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.

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.

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.