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

TitleGrowing a tree in the forest: constructing folksonomies by integrating structured metadata
Publication TypeConference Papers
Year of Publication2010
AuthorsPlangprasopchok A, Lerman K, Getoor L
Conference NameProceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Date Published2010///
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-0055-1
Keywordscollective knowledge, data mining, folksonomies, relational clustering, social information processing, social metadata, taxonomies

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.