Fuzzy clustering-based approach to derive hierarchical structures from folksonomies

Collaborative tagging systems have recently emerged as a powerful way to label and organize large collections of data. The informal social classification structure in these systems, also known as folksonomy, provides a convenient way to annotate resources by allowing users to use any keyword or tag that they find relevant. Although folksonomies and the respective tags often lack a context-independent and intersubjective definition of meaning, the assumption that the evolving structure of these digital records contains implicit evidences for the underlying semantics has been proven by successful approaches of making the emergent semantics explicit. In this paper we propose an approach for extracting ontological structures from folksonomies that exploits the power of fuzzy clustering using new similarity and generality measure. The fuzzy clustering process discovers ambiguous tags and disambiguates them all at once, and the new similarity measure gives more accurate results as it calculates co-occurrences based on distinct users and not only in the number of co-occurrences of two distinct words. The generated ontology can be used to enhance various tasks in the tagging systems, such as tag disambiguation, result visualization, and ontology evolution. Our experimental results on real world data sets show that our method can effectively learn the ontology structure from the folksonomies.