Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G. (2006),
FolkRank: A Ranking Algorithm for Folksonomies, in
'Proc. FGIR 2006'
.
[BibTeX]
[Endnote]
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.
Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G. (2006),
Information Retrieval in Folksonomies: Search and Ranking, in
York Sure & John Domingue, ed.,
'The Semantic Web: Research and Applications'
, Springer, Heidelberg
, pp. 411-426
.
[BibTeX]
[Endnote]
Hotho, A.; J�schke, R.; Schmitz, C. & Stumme, G. (2006),
Information Retrieval in Folksonomies: Search and Ranking, in
York Sure & John Domingue, ed.,
'The Semantic Web: Research and Applications'
, Springer, Heidelberg
, pp. 411-426
.
[BibTeX]
[Endnote]
Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G. (2006),
Trend Detection in Folksonomies, in
Yannis S. Avrithis; Yiannis Kompatsiaris; Steffen Staab & Noel E. O'Connor, ed.,
'Proc. First International Conference on Semantics And Digital Media Technology (SAMT) '
, Springer, Heidelberg
, pp. 56-70
.
[BibTeX]
[Endnote]
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.