Constructing Folksonomies by Integrating Structured Metadata with Relational Clustering

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.