Social resource sharing systems are central elements
of theWeb 2.0 and use the same kind of lightweight knowledge
representation, called folksonomy. Their large user communities
and ever-growing networks of user-generated content have
made them an attractive object of investigation for researchers
from different disciplines like Social Network Analysis, Data
Mining, Information Retrieval or Knowledge Discovery. In this
paper, we summarize and extend our work on different aspects
of this branch of Web 2.0 research, demonstrated and evaluated
within our own social bookmark and publication sharing system
BibSonomy, which is currently among the three most popular
systems of its kind. We structure this presentation along
the different interaction phases of a user with our system, coupling
the relevant research questions of each phase with the
corresponding implementation issues. This approach reveals in
a systematic fashion important aspects and results of the broad
bandwidth of folksonomy research like capturing of emergent
semantics, spam detection, ranking algorithms, analogies to
search engine log data, personalized tag recommendations and
information extraction techniques. We conclude that when integrating
a real-life application like BibSonomy into research,
certain constraints have to be considered; but in general, the
tight interplay between our scientific work and the running system
has made BibSonomy a valuable platform for demonstrating
and evaluating Web 2.0 research.