CALL FOR PAPERS
IEEE Workshop on Knowledge Acquisition from Distributed, Autonomous,
Semantically Heterogeneous Data and Knowledge Sources
(http://www.cild.iastate.edu/events/ICDM2005Workshop.html)
Organized in conjunction with The Fifth IEEE International Conference
on Data Mining, New Orleans, Louisiana, USA, 2005
(http://www.cacs.louisiana.edu/~icdm05/)
Workshop Goals
The workshop aims to bring together researchers in relevant areas of
artificial intelligence (machine learning, data mining, knowledge
representation, ontologies), information systems (information
integration, databases, semantic Web), distributed computing, and
selected application areas (e.g., bioinformatics, security informatics,
environmental informatics) to address several questions that arise in
the process of knowledge acquisition from distributed, autonomous,
semantically heterogeneous data and knowledge sources.
Topics of Interest
Topics of interest include, but are not restricted to:
Challenges presented by emerging data-rich application domains such
as bioinformatics, health informatics, security informatics, social
informatics, environmental informatics.
Knowledge discovery from distributed data (assuming different types
of data fragmentation, e.g., horizontal or vertical data fragmentation;
different hypothesis classes, e.g., naοve Bayes, decision tree;
different performance criteria, e.g., accuracy versus complexity versus
reliability of the model generated, etc.).
Making semantically heterogeneous data sources self-describing (e.g.,
by explicitly associating ontologies with data sources and mappings
between them) in order to help collaborative science .
Representation, manipulation, and reasoning with ontologies and
mappings between ontologies.
Learning ontologies from data (e.g., attribute value taxonomies).
Learning mappings between semantically heterogeneous data source
schemas and between their associated ontologies.
Knowledge discovery in the presence of ontologies (e.g., attribute
value taxonomies) and partially specified data (data described at
different levels of abstraction within an ontology)?
Online query relaxation when an initial query posed to the data
sources fails (i.e., returns no tuples), or equivalently, query-driven
mining of the individual sources that will result in knowledge that can
be used for query relaxation.
Submission Instructions
Postscript or PDF versions of papers, no more than 10 pages long
(including figures, tables, and references) in the ICDM camera-ready
format (IEEE 2-column format), should be submitted electronically to
[log in to unmask] by August 15, 2005. Each paper will be
rigorously refereed by at least 2 reviewers for technical soundness,
originality, and clarity of presentation. Accepted papers will be
included in informal workshop proceedings published by ICDM and
distributed at the workshop. Additional details about the workshop will
be available at:
http://www.cild.iastate.edu/events/ICDM2005Workshop.html
Important Dates
Aug. 15: Paper Due
Oct. 1: Notification
Oct. 15: Camera Ready Due
Nov. 27: Workshop
Organizing Committee
Doina Caragea, [log in to unmask]
Iowa State University
Vasant Honavar, [log in to unmask]
Iowa State University
Ion Muslea, [log in to unmask]
Language Weaver, Inc.
Raghu Ramakrishnan, [log in to unmask]
University of Wisconsin-Madison
Program Committee
Naoki Abe, IBM
Liviu Badea, ICI, Romania
Doina Caragea, Iowa State Univ.
Marie desJardins, UMBC
C. Lee Giles, Penn State Univ.
Vasant Honavar, Iowa State Univ.
Hillol Kargupta, UMBC
Sally McClean, U. of Ulster, UK
Bamshad Mobasher DePaul U.
Ion Muslea, Language Weaver, Inc.
C. David Page, Univ. of Wisconsin
Alexandrin Popescul - Ask Jeeves
Raghu Ramakrishnan, Univ. of Wisconsin
Steffen Staab Univ. of Koblenz
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