  Social (or folksonomic) tagging has become a very popular way to describe
content within Web 2.0 websites. However, as tags are informally defined,
continually changing, and ungoverned, it has often been criticised for
lowering, rather than increasing, the efficiency of searching. To address this
issue, a variety of approaches have been proposed that recommend users what
tags to use, both when labeling and when looking for resources. These
techniques work well in dense folksonomies, but they fail to do so when tag
usage exhibits a power law distribution, as it often happens in real-life
folksonomies. To tackle this issue, we propose an approach that induces the
creation of a dense folksonomy, in a fully automatic and transparent way: when
users label resources, an innovative tag similarity metric is deployed, so to
enrich the chosen tag set with related tags already present in the folksonomy.
The proposed metric, which represents the core of our approach, is based on the
mutual reinforcement principle. Our experimental evaluation proves that the
accuracy and coverage of searches guaranteed by our metric are higher than
those achieved by applying classical metrics.
