Workshop on
Learning Statistical Models from Relational Data

This workshop will explore approaches to learning statistical models from relational data. The workshop will explore the foundations, advantages, and limitations of the surprising array of approaches that have been developed over the past decade. These include probabilistic relational models, stochastic logic programs, Bayesian logic programs, relational Bayesian networks, relational probability trees, first-order Bayesian classifiers, relational Markov models, block models and statistical relational models.

These techniques have been developed in several related, but different, subareas of artificial intelligence (reasoning under uncertainty, inductive logic programming, machine learning, and knowledge discovery and data mining) and in some areas outside of AI (e.g., databases and social network analysis). Most researchers only have exposure to one or two techniques, and no clear understanding of the relative advantages and limitations of different techniques has yet emerged. We believe this is an ideal time for a workshop that allows active researchers in this area to discuss and debate the methods and compare and contrast their merits.

Website

The workshop schedule, papers, and other materials are available online at the SRL 2003 Website.

Format

This one-day workshop will consist of interactive sessions that address specific topics identified by the organizing committee (see below) rather than consisting primarily of paper presentations. Each 60-90 minute session will begin with two or three short (10-minute) presentations intended to highlight positions on a specific topic (e.g., representing probabilities or incorporating background knowledge). Prior to the workshop, participants will have access to a variety of tutorial materials provided by both organizers and participants.

Potential topics include:

• Unique challenges of relational learning
• Representational power of different techniques
• Alternative methods of incorporating background knowledge
• Modeling tasks for relational data (e.g., attribute prediction, link prediction, consolidation, and clustering)
• Contribution of ancillary steps to modeling (e.g., data cleaning, transformation, and querying)
• Applications of relational models (e.g., social network analysis, security and law enforcement, and analysis of hypertext collections)

This workshop is intended for researchers in the areas of machine learning, knowledge discovery and data mining, information retrieval, link analysis, and social network analysis.

Submission Instructions

Participants are encouraged to submit position papers and research summaries (up to 8 pages in length) on recent and continuing research. To encourage participation but focus discussions on key topics, we also invite 2-page research synopses and position papers from participants who do not wish to submit full papers. In either case, we encourage authors to identify the discussion session under which their research/position falls.

Each submission shall be accompanied by a short statement (500 words) describing the participant's interests in the workshop topics.

Papers should be formatted according to IJCAI guidelines and should be submitted electronically in postscipt, PDF, or MS Word format via e-mail.

Workshop participation is by invitation only. However, individuals interested in attending the workshop should contact the organizers with a 1-3 paragraph description of their interest regardless of the date.

All submissions and requests should be sent to: srl2003@cs.umass.edu

Note: Participants are expected to register for the main IJCAI conference in addition to the workshop.

Important Dates and Deadlines

• Mar 7, 2003 — Submission deadline (past)
• Mar 21, 2003 — Acceptance notification (past)
• May 27, 2003 — Camera-ready version of papers

Organizing Committee

Lise Getoor (co-chair)
Computer Science Department/UMIACS
AV Williams Building
University of Maryland
College Park, MD 20742
getoor@cs.umd.edu
http://www.cs.umd.edu/~getoor

David Jensen (co-chair)
Department of Computer Science
140 Governors Drive
University of Massachusetts
Amherst, MA 01003-4610
jensen@cs.umass.edu
http://www.cs.umass.edu/~jensen/

Program Committee

James Cussens, University of York, UK
Luc De Raedt, Albert-Ludwigs-University, Germany
Pedro Domingos, University of Washington, USA
Kristian Kersting, Albert-Ludwigs-University, Germany
Stephen Muggleton, Imperial College, London, UK
Avi Pfeffer, Harvard University, USA
Taisuke Sato, Tokyo Institute of Technology, Japan
Lyle Ungar, University of Pennsylvania, USA