WordNet: a lexical database for English
Communications of the ACM
The small-world phenomenon: an algorithmic perspective
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
Ontology Learning for the Semantic Web
Ontology Learning for the Semantic Web
Ontologies are us: A unified model of social networks and semantics
Web Semantics: Science, Services and Agents on the World Wide Web
Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
Towards effective browsing of large scale social annotations
Proceedings of the 16th international conference on World Wide Web
Network properties of folksonomies
AI Communications - Network Analysis in Natural Sciences and Engineering
Semantic Grounding of Tag Relatedness in Social Bookmarking Systems
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
Semantic Enrichment of Folksonomy Tagspaces
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
A k-Nearest-Neighbour Method for Classifying Web Search Results with Data in Folksonomies
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Contextualising tags in collaborative tagging systems
Proceedings of the 20th ACM conference on Hypertext and hypermedia
Deriving a large scale taxonomy from Wikipedia
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Evaluating the Impact of Attacks in Collaborative Tagging Environments
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Folks in Folksonomies: social link prediction from shared metadata
Proceedings of the third ACM international conference on Web search and data mining
Stop thinking, start tagging: tag semantics emerge from collaborative verbosity
Proceedings of the 19th international conference on World wide web
Growing a tree in the forest: constructing folksonomies by integrating structured metadata
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
On the Navigability of Social Tagging Systems
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
Pragmatic evaluation of folksonomies
Proceedings of the 20th international conference on World wide web
Building directories for social tagging systems
Proceedings of the 20th ACM international conference on Information and knowledge management
On how to perform a gold standard based evaluation of ontology learning
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Navigational efficiency of broad vs. narrow folksonomies
Proceedings of the 23rd ACM conference on Hypertext and social media
Evaluating tag-based information access in image collections
Proceedings of the 23rd ACM conference on Hypertext and social media
Proceedings of the 12th International Conference on Knowledge Management and Knowledge Technologies
Meaning as collective use: predicting semantic hashtag categories on twitter
Proceedings of the 22nd international conference on World Wide Web companion
Crowdsourced Knowledge Acquisition: Towards Hybrid-Genre Workflows
International Journal on Semantic Web & Information Systems
Hi-index | 0.00 |
Algorithms for constructing hierarchical structures from user-generated metadata have caught the interest of the academic community in recent years. In social tagging systems, the output of these algorithms is usually referred to as folksonomies (from folk-generated taxonomies). Evaluation of folksonomies and folksonomy induction algorithms is a challenging issue complicated by the lack of golden standards, lack of comprehensive methods and tools as well as a lack of research and empirical/simulation studies applying these methods. In this article, we report results from a broad comparative study of state-of-the-art folksonomy induction algorithms that we have applied and evaluated in the context of five social tagging systems. In addition to adopting semantic evaluation techniques, we present and adopt a new technique that can be used to evaluate the usefulness of folksonomies for navigation. Our work sheds new light on the properties and characteristics of state-of-the-art folksonomy induction algorithms and introduces a new pragmatic approach to folksonomy evaluation, while at the same time identifying some important limitations and challenges of folksonomy evaluation. Our results show that folksonomy induction algorithms specifically developed to capture intuitions of social tagging systems outperform traditional hierarchical clustering techniques. To the best of our knowledge, this work represents the largest and most comprehensive evaluation study of state-of-the-art folksonomy induction algorithms to date.