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. Finally, the authors propose an approach to extract ontology from social tagging systems. The authors also present and describe many techniques, tools and online resources that can be useful in working on such systems. In this paper, the authors present, describe and compare the most relevant approaches to capturing hidden semantics in folksonomies and turning it into ontologies. Several researches have been proposed to overcome these drawbacks. ![]() 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.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |