Lise Getoor

Growing a Tree in the Forest: Constructing Folksonomies by Integrating Structured Metadata
July 27 3:15PM
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.
Workshop 1: Mining and Learning with Graphs Workshop 2010 (MLG-2010)
July 24 9:00AM

There is a great deal of interest in analyzing data that is best represented as a graph. Examples include the WWW, social networks, biological networks, communication networks, and many others. The importance of being able to effectively mine and learn from such data is growing, as more and more structured and semi-structured data is becoming available.

Traditionally, a number of subareas have worked with mining and learning from graph structured data, including communities in graph mining, learning from structured data, statistical relational learning, inductive logic programming, and, moving beyond subdisciplines in computer science, social network analysis, and, more broadly network science.

The objective of this workshop is to bring together researchers from a variety of these areas, and discuss commonality and differences in challenges faced, survey some of the different approaches, and provide a forum to present and learn about some of the most cutting edge research in this area. As an outcome, we expect participants to walk away with a better sense of the variety of different tools available for graph mining and learning, and an appreciation for some of the interesting emerging applications for mining and learning from graphs.

Invited Speakers


  • Stephen Fienberg, CMU
  • Thomas Gärtner, University of Bonn and Fraunhofer IAIS
  • Aristides Gionis, Yahoo! Research
  • Jennifer Neville, Purdue University
  • Padhraic Smyth, UCI
  • Eric Xing, CMU
  • Chris Volinsky, AT&T Labs

Workshop 1: Mining and Learning with Graphs Workshop 2010 (MLG-2010)
July 25 9:00AM

There is a great deal of interest in analyzing data that is best represented as a graph. Examples include the WWW, social networks, biological networks, communication networks, and many others. The importance of being able to effectively mine and learn from such data is growing, as more and more structured and semi-structured data is becoming available.

Traditionally, a number of subareas have worked with mining and learning from graph structured data, including communities in graph mining, learning from structured data, statistical relational learning, inductive logic programming, and, moving beyond subdisciplines in computer science, social network analysis, and, more broadly network science.

The objective of this workshop is to bring together researchers from a variety of these areas, and discuss commonality and differences in challenges faced, survey some of the different approaches, and provide a forum to present and learn about some of the most cutting edge research in this area. As an outcome, we expect participants to walk away with a better sense of the variety of different tools available for graph mining and learning, and an appreciation for some of the interesting emerging applications for mining and learning from graphs.

Invited Speakers (on Sunday)


  • Jennifer Neville, Purdue University
  • Padhraic Smyth, UCI
  • Stephen E. Fienberg, CMU
  • Chris Volinsky, AT&T Labs