Advanced Methods for Inconsistent Knowledge Management. By Ngoc Thanh Nguyen. ISBN. 1846288883.pdf

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Unformatted text preview: Advanced Information and Knowledge Processing Series Editors Professor Lakhmi Jain [email protected] Professor Xindong Wu [email protected] Also in this series Gregoris Mentzas, Dimitris Apostolou, Andreas Abecker and Ron Young Knowledge Asset Management 1-85233-583-1 Michalis Vazirgiannis, Maria Halkidi and Dimitrios Gunopulos Uncertainty Handling and Quality Assessment in Data Mining 1-85233-655-2 Asunción Gómez-Pérez, Mariano Fernández-López and Oscar Corcho Ontological Engineering 1-85233-551-3 Arno Scharl (Ed.) Environmental Online Communication 1-85233-783-4 Shichao Zhang, Chengqi Zhang and Xindong Wu Knowledge Discovery in Multiple Databases 1-85233-703-6 Jason T.L. Wang, Mohammed J. Zaki, Hannu T.T. Toivonen and Dennis Shasha (Eds) Data Mining in Bioinformatics 1-85233-671-4 C.C. Ko, Ben M. Chen and Jianping Chen Creating Web-based Laboratories 1-85233-837-7 Manuel Graña, Richard Duro, Alicia d’Anjou and Paul P. Wang (Eds) Information Processing with Evolutionary Algorithms 1-85233-886-0 Colin Fyfe Hebbian Learning and Negative Feedback Networks 1-85233-883-0 Yun-Heh Chen-Burger and Dave Robertson Automating Business Modelling 1-85233-835-0 Dirk Husmeier, Richard Dybowski and Stephen Roberts (Eds) Probabilistic Modeling in Bioinformatics and Medical Informatics 1-85233-778-8 Ajith Abraham, Lakhmi Jain and Robert Goldberg (Eds) Evolutionary Multiobjective Optimization 1-85233-787-7 K.C. Tan, E.F.Khor and T.H. Lee Multiobjective Evolutionary Algorithms and Applications 1-85233-836-9 Nikhil R. Pal and Lakhmi Jain (Eds) Advanced Techniques in Knowledge Discovery and Data Mining 1-85233-867-9 Amit Konar and Lakhmi Jain Cognitive Engineering 1-85233-975-6 Miroslav Kárný (Ed.) Optimized Bayesian Dynamic Advising 1-85233-928-4 Yannis Manolopoulos, Alexandros Nanopoulos, Apostolos N. Papadopoulos and Yannis Theodoridis R-trees: Theory and Applications 1-85233-977-2 Sanghamitra Bandyopadhyay, Ujjwal Maulik, Lawrence B. Holder and Diane J. Cook (Eds) Advanced Methods for Knowledge Discovery from Complex Data 1-85233-989-6 Marcus A. Maloof (Ed.) Machine Learning and Data Mining for Computer Security 1-84628-029-X Sifeng Liu and Yi Lin Grey Information 1-85233-995-0 Vasile Palade, Cosmin Danut Bocaniala and Lakhmi Jain (Eds) Computational Intelligence in Fault Diagnosis 1-84628-343-4 Mitra Basu and Tin Kam Ho (Eds) Data Complexity in Pattern Recognition 1-84628-171-7 Samuel Pierre (Ed.) E-learning Networked Environments and Architectures 1-84628-351-5 Arno Scharl and Klaus Tochtermann (Ed.) The Geospatial Web 1-84628-826-5 Ngoc Thanh Nguyen Advanced Methods for Inconsistent Knowledge Management Ngoc Thanh Nguyen, DSc, PhD, Professor Institute of Information Science and Engineering Wroclaw University of Technology Str. Janiszewskiego 11/17 50-370 Wroclaw Poland British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Control Number: 2007929956 AI&KP ISSN 1610-3947 ISBN 978-1-84628-888-3 e-ISBN 978-1-84628-889-0 Printed on acid-free paper © Springer-Verlag London Limited 2008 Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms of licences issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. The use of registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant laws and regulations and therefore free for general use. The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made. 987654321 Springer Science+Business Media springer.com Foreword Nowadays in the knowledge society, each member deals with a number of tasks related to knowledge management. The most often realized tasks are: decision making, knowledge integration, selection, and retrieval. In all these tasks one has to solve inconsistency of knowledge. Inconsistency is a feature of knowledge which is characterized by the lack of possibility for inference processes. Therefore, solving inconsistency of knowledge is a basic and very essential subtask in many tasks of knowledge management. The whole management process may become impossible if the inconsistency is not resolved. This book presents a set of methods for resolving inconsistency of knowledge. It originally treats the inconsistency on two levels, syntactic and semantic, and proposes methods for processing inconsistency on these levels. The methods proposed here are consensus based. They are worked out on the basis of mathematical models for representing inconsistency as well as tools for measuring and evaluating the degree of inconsistency, defined by the author. The presented material shows that the solution of inconsistency is strongly related to knowledge integration processes. Therefore, along with inconsistency resolution tools, the author proposes algorithms for knowledge integration, such as ontology integration, or agent knowledge states integration. The author has put across a deep and valuable analysis of the proposed models by proving a number of interesting and useful theorems and remarks. Owing to these analysis results one can decide to use the worked out algorithms for concrete practical situations. The author also presents two concrete applications of the proposed methods. The first refers to recommendation processes in intelligent learning systems. Using the method for rough classification, a model for representing learner profiles, learning scenarios, and the choice of a proper scenario for a new learner is proposed. The recommendation mechanisms are built by means of consensus methods and clustering algorithms. As a result, there is the possibility to adapt the learning path to learner needs and preferences. The second application is related to the conception of a multiagent metasearch engine for information retrieval from the Internet. vi Foreword The conception consists of the structure agent knowledge and a set of procedures enabling knowledge exchange, recommendation processes, and decision-making processes of the agents. Another aspect of this book is related to quality analysis of expert knowledge using consensus methods. The author has shown the relationships between the consistency degree of expert solutions for some problem and the distance between their consensus and the proper solution of the problem. He has proved, with some restrictions, that the consensus of the set of expert solutions is better than these solutions. The results are original and very interesting. I would like to congratulate Professor Nguyen for his wonderful contribution. In my opinion, the methods for knowledge inconsistency resolution and integration included in this book are very valuable and many readers such as postgraduate and PhD students in computer science, as well as scientists who are working on knowledge management, ontology integration, and multiagent systems, will find it interesting. Lakhmi C. Jain Preface Inconsistent knowledge management (IKM) is a subject which is the common point of knowledge management and conflict resolution. IKM deals with methods for reconciling inconsistent content of knowledge. Inconsistency in the logic sense has been known for a long time. Inconsistency of this kind refers to a set of logic formulae which have no common model. However, inconsistency of knowledge has a larger aspect which may be considered on two levels: syntactic and semantic. On the syntactic level inconsistency may be treated in the same way as the inconsistency of logic formulae mentioned above, but in a larger context. On the semantic level, on the other hand, inconsistency appears when these formulas are interpreted in some concrete structures and some real world. For solving a large number of conflicts, and especially, for resolving inconsistency of knowledge on the semantic level, consensus methods have been shown to be useful. This book is about methods for processing inconsistent knowledge. The need for knowledge inconsistency resolution arises in many practical applications of computer systems. This kind of inconsistency results from the use of various sources of knowledge in realizing practical tasks. These sources often are autonomous and they use different mechanisms for processing knowledge about the same real world. This can lead to inconsistency. This book provides a wide snapshot of some intelligent technologies for knowledge inconsistency resolution. This book completes the newest research results of the author in the period of the last five years. A part of these results has been published in prestigious international journals and conference proceedings. In this book, along with other new results, the results are completed, extended, and presented in a comprehensive and unified way. The material of each chapter of this book is self-contained. I hope that the book can be useful for graduate and PhD students in computer science; participants of courses in knowledge management and multiagent systems; researchers and all readers working on knowledge management and/or ontology integration; and specialists from social choice. viii Preface I wish to express my great gratitude to Professor Lakhmi C. Jain, the editor of this series, for his encouragement, inspiration, and interest. Thanks are also due to my colleagues at the Institute of Information Science and Engineering of Wroclaw University of Technology, for their nurturing of this project. Special thanks go to Catherine Brett for her kind contacts and advice in preparing this book. Finally, I cordially thank my wife Bich Ngoc and my sons Ngoc Trung and Ngoc Khanh for their great patience and understanding during the preparation of this book. This work was supported by the Polish Ministry of Science and Higher Education under the grant no N516 013 32/1733. Ngoc Thanh Nguyen Table of Contents Foreword .................................................................................................... v Preface ......................................................................................................vii Chapter 1: Inconsistency of Knowledge .................................................. 1 1.1. Introduction .................................................................................... 1 1.2. Levels of Knowledge Inconsistency ................................................ 5 1.3. Knowledge Inconsistency and Integration....................................... 7 1.4. The Subject of this Book ................................................................. 8 1.5. The Structure of this Book............................................................... 9 Chapter 2: Model of Knowledge Conflict.............................................. 13 2.1. Introduction ................................................................................... 13 2.2. What is Conflict? ........................................................................... 16 2.3. Conflict Representation ................................................................. 18 2.3.1. Basic Notions ................................................................................. 18 2.3.2. Definition of Knowledge Conflict................................................. 21 2.3.3. Credibility Degree of Conflict Participants................................... 24 2.4. Consistency Measure for Conflict Profiles ................................... 24 2.4.1. Notion of Conflict Profile Consistency ......................................... 24 2.4.2. Postulates for Consistency Functions ............................................ 26 2.4.3. Analysis of Postulates.................................................................... 32 2.4.4. Consistency Functions................................................................... 38 2.4.5. Reflecting Weights in Consistency Measure................................. 43 2.4.6. Practical Aspect of Consistency Measures.................................... 44 2.5. Conclusions ................................................................................... 46 Chapter 3: Consensus as a Tool for Conflict Solving ........................... 47 3.1. Introduction ................................................................................... 47 3.2. Consensus Theory – A Case Study................................................ 48 3.2.1. An Overview.................................................................................. 48 3.2.2. Consensus versus Conflicts ........................................................... 52 3.3. Consensus Functions ..................................................................... 55 3.3.1. Definition of Consensus Function ................................................. 55 3.3.2. Postulates for Consensus Function ................................................ 56 3.3.3. Analysis of Postulates.................................................................... 59 3.3.4. Other Consensus Choice Functions............................................... 70 x Table of Contents 3.4. Quality of Consensus..................................................................... 73 3.5. Susceptibility to Consensus ........................................................... 76 3.5.1. Criteria for Consensus Susceptibility ............................................ 77 3.5.2. Consensus Susceptibility versus Consistency ............................... 84 3.6. Methods for Achieving Consensus Susceptibility ......................... 87 3.6.1. Profile Modification ...................................................................... 88 3.6.2. Using Weights ............................................................................... 89 3.7. Reduction of Number of Consensuses........................................... 95 3.7.1. Additional Criterion....................................................................... 96 3.7.2. Profile Modification ...................................................................... 98 3.8. Conclusions ................................................................................. 100 Chapter 4: Model for Knowledge Integration..................................... 101 4.1. Introduction ................................................................................. 101 4.2. A General Model for Knowledge Integration.............................. 103 4.2.1. Basis notions................................................................................ 103 4.2.2. Distance Functions between Attribute Values............................. 105 4.2.2.1. Functions Minimizing Transformation Costs ............................ 106 4.2.2.2. Functions Reflecting Element Shares in the Distance.................108 4.3. Knowledge Integration Problem.................................................. 113 4.4. Postulates for Knowledge Integration ......................................... 115 4.5. Algorithms for Integration........................................................... 120 4.6. Conclusions ................................................................................. 122 Chapter 5: Processing Inconsistency on the Syntactic Level ............. 123 5.1. Introduction ................................................................................. 123 5.2. Conjunctive Structure of Knowledge .......................................... 124 5.2.1. Basic Notions............................................................................... 124 5.2.2. Distance Function between Conjunctions.................................... 127 5.2.3. Integration Problem and Postulates for Consensus ..................... 129 5.2.4. Analysis of Postulates.................................................................. 132 5.2.5. Heuristic Algorithm for Determining Consensus........................ 141 5.3. Disjunctive Structure of Knowledge ........................................... 145 5.3.1. Basic Notions............................................................................... 146 5.3.2. Distance Function between Clauses ............................................ 149 5.3.3. Integration Problem and Postulates for Consensus ..................... 150 5.3.4. Heuristic Algorithm for Consensus Determination..................... 156 5.4. Fuzzy Structure of Knowledge .................................................... 158 5.4.1. Basic Notions............................................................................... 159 5.4.2. Distance Function ........................................................................ 159 5.4.3. Integration Problem and Algorithm for Consensus Choice ........ 161 5.5. Conclusions ................................................................................. 163 Table of Contents xi Chapter 6: Processing Inconsistency on the Semantic Level ............. 165 6.1. Introduction ................................................................................. 165 6.2. Conjunctive Structure .................................................................. 166 6.2.1. Basic Notions............................................................................... 166 6.2.2. Conjunctions of Literals .............................................................. 167 6.2.3. Distance Function between Attribute Values .............................. 175 6.2.4. Inconsistency Representation ...................................................... 176 6.2.5. Integration Problem ..................................................................... 178 6.2.6. Consensus Determination for Subprofiles....................................178 6.3. Disjunctive Structure .................................................................. 185 6.3.1. Basic Notions............................................................................... 185 6.3.2. Inconsistency Representation ...................................................... 192 6.3.3. Integration Problem and Consensus ............................................ 193 6.4. Dependences of Attributes.......................................................... 194 6.5. Conclusions ................................................................................. 201 Chapter 7: Consensus for Fuzzy Conflict Profiles.............................. 203 7.1. Introduction ................................................................................. 203 7.2. Basic Notions............................................................................... 204 7.3. Postulates for Consensus ............................................................ 207 7.4. Analysis of Postulates.................................................................. 211 7.5. Algorithms for Consensus Choice ............................................... 216 7.6. Conclusions ................................................................................. 222 Chapter 8: Processing Inconsistency of Expert Knowledge .............. 223 8.1. Introduction ................................................................................. 223 8.2. Basic Notions............................................................................... 226 8.3. Consensus Determination Problems ........................................... 227 8.4. The Quality Analysis ................................................................... 232 8.5. Conclusions ................................................................................. 239 Cha...
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