Machine Learning Journal Special Issue on Fusion of Knowledge

From: Richard Dybowski (rdybowski@btinternet.com)
Date: Fri Feb 09 2001 - 15:20:59 MET

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          -----------------------------------------------------------------------------

          Machine Learning Journal Special Issue on Fusion of
          Domain Knowledge with Data for Decision Support
          -----------------------------------------------------------------------------

    >>> Second Call for Papers <<<

    Statistics and machine learning are data-oriented tasks in which domain
    models are induced from data. The bulk of research in these fields
    concentrates on inducing models from data archived in computer databases.
    However, for many problem domains, human expertise forms an essential part
    of the corpus of knowledge needed to construct models of the domain. The
    discipline of knowledge engineering has focused on encoding the knowledge
    of experts in a form that can be encoded into computational models of a
    domain. At present, knowledge engineering and machine learning remain
    largely separate disciplines. Yet in many fields of endeavor, substantial
    human expertise exists alongside data archives. When both data and domain
    knowledge are available, how can these two resources effectively be
    combined to construct decision support systems?

    The aim of this special issue of the Machine Learning journal is to allow
    researchers to communicate their work on integrating domain knowledge with
    data (knowledge-data fusion; theory revision; theory refinement) to a
    general machine learning audience. Emphasis is on sound theoretical
    frameworks rather than ad hoc approaches. Of particular interest are papers
    that combine clear theoretical discussion with practical examples, and
    papers that compare different approaches.

    Possible frameworks for knowledge-data fusion include probabilistic
    (Bayesian/belief) networks, possibilistic logics and networks, hybrid
    neuro-fuzzy networks, and inductive logic programming.

    Topics of interest include (but are not limited to):
    * Practical applications of knowledge-data fusion. What lessons have been
    learnt from attempts to apply knowledge-data fusion to real-world decision
    problems?
    * How are the various knowledge representation and inference frameworks
    that permit induction theoretically related to each other?
    * What frameworks enable an existing induced model, such as a neural
    network, to be incorporated into a proposed knowledge-based system?
    * How can knowledge-data fusion be applied to temporal data?

    Submitted papers must not exceed 30 pages and must conform to the Machine
    Learning journal style. Please see the associated Web site for further
    submission details: http://www.btinternet.com/~rdybowski/mlkdf/

    This Call for Papers is *not* restricted to those who presented at the UAI
    2000 Workshop on Knowledge-Data Fusion: it is open to everyone who has an
    interest in this topic.

    Please direct any enquiries to Richard Dybowski: rdybowski@btinternet.com

    Schedule
    --------------
    Paper submission deadline: June 1, 2001
    Authors' notification of decisions: September 1, 2001
    Final revised papers due: December 15, 2001

    Guest Editors
    --------------------
    Richard Dybowski
    Kathryn Blackmond Laskey (George Mason University)
    James Myers (Ballistic Missile Defense Organization)
    Simon Parsons (Liverpool University)

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