Social tagging systems have recently became very popular as a means to classify large sets of resources shared among on-line communities over the social Web. However, the folksonomies resulting from the use of these systems revealed limitations : tags are ambiguous and their spelling may vary, and folksonomies are difficult to exploit in order to retrieve or exchange information. This article compares the recent attempts to overcome these limitations and to support the use of folksonomies with formal languages and ontologies from the Semantic Web.

Abstract  Ontology is one of the fundamental cornerstones of the semantic Web. The pervasive use of ontologies in information sharing and knowledge management calls for efficient and effective approaches to ontology development. Ontology learning, which seeksto discover ontological knowledge from various forms of data automatically or semi-automatically, can overcome the bottleneckof ontology acquisition in ontology development. Despite the significant progress in ontology learning research over the pastdecade, there remain a number of open problems in this field. This paper provides a comprehensive review and discussion ofmajor issues, challenges, and opportunities in ontology learning. We propose a new learning-oriented model for ontology developmentand a framework for ontology learning. Moreover, we identify and discuss important dimensions for classifying ontology learningapproaches and techniques. In light of the impact of domain on choosing ontology learning approaches, we summarize domaincharacteristics that can facilitate future ontology learning effort. The paper offers a road map and a variety of insightsabout this fast-growing field.