Effective Retrieval of Resources in Folksonomies Using a New Tag Similarity Measure
This work addresses search inefficiencies in social tagging systems for users of Web 2.0 websites, offering an incremental improvement over existing methods.
The paper tackles the problem of inefficient search in folksonomies due to informal and sparse tag usage by proposing a new tag similarity measure based on mutual reinforcement, which automatically enriches tag sets to create denser folksonomies. Experimental results show that this metric achieves higher accuracy and coverage in searches compared to classical metrics.
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.