  In our daily lives, organizing resources into a set of categories is a common
task. Categorization becomes more useful as the collection of resources
increases. Large collections of books, movies, and web pages, for instance, are
cataloged in libraries, organized in databases and classified in directories,
respectively. However, the usual largeness of these collections requires a vast
endeavor and an outrageous expense to organize manually.
  Recent research is moving towards developing automated classifiers that
reduce the increasing costs and effort of the task. Little work has been done
analyzing the appropriateness of and exploring how to harness the annotations
provided by users on social tagging systems as a data source. Users on these
systems save resources as bookmarks in a social environment by attaching
annotations in the form of tags. It has been shown that these tags facilitate
retrieval of resources not only for the annotators themselves but also for the
whole community. Likewise, these tags provide meaningful metadata that refers
to the content of the resources.
  In this thesis, we deal with the utilization of these user-provided tags in
search of the most accurate classification of resources as compared to
expert-driven categorizations. To the best of our knowledge, this is the first
research work performing actual classification experiments utilizing social
tags. By exploring the characteristics and nature of these systems and the
underlying folksonomies, this thesis sheds new light on the way of getting the
most out of social tags for the sake of automated resource classification
tasks. Therefore, we believe that the contributions in this work are of utmost
interest for future researchers in the field, as well as for the scientific
community in order to better understand these systems and further utilize the
knowledge garnered from social tags.
