Advances in Knowledge Acquisition and Management
Pacific Rim Knowledge Acquisition Workshop, PKAW 2006, Guilin, China, August 7-8, 2006, Revised Selected Papers
Published on 14. December 2006
Book
Paperback/Softback
XI, 264 pages
978-3-540-68955-3 (ISBN)
Description
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 - main by eliciting the knowledge from available experts before the system was c- pleted 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 ontologies. Of particular interest have been ontologies that could be re-used in a number of ways and could possibly be shared across diff- ent users as well as domains. The Pacific Knowledge Acquisition Workshops (PKAW) have a long tradition in providing a forum for researchers to exchange the latest ideas on the topic. Parti- pants come from all over the world but with a focus on the Pacific Rim region. PKAW is one of three international knowledge acquisition workshop series held in the Pacific-Rim, Canada and Europe over the last two decades. The previous Pacific Knowledge Acquisition Workshop, PKAW 2004, had a strong emphasis on inc- mental knowledge acquisition, machine learning, neural networks and data mining.
More details
Series
Edition
2006 ed.
Language
English
Place of publication
Berlin
Germany
Publishing group
Springer Berlin
Target group
Professional and scholarly
Research
Illustrations
XI, 264 p.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 16 mm
Weight
423 gr
ISBN-13
978-3-540-68955-3 (9783540689553)
DOI
10.1007/11961239
Schweitzer Classification
Content
Regular Papers.- Visual Knowledge Annotation and Management by Using Qualitative Spatial Information.- Ad-Hoc and Personal Ontologies: A Prototyping Approach to Ontology Engineering.- Relating Business Process Models to Goal-Oriented Requirements Models in KAOS.- Heuristic and Rule-Based Knowledge Acquisition: Classification of Numeral Strings in Text.- RFID Tag Based Library Marketing for Improving Patron Services.- Extracting Discriminative Patterns from Graph Structured Data Using Constrained Search.- Evaluating Learning Algorithms with Meta-learning Schemes for a Rule Evaluation Support Method Based on Objective Indices.- Training Classifiers for Unbalanced Distribution and Cost-Sensitive Domains with ROC Analysis.- Revealing Themes and Trends in the Knowledge Domain's Intellectual Structure.- Evaluation of the FastFIX Prototype 5Cs CARD System.- Intelligent Decision Support for Medication Review.- A Hybrid Browsing Mechanism Using Conceptual Scales.- Knowledge Representation for Video Assisted by Domain-Specific Ontology.- An Ontological Infrastructure for the Semantic Integration of Clinical Archetypes.- Improvement of Air Handling Unit Control Performance Using Reinforcement Learning.- Optimizing Dissimilarity-Based Classifiers Using a Newly Modified Hausdorff Distance.- A New Model for Classifying DNA Code Inspired by Neural Networks and FSA.- Improvements on Common Vector Approach Using k-Clustering Method.- The Method for the Unknown Word Classification.- Regular Papers (6-8 Pages).- An Ontology Supported Approach to Learn Term to Concept Mapping.- Building Corporate Knowledge Through Ontology Integration.- Planning with Domain Rules Based on State-Independent Activation Sets.- Elicitation of Non-functional Requirement Preference for Actors of Usecase fromDomain Model.- Enhancing Information Retrieval Using Problem Specific Knowledge.- Acquiring Innovation Knowledge.