Artikel in Tagungsbänden
FolkRank: A Ranking Algorithm for Folksonomies.
In:
Proc. FGIR 2006.
2006.
Andreas Hotho, Robert Jäschke, Christoph Schmitz und Gerd Stumme.
[doi]
[Kurzfassung]
[BibTeX]
In social bookmark tools users are setting up
lightweight conceptual structures called folksonomies. Currently,
the information retrieval support is limited. We present a formal
model and a new search algorithm for folksonomies, called
FolkRank, that exploits the structure of the folksonomy. The
proposed algorithm is also applied to find communities within the
folksonomy and is used to structure search results. All findings are
demonstrated on a large scale dataset. A long version of this paper
has been published at the European Semantic Web Conference
2006.
Information Retrieval in Folksonomies: Search and Ranking.
In: Y. Sure und J. Domingue
(Herausgeber):
The Semantic Web: Research and Applications, Band 4011, Reihe LNAI, Seiten 411-426.
Springer, Heidelberg, 2006.
Andreas Hotho, Robert J?schke, Christoph Schmitz und Gerd Stumme.
[BibTeX]
Information Retrieval in Folksonomies: Search and Ranking.
In: Y. Sure und J. Domingue
(Herausgeber):
The Semantic Web: Research and Applications, Band 4011, Reihe LNAI, Seiten 411-426.
Springer, Heidelberg, 2006.
Andreas Hotho, Robert Jäschke, Christoph Schmitz und Gerd Stumme.
[BibTeX]
Trend Detection in Folksonomies.
In: Y. S. Avrithis, Y. Kompatsiaris, S. Staab und N. E. O'Connor
(Herausgeber):
Proc. First International Conference on Semantics And Digital Media Technology (SAMT) , Band 4306, Reihe LNCS, Seiten 56-70.
Springer, Heidelberg, 2006.
Andreas Hotho, Robert Jäschke, Christoph Schmitz und Gerd Stumme.
[doi]
[Kurzfassung]
[BibTeX]
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.