document: Constructing folksonomies from user-specified relations on flickr Automatic folksonomy construction from tags has attracted much attention recently . However , inferring hierarchical relations between concepts from tags has a drawback in that it is difficult to distinguish between more popular and more general concepts . Instead of tags we propose to use user-specified relations for learning folksonomy . We explore two statistical frameworks for aggregating many shallow individual hierarchies , expressed through the collection\/set relations on the social photosharing site Flickr , into a common deeper folksonomy that reflects how a community organizes knowledge . Our approach addresses a number of challenges that arise while aggregating information from diverse users , namely noisy vocabulary , and variations in the granularity level of the concepts expressed . Our second contribution is a method for automatically evaluating learned folksonomy by comparing it to a reference taxonomy , e.g. , the Web directory created by the Open Directory Project . Our empirical results suggest that user-specified relations are a good source of evidence for learning folksonomies .
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