知识获得和管理的进展LNCS-4303
2007-02-01
Springer
Achim Hoffmann
2567
This book constitutes the thoroughly refereed post-proceedings of the 2006 Pacific Rim Knowledge Acquisition Workshop, PKAW 2006, held in Guilin, China in August 2006 as part of 9th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2006. The 21 revised full papers and 6 revised short papers presented together with 4 invited talks were carefully reviewed and selected from 81 submissions. The papers are organized in topical sections on ontology and knowledge acquisition, algorithm approaches to knowledge acquisition, incremental knowledge acquisition and RDR, knowledge acquisition and applications, as well as machine learning and data mining.
PART I: Regular Papers Ontology and Knowledge Acquisition 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 Algorithm Approaches to Knowledge Acquisition 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 Incremental Knowledge Acquisition and RDR Evaluation of the FastFIX Prototype 5Cs CARD System Intelligent Decision Support for Medication Review A Hybrid Browsing Mechanism Using Conceptual Scales Knowledge Acquisition and Applications 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 Machine Learning and Data Mining 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 ClassificationPART II:Regular Papers(6-8Pages)Author Index
