***** To join INSNA, visit http://www.sfu.ca/~insna/ ***** CFP: IJCAI-2003 Workshop Learning Statistical Models from Relational Data Monday, 11 August 2003 Acapulco, Mexico http://kdl.cs.umass.edu/events/srl2003/ 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 unique challenges of learning statistical models from relational data. - ---------------------------------------------------------------------- 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 * Scalability of statistical relational model-building * Alternative methods of incorporating background knowledge * Inference and learning tasks for relational data (e.g., attribute prediction, link prediction, consolidation, entity detection, object identification and clustering) * Learning statistical models from time-changing relational data * Using statistical models to fuse relational information from noisy, heterogeneous sources * 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. - ---------------------------------------------------------------------- Paper Submissions - ---------------------------------------------------------------------- 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 postscript, PDF, or MS Word format via e-mail. All submissions should be sent to: [log in to unmask] - ---------------------------------------------------------------------- Important Dates - ---------------------------------------------------------------------- Mar 7, 2003 Submission deadline Mar 21, 2003 Acceptance notification May 16, 2003 Camera-ready version of papers - - ---------------------------------------------------------------------- Organizing Chairs - - ---------------------------------------------------------------------- Lise Getoor (co-chair) Computer Science Department/UMIACS AV Williams Building University of Maryland College Park, MD 20742 voice 301-405-2691 fax 301-405-6707 http://www.cs.umd.edu/~getoor [log in to unmask] David Jensen (co-chair) Department of Computer Science 140 Governors Drive University of Massachusetts Amherst, MA 01003-4610 voice 413-545-9677 fax 413-545-1249 http://www.cs.umass.edu/~jensen [log in to unmask] - ---------------------------------------------------------------------- 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 - ---------------------------------------------------------------------- Additional information - ---------------------------------------------------------------------- See: http://kdl.cs.umass.edu/events/srl2003/ for more information. _____________________________________________________________________ SOCNET is a service of INSNA, the professional association for social network researchers (http://www.sfu.ca/~insna/). 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