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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 |
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