@conference {14502,
	title = {Constructing Folksonomies by Integrating Structured Metadata with Relational Clustering},
	booktitle = {Workshops at the Twenty-Fourth AAAI Conference on Artificial Intelligence},
	year = {2010},
	month = {2010///},
	abstract = {Many social Web sites allow users to annotate the content with descriptive metadata, such as tags, and more recently also to organize content hierarchically. These types of struc- tured metadata provide valuable evidence for learning how a community organizes knowledge. For instance, we can ag- gregate many personal hierarchies into a common taxonomy, also known as a folksonomy, that will aid users in visual- izing and browsing social content, and also to help them in organizing their own content. However, learning from so- cial metadata presents several challenges: sparseness, ambi- guity, noise, and inconsistency. We describe an approach to folksonomy learning based on relational clustering that ad- dresses these challenges by exploiting structured metadata contained in personal hierarchies. Our approach clusters sim- ilar 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. We evaluate the learned folksonomy quantitatively by automatically comparing it to a reference taxonomy. Our empirical results suggest that the proposed framework, which addresses the challenges listed above, improves on existing folksonomy learning methods.},
	author = {Plangprasopchok,A. and Lerman,K. and Getoor, Lise}
}
