Search (15 results, page 1 of 1)

  • × language_ss:"e"
  • × theme_ss:"Semantisches Umfeld in Indexierung u. Retrieval"
  • × theme_ss:"Wissensrepräsentation"
  1. Järvelin, K.; Kristensen, J.; Niemi, T.; Sormunen, E.; Keskustalo, H.: ¬A deductive data model for query expansion (1996) 0.04
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    Pages
    S.235-243
    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
  2. Baofu, P.: ¬The future of information architecture : conceiving a better way to understand taxonomy, network, and intelligence (2008) 0.02
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    Classification
    BBV (FH K)
    GHBS
    BBV (FH K)
    Pages
    XX, 283 S
  3. Mäkelä, E.; Hyvönen, E.; Saarela, S.; Vilfanen, K.: Application of ontology techniques to view-based semantic serach and browsing (2012) 0.01
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    Abstract
    We scho how the beenfits of the view-based search method, developed within the information retrieval community, can be extended with ontology-based search, developed within the Semantic Web community, and with semantic recommendations. As a proof of the concept, we have implemented an ontology-and view-based search engine and recommendations system Ontogaotr for RDF(S) repositories. Ontogator is innovative in two ways. Firstly, the RDFS.based ontologies used for annotating metadata are used in the user interface to facilitate view-based information retrieval. The views provide the user with an overview of the repositorys contents and a vocabulary for expressing search queries. Secondlyy, a semantic browsing function is provided by a recommender system. This system enriches instance level metadata by ontologies and provides the user with links to semantically related relevant resources. The semantic linkage is specified in terms of logical rules. To illustrate and discuss the ideas, a deployed application of Ontogator to a photo repository of the Helsinki University Museum is presented.
  4. Thenmalar, S.; Geetha, T.V.: Enhanced ontology-based indexing and searching (2014) 0.01
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    Date
    20. 1.2015 18:30:22
    Source
    Aslib journal of information management. 66(2014) no.6, S.678-696
  5. Wang, Y.-H.; Jhuo, P.-S.: ¬A semantic faceted search with rule-based inference (2009) 0.00
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    Pages
    S.1002-1008
  6. Calegari, S.; Sanchez, E.: Object-fuzzy concept network : an enrichment of ontologies in semantic information retrieval (2008) 0.00
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    Source
    Journal of the American Society for Information Science and Technology. 59(2008) no.13, S.2171-2185
  7. Green, R.: See-also relationships in the Dewey Decimal Classification (2011) 0.00
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    Source
    Knowledge organization. 38(2011) no.4, S.335-341
  8. Atanassova, I.; Bertin, M.: Semantic facets for scientific information retrieval (2014) 0.00
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    Pages
    S.108-113
  9. Moreira, W.; Martínez-Ávila, D.: Concept relationships in knowledge organization systems : elements for analysis and common research among fields (2018) 0.00
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    Source
    Cataloging and classification quarterly. 56(2018) no.1, S.19-39
  10. Drexel, G.: Knowledge engineering for intelligent information retrieval (2001) 0.00
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    Pages
    S.495-504
  11. Vallet, D.; Fernández, M.; Castells, P.: ¬An ontology-based information retrieval model (2005) 0.00
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    Pages
    S.455-470
  12. Arenas, M.; Cuenca Grau, B.; Kharlamov, E.; Marciuska, S.; Zheleznyakov, D.: Faceted search over ontology-enhanced RDF data (2014) 0.00
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  13. Cao, N.; Sun, J.; Lin, Y.-R.; Gotz, D.; Liu, S.; Qu, H.: FacetAtlas : Multifaceted visualization for rich text corpora (2010) 0.00
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  14. Koopman, B.; Zuccon, G.; Bruza, P.; Sitbon, L.; Lawley, M.: Information retrieval as semantic inference : a graph Inference model applied to medical search (2016) 0.00
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    Source
    Information Retrieval Journal. 19(2016) no.1, S.6-37
  15. Sebastian, Y.: Literature-based discovery by learning heterogeneous bibliographic information networks (2017) 0.00
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    Pages
    xix, 215 S