Deriving concept hierarchies from text
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Measuring Similarity between Ontologies
EKAW '02 Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Usage patterns of collaborative tagging systems
Journal of Information Science
Improved annotation of the blogosphere via autotagging and hierarchical clustering
Proceedings of the 15th international conference on World Wide Web
Ontology Matching
Collective entity resolution in relational data
ACM Transactions on Knowledge Discovery from Data (TKDD)
Semantic taxonomy induction from heterogenous evidence
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Ontologies are us: A unified model of social networks and semantics
Web Semantics: Science, Services and Agents on the World Wide Web
Leveraging data and structure in ontology integration
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Towards automatic extraction of event and place semantics from flickr tags
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Constructing folksonomies from user-specified relations on flickr
Proceedings of the 18th international conference on World wide web
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
A metric-based framework for automatic taxonomy induction
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Exploiting statistical and relational information on the web and in social media
Proceedings of the fourth ACM international conference on Web search and data mining
A probabilistic approach for learning folksonomies from structured data
Proceedings of the fourth ACM international conference on Web search and data mining
Pragmatic evaluation of folksonomies
Proceedings of the 20th international conference on World wide web
Evolutionary taxonomy construction from dynamic tag space
WISE'10 Proceedings of the 11th international conference on Web information systems engineering
Bursty event detection from collaborative tags
World Wide Web
Interactive curating of user tags for audiovisual archives
Proceedings of the International Working Conference on Advanced Visual Interfaces
Evaluation of Folksonomy Induction Algorithms
ACM Transactions on Intelligent Systems and Technology (TIST)
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
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Many social Web sites allow users to annotate the content with descriptive metadata, such as tags, and more recently to organize content hierarchically. These types of structured metadata provide valuable evidence for learning how a community organizes knowledge. For instance, we can aggregate many personal hierarchies into a common taxonomy, also known as a folksonomy, that will aid users in visualizing and browsing social content, and also to help them in organizing their own content. However, learning from social metadata presents several challenges, since it is sparse, shallow, ambiguous, noisy, and inconsistent. We describe an approach to folksonomy learning based on relational clustering, which exploits structured metadata contained in personal hierarchies. Our approach clusters similar hierarchies using their structure and tag statistics, then incrementally weaves them into a deeper, bushier tree. We study folksonomy learning using social metadata extracted from the photo-sharing site Flickr, and demonstrate that the proposed approach addresses the challenges. Moreover, comparing to previous work, the approach produces larger, more accurate folksonomies, and in addition, scales better.