Search (35 results, page 1 of 2)

  • × theme_ss:"Social tagging"
  1. Kruk, S.R.; Kruk, E.; Stankiewicz, K.: Evaluation of semantic and social technologies for digital libraries (2009) 0.04 
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    Date
    1. 8.2010 12:35:22
  2. Yi, K.: Harnessing collective intelligence in social tagging using Delicious (2012) 0.03 
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    Date
    25.12.2012 15:22:37
  3. Tonkin, E.; Baptista, A.A.; Hooland, S. van; Resmini, A.; Mendéz, E.; Neville, L.: Kinds of Tags : a collaborative research study on tag usage and structure (2007) 0.02 
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  4. Farkas, M.G.: Social software in libraries : building collaboration, communication, and community online (2007) 0.02 
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    Classification
    BBK (FH K)
    GHBS
    BBK (FH K)
  5. Regulski, K.: Aufwand und Nutzen beim Einsatz von Social-Bookmarking-Services als Nachweisinstrument für wissenschaftliche Forschungsartikel am Beispiel von BibSonomy (2007) 0.02 
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  6. Hidderley, R.; Rafferty, P.: Flickr and democratic indexing : disciplining desire lines (2006) 0.01 
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    Source
    Knowledge organization for a global learning society: Proceedings of the 9th International ISKO Conference, 4-7 July 2006, Vienna, Austria. Hrsg.: G. Budin, C. Swertz u. K. Mitgutsch
  7. Golub, K.; Moon, J.; Nielsen, M.L.; Tudhope, D.: EnTag: Enhanced Tagging for Discovery (2008) 0.01 
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  8. Tschetschonig, K.; Ladengruber, R.; Hampel, T.; Schulte, J.: Kollaborative Tagging-Systeme im Electronic Commerce (2008) 0.01 
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  9. Estrada, L.M.; Hildebrand, M.; Boer, V. de; Ossenbruggen, J. van: Time-based tags for fiction movies : comparing experts to novices using a video labeling game (2017) 0.01 
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  10. Web-2.0-Dienste als Ergänzung zu algorithmischen Suchmaschinen (2008) 0.01 
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    Content
    Das Buchprojekt entstand im Rahmen des Forschungsprojektes Theseus (Teilprojekt Alexandria). - Als Online Publikation vgl.: http://www.bui.haw-hamburg.de/fileadmin/user_upload/lewandowski/Web20-Buch/lewandowski-maass.pdf. Rez. in: ZfBB 56(2009) H.2, S.134-135 (K. Lepsky)
  11. Komus, A.; Wauch, F.: Wikimanagement : was Unternehmen von Social-Software und Web 2.0 lernen können (2008) 0.01 
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    Classification
    TVK (FH K)
    GHBS
    TVK (FH K)
  12. Golub, K.; Lykke, M.; Tudhope, D.: Enhancing social tagging with automated keywords from the Dewey Decimal Classification (2014) 0.01 
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  13. Munk, T.B.; Moerk, K.: Folksonomies, tagging communities, and tagging strategies : an empirical study (2007) 0.01 
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  14. Chopin, K.: Finding communities : alternative viewpoints through weblogs and tagging (2008) 0.01 
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  15. Simon, J.: Interdisciplinary knowledge creation : using wikis in science (2006) 0.01 
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    Source
    Knowledge organization for a global learning society: Proceedings of the 9th International ISKO Conference, 4-7 July 2006, Vienna, Austria. Hrsg.: G. Budin, C. Swertz u. K. Mitgutsch
  16. Yi, K.: ¬A semantic similarity approach to predicting Library of Congress subject headings for social tags (2010) 0.01 
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  17. Matthews, B.; Jones, C.; Puzon, B.; Moon, J.; Tudhope, D.; Golub, K.; Nielsen, M.L.: ¬An evaluation of enhancing social tagging with a knowledge organization system (2010) 0.01 
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  18. Sun, A.; Bhowmick, S.S.; Nguyen, K.T.N.; Bai, G.: Tag-based social image retrieval : an empirical evaluation (2011) 0.01 
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    Abstract
    Tags associated with social images are valuable information source for superior image search and retrieval experiences. Although various heuristics are valuable to boost tag-based search for images, there is a lack of general framework to study the impact of these heuristics. Specifically, the task of ranking images matching a given tag query based on their associated tags in descending order of relevance has not been well studied. In this article, we take the first step to propose a generic, flexible, and extensible framework for this task and exploit it for a systematic and comprehensive empirical evaluation of various methods for ranking images. To this end, we identified five orthogonal dimensions to quantify the matching score between a tagged image and a tag query. These five dimensions are: (i) tag relatedness to measure the degree of effectiveness of a tag describing the tagged image; (ii) tag discrimination to quantify the degree of discrimination of a tag with respect to the entire tagged image collection; (iii) tag length normalization analogous to document length normalization in web search; (iv) tag-query matching model for the matching score computation between an image tag and a query tag; and (v) query model for tag query rewriting. For each dimension, we identify a few implementations and evaluate their impact on NUS-WIDE dataset, the largest human-annotated dataset consisting of more than 269K tagged images from Flickr. We evaluated 81 single-tag queries and 443 multi-tag queries over 288 search methods and systematically compare their performances using standard metrics including Precision at top-K, Mean Average Precision (MAP), Recall, and Normalized Discounted Cumulative Gain (NDCG).
  19. Yoon, K.: Conceptual syntagmatic associations in user tagging (2012) 0.01 
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  20. Knautz, K.; Stock, W.G.: Collective indexing of emotions in videos (2011) 0.01 
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