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Web-2.0-Dienste als Ergänzung zu algorithmischen Suchmaschinen (2008)
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- 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)
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Niemann, C.: Tag-Science : Ein Analysemodell zur Nutzbarkeit von Tagging-Daten (2011)
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Farkas, M.G.: Social software in libraries : building collaboration, communication, and community online (2007)
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- Source
- Categories, contexts and relations in knowledge organization: Proceedings of the Twelfth International ISKO Conference 6-9 August 2012, Mysore, India. Eds.: Neelameghan, A. u. K.S. Raghavan
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Golub, K.; Lykke, M.; Tudhope, D.: Enhancing social tagging with automated keywords from the Dewey Decimal Classification (2014)
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Munk, T.B.; Moerk, K.: Folksonomies, tagging communities, and tagging strategies : an empirical study (2007)
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Chopin, K.: Finding communities : alternative viewpoints through weblogs and tagging (2008)
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Yi, K.: ¬A semantic similarity approach to predicting Library of Congress subject headings for social tags (2010)
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Sun, A.; Bhowmick, S.S.; Nguyen, K.T.N.; Bai, G.: Tag-based social image retrieval : an empirical evaluation (2011)
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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).
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Yoon, K.: Conceptual syntagmatic associations in user tagging (2012)
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Knautz, K.; Stock, W.G.: Collective indexing of emotions in videos (2011)
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