document: Growing a tree in the forest : constructing folksonomies by integrating structured metadata 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 .
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