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SOCNET  January 2003

SOCNET January 2003

Subject:

CFP: IJCAI-2003 Workshop on Learning Statistical Models from Relational Data

From:

Lise Getoor <[log in to unmask]>

Reply-To:

Lise Getoor <[log in to unmask]>

Date:

Fri, 31 Jan 2003 18:32:55 -0500

Content-Type:

text/plain

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Parts/Attachments

text/plain (145 lines)

*****  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/). To unsubscribe, send
an email message to [log in to unmask] containing the line
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