[loginf] CfP: Relational Approaches to Knowledge Representation and Learning

Christoph Beierle Christoph.Beierle at FernUni-Hagen.de
Mon Mar 9 19:35:49 CET 2009


**********************************************************************
                         Call for Papers:

    Relational Approaches to Knowledge Representation and Learning
    ==============================================================
            http://www.fernuni-hagen.de/wbs/relkrl09.html

   Organized by the FG Wissensrepräsentation und Schließen of the GI 

                        Workshop at KI-2009,
          32nd Annual Conference on Artificial Intelligence, 
		September 15-18, 2009, Paderborn, Germany
                  http://ki2009.uni-paderborn.de/
                             
***********************************************************************

Knowledge representation encompasses a variety of methods and 
formalisms to encode and process all types of knowledge, belief, 
and information. It provides the theoretical foundation for rational  
and intelligent behaviour in real environments, focusing on topics 
like default logics and uncertain reasoning, belief change, ontologies, 
and argumentation, among many others. Moreover, in a thematical 
respect, knowledge representation is closely related to the areas of 
machine learning and knowledge discovery the methods of which allow 
the acquisition of useful information to build up knowledge bases. 

Knowledge representation has made substantial progress over the last 
decade by devising sophisticated methods for inference and reasoning. 
Nevertheless, the connection to learning still holds undeveloped 
potential in methodological and technical respects which might be 
crucial for practical applications. Furthermore, the handling of 
relational information, i.e. the explicit representation of knowledge 
about objects and its linking to knowledge about classes, is still a 
challenge for many subareas of knowledge representation. Ontologies, 
logic programming and probabilistic relational models are just some 
important examples of areas of research that address both of these 
points. 

The aim of this workshop is to strengthen the connection between 
knowledge representation and learning by focusing on  relational and 
first-order approaches to all areas of knowledge representation and 
learning, in particular

    * default and conditional logics
    * logic programming
    * uncertain reasoning
    * nonmonotonic and nonclassical logics
    * belief revision
    * probabilistic networks
    * inference processes
    * machine learning 
    * data mining 
    * knowledge discovery
    * knowledge engineering
    * ontologies
    * agent systems
    * applications

Important Dates:
----------------
     Deadline for Submission:    June 16, 2009
     Notification of Authors:    July 23, 2009
     Final Versions of Papers:   August 15, 2009
     Workshop:                   September 15, 2009 (exact workshop day to be confirmed)
     Conference: 		 September 15-18, 2009

Workshop Organizers and Co-Chairs:
----------------------------------
     Gabriele Kern-Isberner, TU Dortmund
     Christoph Beierle, FernUniversität in Hagen
  
Program Committee:
------------------
     Salem Benferhat     Université d'Artois, Lens, France
     Gerd Brewka         Universität Leipzig, Germany
     James P. Delgrande  Simon Fraser University, Canada
     Jürgen Dix          TU Clausthal-Zellerfeld, Germany
     Eduardo Ferme       Universidade da Madeira, Portugal
     Andreas Herzig      Universite Paul Sabatier, Toulouse
     Pascal Hitzler      Universität Karlsruhe (TH), Germany
     Antonis C. Kakas    University of Cyprus, Cyprus
     Christian Kersting  Fraunhofer IAIS, University of Bonn, Germany
     Thomas Lukasiewicz  University of Oxford, UK
     Emil Weydert        University of Luxembourg, Luxembourg

Paper Submission and Publication:
---------------------------------
Paper format and submission details are available at
     http://www.fernuni-hagen.de/wbs/relkrl09

**************************************************************************
-------------- next part --------------

**********************************************************************
                         Call for Papers:

    Relational Approaches to Knowledge Representation and Learning
    ==============================================================
            http://www.fernuni-hagen.de/wbs/relkrl09.html

   Organized by the FG Wissensrepräsentation und Schließen of the GI 

                        Workshop at KI-2009,
          32nd Annual Conference on Artificial Intelligence, 
		September 15-18, 2009, Paderborn, Germany
                  http://ki2009.uni-paderborn.de/
                             
***********************************************************************

Knowledge representation encompasses a variety of methods and 
formalisms to encode and process all types of knowledge, belief, 
and information. It provides the theoretical foundation for rational  
and intelligent behaviour in real environments, focusing on topics 
like default logics and uncertain reasoning, belief change, ontologies, 
and argumentation, among many others. Moreover, in a thematical 
respect, knowledge representation is closely related to the areas of 
machine learning and knowledge discovery the methods of which allow 
the acquisition of useful information to build up knowledge bases. 

Knowledge representation has made substantial progress over the last 
decade by devising sophisticated methods for inference and reasoning. 
Nevertheless, the connection to learning still holds undeveloped 
potential in methodological and technical respects which might be 
crucial for practical applications. Furthermore, the handling of 
relational information, i.e. the explicit representation of knowledge 
about objects and its linking to knowledge about classes, is still a 
challenge for many subareas of knowledge representation. Ontologies, 
logic programming and probabilistic relational models are just some 
important examples of areas of research that address both of these 
points. 

The aim of this workshop is to strengthen the connection between 
knowledge representation and learning by focusing on  relational and 
first-order approaches to all areas of knowledge representation and 
learning, in particular

    * default and conditional logics
    * logic programming
    * uncertain reasoning
    * nonmonotonic and nonclassical logics
    * belief revision
    * probabilistic networks
    * inference processes
    * machine learning 
    * data mining 
    * knowledge discovery
    * knowledge engineering
    * ontologies
    * agent systems
    * applications

Important Dates:
----------------
     Deadline for Submission:    June 16, 2009
     Notification of Authors:    July 23, 2009
     Final Versions of Papers:   August 15, 2009
     Workshop:                   September 15, 2009 (exact workshop day to be confirmed)
     Conference: 		 September 15-18, 2009

Workshop Organizers and Co-Chairs:
----------------------------------
     Gabriele Kern-Isberner, TU Dortmund
     Christoph Beierle, FernUniversität in Hagen
  
Program Committee:
------------------
     Salem Benferhat     Université d'Artois, Lens, France
     Gerd Brewka         Universität Leipzig, Germany
     James P. Delgrande  Simon Fraser University, Canada
     Jürgen Dix          TU Clausthal-Zellerfeld, Germany
     Eduardo Ferme       Universidade da Madeira, Portugal
     Andreas Herzig      Universite Paul Sabatier, Toulouse
     Pascal Hitzler      Universität Karlsruhe (TH), Germany
     Antonis C. Kakas    University of Cyprus, Cyprus
     Christian Kersting  Fraunhofer IAIS, University of Bonn, Germany
     Thomas Lukasiewicz  University of Oxford, UK
     Emil Weydert        University of Luxembourg, Luxembourg

Paper Submission and Publication:
---------------------------------
Paper format and submission details are available at
     http://www.fernuni-hagen.de/wbs/relkrl09

**************************************************************************


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