However, the lacking of standardization and the flat structure of tags in folksonomies pose challenges for folksonomy searching and information retrieval. Folksonomies provide a valuable addition to the knowledge organization methods since they allow users to choose vocabularies that meet their real tastes and cognition. This kind of environments allows users to assign shared resources with freely chosen keywords (tags). Recently, Social tagging systems (folksonomies) have become very popular platforms where content is created collaboratively by users. Some initial results of its application to the delicious database are presented, showing that such an approach could be useful to solve some of the well known problems of folksonomies. To put our thoughts in practice, we present a modular and extensible framework of analysis for discovering synonyms, homonyms and hierarchical relationships amongst sets of tags. This approach is grounded on a model that builds upon an edge-colored multigraph of users, tags, and resources. In this paper we rely on the concept of tag relatedness in order to study small groups of similar tags and detect relationships between them. Synonyms, homonyms, and polysemous words, while not harmful for the casual user, strongly affect the quality of search results and the performances of tag-based recommendation systems. However, they are still affected by some issues that limit their utility, mainly due to the inherent ambiguity in the semantics of tags. Tag-based systems have become very common for online classification thanks to their intrinsic advantages such as self-organization and rapid evolution. The proposed approach can also enrich knowledge bases with new subsumption relations, having the potential to significantly reduce time and human effort for knowledge base maintenance and ontology evolution. The results clearly show that the proposed method can extract knowledge of better quality than the existing methods against the gold standard knowledge bases. We performed a comprehensive evaluation using different strategies: relation-level, ontology-level, and knowledge base enrichment based evaluation. Experiments were conducted using a large, publicly available dataset, Bibsonomy, and three popular, human-engineered or data-driven knowledge bases: DBpedia, Microsoft Concept Graph, and ACM Computing Classification System. We further develop an algorithm to organise tags into hierarchies based on the learned relations. The key to this method is quantifying the probabilistic association among tags to better characterise their relations. We propose a supervised learning method to discover subsumption relations from tags. Furthermore, there have been few comprehensive evaluation studies regarding the quality of the discovered knowledge. Research in this line mostly exploits data co-occurrence and often overlooks the complex and ambiguous meanings of tags. There has been considerable interest in transforming unstructured social tagging data into structured knowledge for semantic-based retrieval and recommendation.
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