Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G.: Das Entstehen von Semantik in BibSonomy. Social Software in der Wertschöpfung. Baden-Baden: Nomos, 2006
[Volltext]
Immer mehr Soziale-Lesezeichen-Systeme entstehen im heutigen Web. In solchen Systemen erstellen die Nutzer leichtgewichtige begriffliche Strukturen, so genannte Folksonomies. Ihren Erfolg verdanken sie der Tatsache, dass man keine speziellen Fähigkeiten benötigt, um an der Gestaltung mitzuwirken. In diesem Artikel beschreiben wir unser System BibSonomy. Es erlaubt das Speichern, Verwalten und Austauschen sowohl von Lesezeichen (Bookmarks) als auch von Literaturreferenzen in Form von BibTeX-Einträgen. Die Entwicklung des verwendeten Vokabulars und der damit einhergehenden Entstehung einer gemeinsamen Semantik wird detailliert diskutiert.

Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G.: FolkRank: A Ranking Algorithm for Folksonomies. Proc. FGIR 2006. 2006
[Volltext]
In social bookmark tools users are setting up
ghtweight conceptual structures called folksonomies. Currently,
e information retrieval support is limited. We present a formal
del and a new search algorithm for folksonomies, called
lkRank, that exploits the structure of the folksonomy. The
oposed algorithm is also applied to find communities within the
lksonomy and is used to structure search results. All findings are
monstrated on a large scale dataset. A long version of this paper
s been published at the European Semantic Web Conference
06.

Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G.: FolkRank: A Ranking Algorithm for Folksonomies. Proc. FGIR 2006. 2006
[Volltext]
In social bookmark tools users are setting up
ghtweight conceptual structures called folksonomies. Currently,
e information retrieval support is limited. We present a formal
del and a new search algorithm for folksonomies, called
lkRank, that exploits the structure of the folksonomy. The
oposed algorithm is also applied to find communities within the
lksonomy and is used to structure search results. All findings are
monstrated on a large scale dataset. A long version of this paper
s been published at the European Semantic Web Conference
06.

Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G.: Trend Detection in Folksonomies. In: Avrithis, Y. S.; Kompatsiaris, Y.; Staab, S. & O'Connor, N. E. (Hrsg.): Proc. First International Conference on Semantics And Digital Media Technology (SAMT) . Heidelberg: Springer, 2006 (LNCS 4306), S. 56-70
[Volltext]
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.

Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G.: Trend Detection in Folksonomies. In: Avrithis, Y. S.; Kompatsiaris, Y.; Staab, S. & O'Connor, N. E. (Hrsg.): Proc. First International Conference on Semantics And Digital Media Technology (SAMT) . Heidelberg: Springer, 2006 (LNCS 4306), S. 56-70
[Volltext]
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.