Knowledge Acquisition, Modeling and Management

11th European Workshop, EKAW’99 Dagstuhl Castle, Germany, May 26–29, 1999 Proceedings

  • Dieter Fensel
  • Rudi Studer
Conference proceedings EKAW 1999
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1621)

Table of contents

  1. Front Matter
    Pages I-XI
  2. Invited Papers

    1. Daniel E. O’Leary
      Pages 1-12
    2. Mike P. Papazoglou, Jeroen Hoppenbrouwers
      Pages 13-32
  3. Long Papers

    1. V. Richard Benjamins, Bob Wielinga, Jan Wielemaker, Dieter Fensel
      Pages 33-48
    2. Brigitte Biébow, Sylvie Szulman, Av. J. B. Clément
      Pages 49-66
    3. Andreas Birk, Dagmar Surmann, Klaus-Dieter Althoff
      Pages 67-84
    4. François Goasdoué, Chantal Reynaud
      Pages 121-138
    5. Asunción Gómez-Pérez, Ma Dolores Rojas-Amaya
      Pages 139-156
    6. Perry Groot, Annette ten Teije, Frank van Harmelen
      Pages 157-171
    7. Daniela E. Herlea, Catholijn M. Jonker, Jan Treur, Niek J. E. Wijngaards
      Pages 173-190
    8. Björn Höfling, Thorsten Liebig, Dietmar Rösner, Lars Webel
      Pages 191-206
    9. Yannis Kalfoglou, David Robertson
      Pages 207-224
    10. Mourad Oussalah, Karima Messaadia
      Pages 225-242
    11. Thibault Parmentier, Danièle Ziébelin
      Pages 243-258
  4. Short Papers

    1. Ghassan Beydoun, Achim Hoffmann
      Pages 309-314

About these proceedings

Introduction

Past, Present, and Future of Knowledge Acquisition This book contains the proceedings of the 11th European Workshop on Kno- edge Acquisition, Modeling, and Management (EKAW ’99), held at Dagstuhl Castle (Germany) in May of 1999. This continuity and the high number of s- missions re?ect the mature status of the knowledge acquisition community. Knowledge Acquisition started as an attempt to solve the main bottleneck in developing expert systems (now called knowledge-based systems): Acquiring knowledgefromahumanexpert. Variousmethodsandtoolshavebeendeveloped to improve this process. These approaches signi?cantly reduced the cost of - veloping knowledge-based systems. However, these systems often only partially ful?lled the taskthey weredevelopedfor andmaintenanceremainedanunsolved problem. This required a paradigm shift that views the development process of knowledge-based systems as a modeling activity. Instead of simply transf- ring human knowledge into machine-readable code, building a knowledge-based system is now viewed as a modeling activity. A so-called knowledge model is constructed in interaction with users and experts. This model need not nec- sarily re?ect the already available human expertise. Instead it should provide a knowledgelevelcharacterizationof the knowledgethat is requiredby the system to solve the application task. Economy and quality in system development and maintainability are achieved by reusable problem-solving methods and onto- gies. The former describe the reasoning process of the knowledge-based system (i. e. , the algorithms it uses) and the latter describe the knowledge structures it uses (i. e. , the data structures). Both abstract from speci?c application and domain speci?c circumstances to enable knowledge reuse.

Keywords

Fuzzy Intelligent Information Systems Knowledge Acquisition Knowledge Management Knowledge Processing expert system information system knowledge engineering knowledge-based system knowledge-based systems linguistics machine learning ontology semantics verification

Editors and affiliations

  • Dieter Fensel
    • 1
  • Rudi Studer
    • 1
  1. 1.AIFB Institute for Applied Computer Science and Formal Description MethodsUniversity of KarlsruheKarlsruheGermany

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-48775-1
  • Copyright Information Springer-Verlag Berlin Heidelberg 1999
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-66044-6
  • Online ISBN 978-3-540-48775-3
  • Series Print ISSN 0302-9743