Harnessing Folksonomies for Resource Classification

TL;DR

This paper investigates how user-generated social tags in folksonomies can be effectively harnessed to improve automated resource classification, offering new insights into their utility compared to traditional expert categorizations.

Contribution

It is the first study to perform actual classification experiments using social tags, demonstrating their potential for enhancing resource categorization accuracy.

Findings

Social tags facilitate resource retrieval for communities.

Folksonomies provide meaningful metadata for classification.

Experimental results show improved accuracy using social tags.

Abstract

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.