Growing a Tree in the Forest: Constructing Folksonomies by Integrating Structured Metadata

TL;DR

This paper presents a relational clustering method for constructing comprehensive folksonomies from social metadata, effectively addressing challenges like sparsity and noise, and demonstrating improved accuracy and scalability on Flickr data.

Contribution

It introduces a novel relational clustering approach that integrates personal hierarchies into a unified, accurate, and scalable folksonomy from social metadata.

Findings

Produces larger folksonomies with higher accuracy

Addresses social metadata challenges such as noise and ambiguity

Scales better than previous methods

Abstract

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.