Efficient clustering of high-dimensional data sets with application to reference matching
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Usage patterns of collaborative tagging systems
Journal of Information Science
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Ontology Matching
Collective entity resolution in relational data
ACM Transactions on Knowledge Discovery from Data (TKDD)
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
Constructing folksonomies from user-specified relations on flickr
Proceedings of the 18th international conference on World wide web
A binary variable model for affinity propagation
Neural Computation
Learning systems of concepts with an infinite relational model
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
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
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
A probabilistic approach to mining geospatial knowledge from social annotations
Proceedings of the 21st ACM international conference on Information and knowledge management
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Learning structured representations has emerged as an important problem in many domains, including document and Web data mining, bioinformatics, and image analysis. One approach to learning complex structures is to integrate many smaller, incomplete and noisy structure fragments. In this work, we present an unsupervised probabilistic approach that extends affinity propagation [7] to combine the small ontological fragments into a collection of integrated, consistent, and larger folksonomies. This is a challenging task because the method must aggregate similar structures while avoiding structural inconsistencies and handling noise. We validate the approach on a real-world social media dataset, comprised of shallow personal hierarchies specified by many individual users, collected from the photosharing website Flickr. Our empirical results show that our proposed approach is able to construct deeper and denser structures, compared to an approach using only the standard affinity propagation algorithm. Additionally, the approach yields better overall integration quality than a state-of-the-art approach based on incremental relational clustering.