.; Rein, B.: Semantische Suche in einer Hochschulontologie : Ontologie-basiertes Information-Filtering und -Retrieval mit relationalen Datenbanken (2005) 0.05
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    Abstract
    Ontologien werden eingesetzt, um durch semantische Fundierung insbesondere für das Dokumentenretrieval eine grundlegend bessere Basis zu haben, als dies gegenwärtiger Stand der Technik ist. Vorgestellt wird eine an der FH Darmstadt entwickelte und eingesetzte Ontologie, die den Gegenstandsbereich Hochschule sowohl breit abdecken und gleichzeitig differenziert semantisch beschreiben soll. Das Problem der semantischen Suche besteht nun darin, dass sie für Informationssuchende so einfach wie bei gängigen Suchmaschinen zu nutzen sein soll, und gleichzeitig auf der Grundlage des aufwendigen Informationsmodells hochwertige Ergebnisse liefern muss. Es wird beschrieben, welche Möglichkeiten die verwendete Software K-Infinity bereitstellt und mit welchem Konzept diese Möglichkeiten für eine semantische Suche nach Dokumenten und anderen Informationseinheiten (Personen, Veranstaltungen, Projekte etc.) eingesetzt werden.
    Date
    11. 2.2011 18:22:25
    Object
    K-Infinity
  4. Lund, K.; Burgess, C.; Atchley, R.A.: Semantic and associative priming in high-dimensional semantic space (1995) 0.04
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    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
  5. Järvelin, K.; Kristensen, J.; Niemi, T.; Sormunen, E.; Keskustalo, H.: ¬A deductive data model for query expansion (1996) 0.04
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    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
  6. Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983) 0.02
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    Date
    30. 3.2001 13:32:22
  7. Gauch, S.; Chong, M.K.: Automatic word similarity detection for TREC 4 query expansion (1996) 0.02
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    Source
    The Fourth Text Retrieval Conference (TREC-4). Ed.: K. Harman
  8. Smeaton, A.F.; Kelledy, L.; O'Donnell, R.: TREC-4 experiments at Dublin City University : thresholding posting lists, query expansion with WordNet and POS tagging of Spanish (1996) 0.02
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    Source
    The Fourth Text Retrieval Conference (TREC-4). Ed.: K. Harman
  9. Rekabsaz, N. et al.: Toward optimized multimodal concept indexing (2016) 0.02
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    Date
    1. 2.2016 18:25:22
  10. Kozikowski, P. et al.: Support of part-whole relations in query answering (2016) 0.02
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    Date
    1. 2.2016 18:25:22
  11. Marx, E. et al.: Exploring term networks for semantic search over RDF knowledge graphs (2016) 0.02
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    Source
    Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou
  12. Kopácsi, S. et al.: Development of a classification server to support metadata harmonization in a long term preservation system (2016) 0.02
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    Source
    Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou
  13. Sacco, G.M.: Dynamic taxonomies and guided searches (2006) 0.02
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    Date
    22. 7.2006 17:56:22
  14. Jarvelin, K.: ¬A deductive data model for thesaurus navigation and query expansion (1996) 0.01
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  15. Efthimiadis, E.N.: End-users' understanding of thesaural knowledge structures in interactive query expansion (1994) 0.01
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    Date
    30. 3.2001 13:35:22
  16. Morato, J.; Llorens, J.; Genova, G.; Moreiro, J.A.: Experiments in discourse analysis impact on information classification and retrieval algorithms (2003) 0.01
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    Abstract
    Researchers in indexing and retrieval systems have been advocating the inclusion of more contextual information to improve results. The proliferation of full-text databases and advances in computer storage capacity have made it possible to carry out text analysis by means of linguistic and extra-linguistic knowledge. Since the mid 80s, research has tended to pay more attention to context, giving discourse analysis a more central role. The research presented in this paper aims to check whether discourse variables have an impact on modern information retrieval and classification algorithms. In order to evaluate this hypothesis, a functional framework for information analysis in an automated environment has been proposed, where the n-grams (filtering) and the k-means and Chen's classification algorithms have been tested against sub-collections of documents based on the following discourse variables: "Genre", "Register", "Domain terminology", and "Document structure". The results obtained with the algorithms for the different sub-collections were compared to the MeSH information structure. These demonstrate that n-grams does not appear to have a clear dependence on discourse variables, though the k-means classification algorithm does, but only on domain terminology and document structure, and finally Chen's algorithm has a clear dependence on all of the discourse variables. This information could be used to design better classification algorithms, where discourse variables should be taken into account. Other minor conclusions drawn from these results are also presented.
  17. Baofu, P.: ¬The future of information architecture : conceiving a better way to understand taxonomy, network, and intelligence (2008) 0.01
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    Classification
    BBV (FH K)
    GHBS
    BBV (FH K)
  18. Nagao, M.: Knowledge and inference (1990) 0.01
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    Classification
    TXX (FH K)
    GHBS
    TXX (FH K)
  19. Gödert, W.; Lepsky, K.: Semantische Umfeldsuche im Information Retrieval (1998) 0.01
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Years

Languages

  • e 43
  • d 7

Types

Classifications