Advanced Methods for Inconsistent Knowledge Management

By: Ngoc Thanh Nguyen

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Hardcover | 28 September 2007

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364 Pages

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23.5 x 15.88 x 2.54

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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 incons- tency is not resolved. This book presents a set of methods for resolving inconsistency of kn- ledge. 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 str- gly related to knowledge integration processes. Therefore, along with - consistency resolution tools, the author proposes algorithms for knowledge integration, such as ontology integration, or agent knowledge states in- gration. The author has put across a deep and valuable analysis of the p- posed 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.

Industry Reviews

From the reviews:

"The author summarizes most of his research, and the other research that has been done concerning how to resolve the aforementioned inconsistencies, when they refer to the same object or phenomenon in the real world. ... It is written in an accessible, pedagogical manner, and thus will be quite useful, not only for specialists, but also for advanced or postgraduate students in computer science, particularly those interested in the development of artificial intelligence and its practical applications. I highly recommend this book." (Arturo Ortiz-Tapia, Computing Reviews, Vol. 50 (1), January, 2009)

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You Can Find This Book In

Non-Fiction Mathematics Mathematical Foundation Mathematical Logic Business & Management Management & Management Techniques Management of Specific Areas Knowledge Management Computing & I.T.Databases Business Mathematics & Systems Business Strategy

Computer Science Artificial Intelligence Computer Programming & Software Development Algorithms & Data Structures

Foreword	p. v
Preface	p. vii
Inconsistency of Knowledge	p. 1
Introduction	p. 1
Levels of Knowledge Inconsistency	p. 5
Knowledge Inconsistency and Integration	p. 7
The Subject of this Book	p. 8
The Structure of this Book	p. 9
Model of Knowledge Conflict	p. 13
Introduction	p. 13
What is Conflict?	p. 16
Conflict Representation	p. 18
Basic Notions	p. 18
Definition of Knowledge Conflict	p. 21
Credibility Degree of Conflict Participants	p. 24
Consistency Measure for Conflict Profiles	p. 24
Notion of Conflict Profile Consistency	p. 24
Postulates for Consistency Functions	p. 26
Analysis of Postulates	p. 32
Consistency Functions	p. 38
Reflecting Weights in Consistency Measure	p. 43
Practical Aspect of Consistency Measures	p. 44
Conclusions	p. 46
Consensus as a Tool for Conflict Solving	p. 47
Introduction	p. 47
Consensus Theory - A Case Study	p. 48
An Overview	p. 48
Consensus versus Conflicts	p. 52
Consensus Functions	p. 55
Definition of Consensus Function	p. 55
Postulates for Consensus Function	p. 56
Analysis of Postulates	p. 59
Other Consensus Choice Functions	p. 70
Quality of Consensus	p. 73
Susceptibility to Consensus	p. 76
Criteria for Consensus Susceptibility	p. 77
Consensus Susceptibility versus Consistency	p. 84
Methods for Achieving Consensus Susceptibility	p. 87
Profile Modification	p. 88
Using Weights	p. 89
Reduction of Number of Consensuses	p. 95
Additional Criterion	p. 96
Profile Modification	p. 98
Conclusions	p. 100
Model for Knowledge Integration	p. 101
Introduction	p. 101
A General Model for Knowledge Integration	p. 103
Basis notions	p. 103
Distance Functions between Attribute Values	p. 105
Functions Minimizing Transformation Costs	p. 106
Functions Reflecting Element Shares in the Distance	p. 108
Knowledge Integration Problem	p. 113
Postulates for Knowledge Integration	p. 115
Algorithms for Integration	p. 120
Conclusions	p. 122
Processing Inconsistency on the Syntactic Level	p. 123
Introduction	p. 123
Conjunctive Structure of Knowledge	p. 124
Basic Notions	p. 124
Distance Function between Conjunctions	p. 127
Integration Problem and Postulates for Consensus	p. 129
Analysis of Postulates	p. 132
Heuristic Algorithm for Determining Consensus	p. 141
Disjunctive Structure of Knowledge	p. 145
Basic Notions	p. 146
Distance Function between Clauses	p. 149
Integration Problem and Postulates for Consensus	p. 150
Heuristic Algorithm for Consensus Determination	p. 156
Fuzzy Structure of Knowledge	p. 158
Basic Notions	p. 159
Distance Function	p. 159
Integration Problem and Algorithm for Consensus Choice	p. 161
Conclusions	p. 163
Processing Inconsistency on the Semantic Level	p. 165
Introduction	p. 165
Conjunctive Structure	p. 166
Basic Notions	p. 166
Conjunctions of Literals	p. 167
Distance Function between Attribute Values	p. 175
Inconsistency Representation	p. 176
Integration Problem	p. 178
Consensus Determination for Subprofiles	p. 178
Disjunctive Structure	p. 185
Basic Notions	p. 185
Inconsistency Representation	p. 192
Integration Problem and Consensus	p. 193
Dependences of Attributes	p. 194
Conclusions	p. 201
Consensus for Fuzzy Conflict Profiles	p. 203
Introduction	p. 203
Basic Notions	p. 204
Postulates for Consensus	p. 207
Analysis of Postulates	p. 211
Algorithms for Consensus Choice	p. 216
Conclusions	p. 222
Processing Inconsistency of Expert Knowledge	p. 223
Introduction	p. 223
Basic Notions	p. 226
Consensus Determination Problems	p. 227
The Quality Analysis	p. 232
Conclusions	p. 239
Ontology Integration	p. 241
Introduction	p. 241
Problem of Ontology Integration	p. 244
Inconsistency Between Ontologies	p. 245
Basic Notions	p. 245
Inconsistency on the Instance Level	p. 247
Inconsistency on the Concept Level	p. 248
Inconsistency on the Relation Level	p. 251
Some Remarks	p. 253
Inconsistency Resolution and Ontology Integration	p. 253
For the Instance Level	p. 253
For the Concept Level	p. 254
For the Relation Level	p. 258
Conclusions	p. 262
Application of Inconsistency Resolution Methods in Intelligent Learning Systems	p. 263
Introduction	p. 263
Structure of Knowledge	p. 266
Basic Notions	p. 266
Distance Functions between Scenarios	p. 271
Learner Profile and Classification	p. 277
User Data	p. 277
Usage Data	p. 279
Learner Classification Process	p. 279
Recommendation Process	p. 281
Recommendation Procedure	p. 281
Algorithm for Determination of Opening Scenario	p. 283
Learners Clustering Process	p. 289
Rough Learner Classification Method	p. 292
Pawlak's Concept	p. 292
Our Concept	p. 293
Basic Notions	p. 293
Rough Learner Classification	p. 296
Conclusions	p. 306
Processing Inconsistency in Information Retrieval	p. 307
Introduction	p. 307
Agent Technology for Information Retrieval	p. 310
A Conception for a Metasearch Engine	p. 313
Knowledge Base of Searching Agents	p. 313
Retrieval Process of a Searching Agent	p. 320
Cooperation between Searching Agents	p. 323
Recommendation Process	p. 323
Recommendation without User Data	p. 325
Recommendation with User Profiles	p. 326
Recommendation by Query Modification	p. 328
Conclusions	p. 333
Conclusions	p. 335
References	p. 337
Index	p. 349
Table of Contents provided by Publisher. All Rights Reserved.

Advanced Methods for Inconsistent Knowledge Management

At a Glance

Hardcover

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