Knowledge Acquisition, Modeling and Management

1. Front Matter

   Pages I-XI

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2. Invited Papers

   1. Reengineering and Knowledge Management

      • Daniel E. O’Leary

      Pages 1-12

   2. Knowledge Navigation in Networked Digital Libraries

      • Mike P. Papazoglou, Jeroen Hoppenbrouwers

      Pages 13-32

3. Long Papers

   1. Towards Brokering Problem-Solving Knowledge on the Internet

      • V. Richard Benjamins, Bob Wielinga, Jan Wielemaker, Dieter Fensel

      Pages 33-48

   2. TERMINAE: A Linguistics-Based Tool for the Building of a Domain Ontology

      • Brigitte Biébow, Sylvie Szulman, Av. J. B. Clément

      Pages 49-66

   3. Applications of Knowledge Acquisition in Experimental Software Engineering

      • Andreas Birk, Dagmar Surmann, Klaus-Dieter Althoff

      Pages 67-84

   4. Acquiring and Structuring Web Content with Knowledge Level Models

      • Louise Crow, Nigel Shadbolt

      Pages 85-102

   5. A Knowledge-Based News Server Supporting Ontology-Driven Story Enrichment and Knowledge Retrieval

      • John Domingue, Enrico Motta

      Pages 103-120

   6. Modeling Information Sources for Information Integration

      • François Goasdoué, Chantal Reynaud

      Pages 121-138

   7. Ontological Reengineering for Reuse

      • Asunción Gómez-Pérez, Ma Dolores Rojas-Amaya

      Pages 139-156

   8. Formally Verifying Dynamic Properties of Knowledge Based Systems

      • Perry Groot, Annette ten Teije, Frank van Harmelen

      Pages 157-171

   9. Integration of Behavioural Requirements Specification within Knowledge Engineering

      • Daniela E. Herlea, Catholijn M. Jonker, Jan Treur, Niek J. E. Wijngaards

      Pages 173-190

   10. Towards an Ontology for Substances and Related Actions

       • Björn Höfling, Thorsten Liebig, Dietmar Rösner, Lars Webel

       Pages 191-206

   11. Use of Formal Ontologies to Support Error Checking in Specifications

       • Yannis Kalfoglou, David Robertson

       Pages 207-224

   12. The Ontologies of Semantic and Transfer Links

       • Mourad Oussalah, Karima Messaadia

       Pages 225-242

   13. Distributed Problem Solving Environment Dedicated to DNA Sequence Annotation

       • Thibault Parmentier, Danièle Ziébelin

       Pages 243-258

   14. Knowledge Acquisition from Multiple Experts Based on Semantics of Concepts

       • Seppo Puuronen, Vagan Terziyan

       Pages 259-273

   15. Acquiring Expert Knowledge for the Design of Conceptual Information Systems

       • Gerd Stumme

       Pages 275-290

   16. A Constraint-Based Approach to the Description of Competence

       • S. White, D. Sleeman

       Pages 291-308

4. Short Papers

   1. Holism and Incremental Knowledge Acquisition

      • Ghassan Beydoun, Achim Hoffmann

      Pages 309-314

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.

• 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

• AIFB Institute for Applied Computer Science and Formal Description Methods, University of Karlsruhe, Karlsruhe, Germany

  Dieter Fensel, Rudi Studer