Advances in Knowledge Acquisition and Management Pacific Rim Kno
Since knowledge was recognized as a crucial part of intelligent systems in the 1970s and early 1980s, the problem of the systematic and efficient acquisition of knowledge was an important research problem. In the early days of expert systems, the focus of
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Subseries of Lecture Notes in Computer Science
4303
Achim Hoffmann Byeong-ho Kang Debbie Richards Shusaku Tsumoto (Eds.)
Advances in Knowledge Acquisition and Management Pacific Rim Knowledge Acquisition Workshop, PKAW 2006 Guilin, China, August 7-8, 2006 Revised Selected Papers
13
Series Editors Jaime G. Carbonell, Carnegie Mellon University, Pittsburgh, PA, USA Jörg Siekmann, University of Saarland, Saarbrücken, Germany Volume Editors Achim Hoffmann The University of New South Wales, School of Computer Science & Engineering Sydney 2052, Australia E-mail: [email protected] Byeong-ho Kang University of Tasmania, School of Computing Hobart Campus, Centenary Building, Hobart, TAS 7001, Australia E-mail: [email protected] Debbie Richards Macquarie University, Department of Computing Sydney, NSW 2109, Australia E-mail: [email protected] Shusaku Tsumoto Shimane University, School of Medicine, Department of Medical Informatics 89-1 Enya-cho, Izumo 693-8501, Japan E-mail: [email protected]
Library of Congress Control Number: 2006938401
CR Subject Classification (1998): I.2.6, I.2, H.2.8, H.3-5, F.2.2, C.2.4, K.3 LNCS Sublibrary: SL 7 – Artificial Intelligence ISSN ISBN-10 ISBN-13
0302-9743 3-540-68955-9 Springer Berlin Heidelberg New York 978-3-540-68955-3 Springer Berlin Heidelberg New York
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Preface
Since knowledge was recognized as a crucial part of intelligent systems in the 1970s and early 1980s, the problem of the systematic and efficient acquisition of knowledge was an important research problem. In the early days of expert systems, the focus of knowledge acquisition was to design a suitable knowledge base for the problem domain by eliciting the knowledge from available experts before the system was completed and deployed. Over the years, alternative approaches were developed, such as incremental approaches which would build a provisional knowledge base initially and would improve the knowledge base while the system was used in practice. Other approaches sought to build knowledge bases fully automatically by employing machine-learning methods. In recent years, a significant interest developed regarding the problem of constructing ontologie
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