Sergieh, Hatem Mousselly ; Döller, Mario ; Egyed-Zsigmond, Elöd ; Gianini, Gabriele ; Kosch, Harald ; 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, pp. 159-171,
Springer International Publishing, Dublin, Ireland, DOI: 10.1007/978-3-319-04114-8_14,
[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 |
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Erschienen: | 2014 |
Creators: | Sergieh, Hatem Mousselly ; Döller, Mario ; Egyed-Zsigmond, Elöd ; Gianini, Gabriele ; Kosch, Harald ; 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. |
Book Title: | 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 |
URL / URN: | https://link.springer.com/chapter/10.1007%2F978-3-319-04114-... |
Identification Number: | TUD-CS-2014-1075 |
PPN: | |
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