systems users are setting up lightweight conceptual structures
called folksonomies. The reason for their immediate success is the
fact that no specific skills are needed for participating. In this
paper we specify a formal model for folksonomies, briefly describe
our own system BibSonomy,
which allows for sharing both bookmarks and
publication references,
and discuss first steps towards emergent semantics. ER - TY - CONF AU - Hotho, Andreas AU - Jäschke, Robert AU - Schmitz, Christoph AU - Stumme, Gerd A2 - Avrithis, Yannis S. A2 - Kompatsiaris, Yiannis A2 - Staab, Steffen A2 - O'Connor, Noel E. T1 - Trend Detection in Folksonomies T2 - Proc. First International Conference on Semantics And Digital Media Technology (SAMT) PB - Springer C1 - Heidelberg PY - 2006/12 CY - VL - 4306 IS - SP - 56 EP - 70 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006trend.pdf DO - KW - 2006 KW - UniK KW - detection KW - folkrank KW - folksonomy KW - hotho KW - intranet KW - itegpub KW - jaeschke KW - l3s KW - myown KW - nepomuk KW - pagerank KW - schmitz KW - stumme KW - tagorapub KW - trend KW - triadic L1 - SN - 3-540-49335-2 N1 - N1 - AB - 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. ER - TY - CONF AU - Hotho, Andreas AU - Jäschke, Robert AU - Schmitz, Christoph AU - Stumme, Gerd A2 - Avrithis, Yannis S. A2 - Kompatsiaris, Yiannis A2 - Staab, Steffen A2 - O'Connor, Noel E. T1 - Trend Detection in Folksonomies T2 - Proc. First International Conference on Semantics And Digital Media Technology (SAMT) PB - Springer C1 - Heidelberg PY - 2006/12 CY - VL - 4306 IS - SP - 56 EP - 70 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006trend.pdf DO - KW - intranet KW - 2006 KW - trend KW - pagerank KW - hotho KW - schmitz KW - jaeschke KW - l3s KW - itegpub KW - detection KW - triadic KW - stumme KW - nepomuk KW - folksonomy KW - tagorapub KW - folkrank KW - UniK L1 - SN - 3-540-49335-2 N1 - N1 - AB - 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. ER - TY - CONF AU - Hotho, Andreas AU - Jäschke, Robert AU - Schmitz, Christoph AU - Stumme, Gerd A2 - Avrithis, Yannis S. A2 - Kompatsiaris, Yiannis A2 - Staab, Steffen A2 - O'Connor, Noel E. T1 - Trend Detection in Folksonomies T2 - Proc. First International Conference on Semantics And Digital Media Technology (SAMT) PB - Springer C1 - Heidelberg PY - 2006/12 CY - VL - 4306 IS - SP - 56 EP - 70 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006trend.pdf DO - KW - 2006 KW - UniK KW - detection KW - folkrank KW - folksonomy KW - hotho KW - intranet KW - itegpub KW - jaeschke KW - l3s KW - myown KW - nepomuk KW - pagerank KW - schmitz KW - stumme KW - tagorapub KW - trend KW - triadic L1 - SN - 3-540-49335-2 N1 - N1 - AB - 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. ER -