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.
Auch erschienen in: Avrithis, Yannis u.a. (Hrsg.): Semantic multimedia. (Lecture notes in computer science ; 4306). Berlin u.a. : Springer, 2006. S. 56-70. ISBN 3-540-49335-2 = 978-3-540-49335-8 (The original publication is available at www.springerlink.com)