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Tag Relatedness Using Laplacian Score Feature Selection and Adapted Jensen-Shannon Divergence

Sergieh, Hatem Mousselly and Döller, Mario and Egyed-Zsigmond, Elöd and Gianini, Gabriele and Kosch, Harald and Pinon, Jean-Marie (2014):
Tag Relatedness Using Laplacian Score Feature Selection and Adapted Jensen-Shannon Divergence.
In: MultiMedia Modeling - 20th Anniversary International Conference, MMM 2014, Dublin, Ireland, January 6-10, 2014, Proceedings, Part I, Springer International Publishing, Dublin, Ireland, DOI: 10.1007/978-3-319-04114-8_14,
[Online-Edition: https://link.springer.com/chapter/10.1007%2F978-3-319-04114-...],
[Conference or Workshop Item]

Abstract

Folksonomies - networks of users, resources, and tags allow users to easily retrieve, organize and browse web contents. However, their advantages are still limited according to the noisiness of user provided tags. To overcome this problem, we propose an approach for identifying related tags in folksonomies. The approach uses tag co-occurrence statistics and Laplacian score feature selection to create probability distribution for each tag. Consequently, related tags are determined according to the distance between their distributions. In this regards, we propose a distance metric based on Jensen-Shannon Divergence. The new metric named AJSD deals with the noise in the measurements due to statistical fluctuations in tag co-occurrences. We experimentally evaluated our approach using WordNet and compared 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 adversary method.

Item Type: Conference or Workshop Item
Erschienen: 2014
Creators: Sergieh, Hatem Mousselly and Döller, Mario and Egyed-Zsigmond, Elöd and Gianini, Gabriele and Kosch, Harald and Pinon, Jean-Marie
Title: Tag Relatedness Using Laplacian Score Feature Selection and Adapted Jensen-Shannon Divergence
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 according to the noisiness of user provided tags. To overcome this problem, we propose an approach for identifying related tags in folksonomies. The approach uses tag co-occurrence statistics and Laplacian score feature selection to create probability distribution for each tag. Consequently, related tags are determined according to the distance between their distributions. In this regards, we propose a distance metric based on Jensen-Shannon Divergence. The new metric named AJSD deals with the noise in the measurements due to statistical fluctuations in tag co-occurrences. We experimentally evaluated our approach using WordNet and compared 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 adversary method.

Title of Book: MultiMedia Modeling - 20th Anniversary International Conference, MMM 2014, Dublin, Ireland, January 6-10, 2014, Proceedings, Part I
Publisher: Springer International Publishing
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Ubiquitous Knowledge Processing
Event Location: Dublin, Ireland
Date Deposited: 31 Dec 2016 14:29
DOI: 10.1007/978-3-319-04114-8_14
Official URL: https://link.springer.com/chapter/10.1007%2F978-3-319-04114-...
Identification Number: TUD-CS-2014-1075
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