Ranking resources in folksonomies by exploiting semantic information

Organizing and sharing resources are the main aims of social bookmarking applications. By tagging resources, folksonomies emerge collaboratively. Information in folksonomies is valuable for ranking resources in social bookmarking applications as well as on the Web. Folksonomies are therefore important for knowledge management. There are however limitations to ranking in folksonomies, as they are not designed for search. As new Web 2.0 applications emerge providing semantic information, it becomes essential to incorporate this information for improved ranking strategies. Hence, in this work, the algorithms AspectScore and InteliScore are proposed. Both algorithms aim to overcome limitations and drawbacks of graph-based ranking algorithms in folksonomies by incorporating semantic information. Furthermore, a method that leverages semantic information to disambiguate tags is proposed as well as an evaluation methodology for ranking resources in folksonomies.