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AuthorTitleYearJournal/ProceedingsReftypeDOI/URL
Hotho, A., Jäschke, R., Schmitz, C. & Stumme, G. Trend Detection in Folksonomies 2006 Proc. First International Conference on Semantics And Digital Media Technology (SAMT)   inproceedings URL  
Abstract: As the number of resources on the web exceeds by far the number ofdocuments one can track, it becomes increasingly difficult to remainup to date on ones own areas of interest. The problem becomes moresevere with the increasing fraction of multimedia data, from whichit is difficult to extract some conceptual description of theircontents.One way to overcome this problem are social bookmark tools, whichare rapidly emerging on the web. In such systems, users are settingup lightweight conceptual structures called folksonomies, andovercome thus the knowledge acquisition bottleneck. As more and morepeople participate in the effort, the use of a common vocabularybecomes more and more stable. We present an approach for discoveringtopic-specific trends within folksonomies. It is based on adifferential adaptation of the PageRank algorithm to the triadichypergraph structure of a folksonomy. The approach allows for anykind of data, as it does not rely on the internal structure of thedocuments. In particular, this allows to consider different datatypes in the same analysis step. We run experiments on a large-scalereal-world snapshot of a social bookmarking system.
BibTeX:
@inproceedings{hotho2006trend,
  author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd},
  title = {Trend Detection in Folksonomies},
  booktitle = {Proc. First International Conference on Semantics And Digital Media Technology (SAMT) },
  publisher = {Springer},
  year = {2006},
  volume = {4306},
  pages = {56-70},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006trend.pdf}
}
Hotho, A., Jäschke, R., Schmitz, C. & Stumme, G. Trend Detection in Folksonomies 2006 Proc. First International Conference on Semantics And Digital Media Technology (SAMT)   inproceedings URL  
Abstract: As the number of resources on the web exceeds by far the number ofdocuments one can track, it becomes increasingly difficult to remainup to date on ones own areas of interest. The problem becomes moresevere with the increasing fraction of multimedia data, from whichit is difficult to extract some conceptual description of theircontents.One way to overcome this problem are social bookmark tools, whichare rapidly emerging on the web. In such systems, users are settingup lightweight conceptual structures called folksonomies, andovercome thus the knowledge acquisition bottleneck. As more and morepeople participate in the effort, the use of a common vocabularybecomes more and more stable. We present an approach for discoveringtopic-specific trends within folksonomies. It is based on adifferential adaptation of the PageRank algorithm to the triadichypergraph structure of a folksonomy. The approach allows for anykind of data, as it does not rely on the internal structure of thedocuments. In particular, this allows to consider different datatypes in the same analysis step. We run experiments on a large-scalereal-world snapshot of a social bookmarking system.
BibTeX:
@inproceedings{hotho2006trend,
  author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd},
  title = {Trend Detection in Folksonomies},
  booktitle = {Proc. First International Conference on Semantics And Digital Media Technology (SAMT) },
  publisher = {Springer},
  year = {2006},
  volume = {4306},
  pages = {56-70},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006trend.pdf}
}
Hotho, A., Jäschke, R., Schmitz, C. & Stumme, G. Trend Detection in Folksonomies 2006 Proc. First International Conference on Semantics And Digital Media Technology (SAMT)   inproceedings URL  
Abstract: As the number of resources on the web exceeds by far the number of
cuments one can track, it becomes increasingly difficult to remain
to date on ones own areas of interest. The problem becomes more
vere with the increasing fraction of multimedia data, from which
is difficult to extract some conceptual description of their
ntents.

ne way to overcome this problem are social bookmark tools, which
e rapidly emerging on the web. In such systems, users are setting
lightweight conceptual structures called folksonomies, and
ercome thus the knowledge acquisition bottleneck. As more and more
ople participate in the effort, the use of a common vocabulary
comes more and more stable. We present an approach for discovering
pic-specific trends within folksonomies. It is based on a
fferential adaptation of the PageRank algorithm to the triadic
pergraph structure of a folksonomy. The approach allows for any
nd of data, as it does not rely on the internal structure of the
cuments. In particular, this allows to consider different data
pes in the same analysis step. We run experiments on a large-scale
al-world snapshot of a social bookmarking system.

BibTeX:
@inproceedings{hotho2006trend,
  author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd},
  title = {Trend Detection in Folksonomies},
  booktitle = {Proc. First International Conference on Semantics And Digital Media Technology (SAMT) },
  publisher = {Springer},
  year = {2006},
  volume = {4306},
  pages = {56-70},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006trend.pdf}
}

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