| Authors: |
Andreas Hotho
and Robert Jäschke
and Christoph Schmitz
and Gerd Stumme
|
| Editors: |
Yannis S. Avrithis
and Yiannis Kompatsiaris
and Steffen Staab
and Noel E. O'Connor
|
| URL: |
http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006trend.pdf |
| Tags: |
2006
detection
folksonomy
l3s
myown
trend
|
| Abstract: |
As the number of resources on the web exceeds by far the number of documents one can track, it becomes increasingly difficult to remain up to date on ones own areas of interest. The problem becomes more severe with the increasing fraction of multimedia data, from which it is difficult to extract some conceptual description of their contents.
One way to overcome this problem are social bookmark tools, which are rapidly emerging on the web. In such systems, users are setting up lightweight conceptual structures called folksonomies, and overcome thus the knowledge acquisition bottleneck. As more and more people participate in the effort, the use of a common vocabulary becomes more and more stable. We present an approach for discovering topic-specific trends within folksonomies. It is based on a differential adaptation of the PageRank algorithm to the triadic hypergraph structure of a folksonomy. The approach allows for any kind of data, as it does not rely on the internal structure of the documents. In particular, this allows to consider different data types in the same analysis step. We run experiments on a large-scale real-world snapshot of a social bookmarking system. |
@inproceedings{hotho2006trend,
title = {Trend Detection in Folksonomies},
address = {Heidelberg},
author = {Andreas Hotho and Robert Jäschke and Christoph Schmitz and Gerd Stumme},
booktitle = {Proc. First International Conference on Semantics And Digital Media Technology (SAMT) },
editor = {Yannis S. Avrithis and Yiannis Kompatsiaris and Steffen Staab and Noel E. O'Connor},
month = {dec},
pages = {56-70},
publisher = {Springer},
series = {LNCS},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006trend.pdf},
volume = {4306},
year = {2006},
abstract = {As the number of resources on the web exceeds by far the number of documents one can track, it becomes increasingly difficult to remain up to date on ones own areas of interest. The problem becomes more severe with the increasing fraction of multimedia data, from which it is difficult to extract some conceptual description of their contents.
One way to overcome this problem are social bookmark tools, which are rapidly emerging on the web. In such systems, users are setting up lightweight conceptual structures called folksonomies, and overcome thus the knowledge acquisition bottleneck. As more and more people participate in the effort, the use of a common vocabulary becomes more and more stable. We present an approach for discovering topic-specific trends within folksonomies. It is based on a differential adaptation of the PageRank algorithm to the triadic hypergraph structure of a folksonomy. The approach allows for any kind of data, as it does not rely on the internal structure of the documents. In particular, this allows to consider different data types in the same analysis step. We run experiments on a large-scale real-world snapshot of a social bookmarking system.},
ee = {http://dx.doi.org/10.1007/11930334_5}, isbn = {3-540-49335-2}, date = {2006-12-13}, vgwort = {27},
keywords = {2006 detection folksonomy l3s myown trend }
}
::