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Tag relatedness in image folksonomies

Sergieh, Hatem Mousselly and Egyed-Zsigmond, Elöd and Gianini, Gabriele and Döller, Mario and Pinon, Jean-Marie and Kosch, Harald (2014):
Tag relatedness in image folksonomies.
In: Document numérique, pp. 33-54, 17, (2), DOI: 10.3166/DN.17.2.33-54,
[Online-Edition: https://dn.revuesonline.com/article.jsp?articleId=19707],
[Article]

Abstract

Folksonomies - networks of users, resources, and tags allow users to easily retrieve, organize and browse web contents. However, their advantages are still limited mainly due to the noisiness of user provided tags. To overcome this issue, we propose an approach for characterizing related tags in folksonomies: we use tag co-occurrence statistics and Laplacian score based feature selection in order to create empirical co-occurrence probability distribution for each tag; then we identify related tags on the basis of the dissimilarity between their distributions. For this purpose, we introduce variant of the Jensen-Shannon Divergence, which is more robust to statistical noise. We experimentally evaluate our approach using WordNet and compare it to a common tag-relatedness approach based on the cosine similarity. The results show the effectiveness of our approach and its advantage over the competing method.

Item Type: Article
Erschienen: 2014
Creators: Sergieh, Hatem Mousselly and Egyed-Zsigmond, Elöd and Gianini, Gabriele and Döller, Mario and Pinon, Jean-Marie and Kosch, Harald
Title: Tag relatedness in image folksonomies
Language: English
Abstract:

Folksonomies - networks of users, resources, and tags allow users to easily retrieve, organize and browse web contents. However, their advantages are still limited mainly due to the noisiness of user provided tags. To overcome this issue, we propose an approach for characterizing related tags in folksonomies: we use tag co-occurrence statistics and Laplacian score based feature selection in order to create empirical co-occurrence probability distribution for each tag; then we identify related tags on the basis of the dissimilarity between their distributions. For this purpose, we introduce variant of the Jensen-Shannon Divergence, which is more robust to statistical noise. We experimentally evaluate our approach using WordNet and compare it to a common tag-relatedness approach based on the cosine similarity. The results show the effectiveness of our approach and its advantage over the competing method.

Journal or Publication Title: Document numérique
Volume: 17
Number: 2
ISBN: 9782746246683
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Ubiquitous Knowledge Processing
Date Deposited: 31 Dec 2016 14:29
DOI: 10.3166/DN.17.2.33-54
Official URL: https://dn.revuesonline.com/article.jsp?articleId=19707
Identification Number: TUD-CS-2014-1076
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