Subject: CFP: AAI SI on ILP for KDD
From: [email-masked] (Bernhard Pfahringer)
Date: 30 Dec 1996 12:59:38 +1300
Newsgroups: comp.ai,comp.lang.prolog

1st Call For Papers

                       Applied Artificial Intelligence

                              Special issue on
                First-Order Knowledge Discovery in Databases

           (URL: http://www.ai.univie.ac.at/ilp_kdd/aai-si.html)

Knowledge Discovery in Databases (KDD) is the non-trivial process of
identifying valid, novel, potentially useful, and ultimately understandable
patterns in data (Fayyad, Piatetsky-Shapiro & Smyth, 1996). Machine Learning
algorithms form the core of many KDD systems and applications. However,
standard inductive learning techniques are constrained to processing a
single relational table, whereas many real-world databases are structured
into several tables containing interrelated information. Learning algorithms
that are able to use representations in first-order logic, in particular
Inductive Logic Programming (ILP) algorithms, explicitly aim at exploiting
structured information. Thus KDD is a fruitful research and application area
for ILP.

A recent MLnet Workshop, held at the ICML-96, focussed on a discussion of
the potential contribution of ILP for KDD. Information on the workshop
including a short summary and all accepted papers can be found at
http://www.ai.univie.ac.at/ilp_kdd/. The general conclusion was that ILP can
be a valuable tool for data mining, its main advantages being the
expressiveness of first-order logic as a representation language and the
ability of many ILP systems to use strong language biases for restricting
the huge search space. ILP has a high flexibility in incorporating various
forms of background knowledge, which can be invaluable for large KDD tasks.

The special issue on "First-Order Knowledge Discovery in Databases" of the
Applied Artificial Intelligence Journal will thus welcome papers that focus
on one or more of the following topics:

   * Embedding ILP into the KDD process
   * Necessary pre- and post-processing steps for real-world applications
   * Interfacing ILP systems with database managers
   * Scalability of ILP for real-world databases
   * Criteria for quantifying the complexity of ILP problems
   * Evaluation of gain and price of ILP versus propositional learning
   * Non-classification learning and discovery in a first-order framework
   * Benefits of using background knowledge and/or strong explicit biases
   * Innovative real-world applications of ILP

Papers on related subjects are also welcome, but a strong focus on
applications and database issues is required for all submissions.

Submissions

Papers should be prepared according to usual standards for journal
submissions. The approximate length of a manuscript should be between 8,000
to 10,000 words. Final manuscripts of accepted papers will have to be
formatted according to the Instructions to Authors, which can be found in
all issues of the journal.

Authors have to submit four copies of their manuscripts to

   * Johannes Fuernkranz
   * Austrian Research Institute for Artificial Intelligence
   * Schottengasse 3
   * A-1010 Vienna
   * AUSTRIA
   * [email-masked]

or

   * Bernhard Pfahringer
   * Department of Computer Science
   * University of Waikato
   * Hamilton
   * NEW ZEALAND
   * [email-masked]

whichever is more convenient.

Submission Deadline: April 30, 1997

Each paper submitted for publication will be judged by its originality,
adequacy of method, significance of findings, and relevance to the special
issue's subject matter. It should be as concise as possible, yet
sufficiently detailed to permit critical review. Each manuscript must be
accompanied by a statement that it has not been submitted or published
elsewhere. The authors of accepted papers will be asked to transfer the
copyright to the publisher.
-- 
-------------- rm - the ultimate in lossy compression ----------------
Bernhard Pfahringer
mailto:[email-masked] http://www.ai.univie.ac.at/~bernhard