Advanced Methods for Inconsistent Knowledge Management

Front Cover
Springer Science & Business Media, Sep 12, 2007 - Business & Economics - 352 pages
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.
 

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Contents

1 Inconsistency of Knowledge
1
12 Levels of Knowledge Inconsistency
5
13 Knowledge Inconsistency and Integration
7
14 The Subject of this Book
8
15 The Structure of this Book
9
2 Model of Knowledge Conflict
13
22 What is Conflict?
16
23 Conflict Representation
18
6 Processing Inconsistency on the Semantic Level
165
62 Conjunction Structure
166
622 Conjunctions of Literals
167
623 Distance Function between Attribute Values
175
624 Inconsistency Representation
176
625 Integration Problem
178
63 Disjunction Structure
185
632 Inconsistency Representation
192

232 Definition of Knowledge Conflict
21
233 Credibility Degree of Conflict Participants
24
242 Postulates for Consistency Functions
26
243 Analysis of Postulates
32
244 Consistency Functions
38
245 Reflecting Weights in Consistency Measure
43
246 Practical Aspect of Consistency Measures
44
25 Conclusions
46
3 Consensus as a Tool for Conflict Solving
47
32 Consensus Theory A Case Study
48
322 Consensus versus Conflicts
52
33 Consensus Functions
55
332 Postulates for Consensus Function
56
333 Analysis of Postulates
59
334 Other Consensus Choice Functions
70
34 Quality of Consensus
73
35 Susceptibility to Consensus
76
351 Criteria for Consensus Susceptibility
77
352 Consensus Susceptibility versus Consistency
84
36 Methods for Achieving Consensus Susceptibility
87
361 Profile Modification
88
362 Using Weights
89
37 Reduction of Number of Consensuses
95
371 Additional Criterion
96
372 Profile Modification
98
38 Conclusions
100
4 Model for Knowledge Integration
101
42 A General Model for Knowledge Integration
103
422 Distance Functions between Attribute Values
105
4221 Functions Minimizing Transformation Costs
106
4222 Functions Reflecting Element Shares in the Distance
108
43 Knowledge Integration Problem
113
44 Postulates for Knowledge Integration
115
45 Algorithms for Integration
120
46 Conclusions
122
5 Processing Inconsistency on the Syntactic Level
123
52 Conjunctive Structure of Knowledge
124
522 Distance Function between Conjunctions
127
523 Integration Problem and Postulates for Consensus
129
524 Analysis of Postulates
132
525 Heuristic Algorithm for Determining Consensus
141
53 Disjunctive Structure of Knowledge
145
531 Basic Notions
146
532 Distance Function between Clauses
149
533 Integration Problem and Postulates for Consensus
150
534 Heuristic Algorithm for Consensus Determination
156
54 Fuzzy Structure of Knowledge
158
541 Basic Notions
159
543 Integration Problem and Algorithm for Consensus Choice
161
55 Conclusions
163
633 Integration Problem and Consensus
193
64 Dependencies of Attributes
194
65 Conclusions
201
7 Consensus for Fuzzy Conflict Profiles
202
72 Basic Notions
204
73 Postulates for Consensus
207
74 Analysis of Postulates
211
75 Algorithms for Consensus Choice
216
76 Conclusions
222
8 Processing Inconsistency of Expert Knowledge
223
82 Basic Notions
226
83 Consensus Determination Problems
227
84 The Quality Analysis
232
85 Conclusions
239
9 Ontology Integration
241
92 Problem of Ontology Integration
244
93 Inconsistency between Ontologies
245
932 Inconsistency on the Instance Level
247
933 Inconsistency on the Concept Level
248
934 Inconsistency on the Relation Level
251
935 Some Remarks
253
942 For the Concept Level
254
943 For the Relation Level
258
95 Conclusions
262
10 Application of Inconsistency Resolution Methods in Intelligent Learning Systems
263
102 Structure of Knowledge
266
1022 Distance Functions between Scenarios
271
103 Learner Profile and Classification
277
1032 Usage Data
279
104 Recommendation Process
281
1042 Algorithm for Determination of Opening Scenario
283
105 Learner Clustering Process
289
106 Rough Learner Classification Method
292
1062 Our Concept
293
1064 Rough Learner Classification
296
107 Conclusions
306
11 Processing Inconsistency in Information Retrieval
307
112 Agent Technology for Information Retrieval
310
113 A Conception for a Metasearch Engine
313
1132 Retrieval Process of a Searching Agent
320
1133 Cooperation between Searching Agents
323
1141 Recommendation without User Data
325
1142 Recommendation with User Profiles
326
1143 Recommendation by Query Modification
328
115 Conclusions
333
12 Conclusions
334
References
337
Index
348
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Page 342 - McElligot M, Sorensen H (1994) An evolutionary connectionist approach to personal information filtering. In: Proceeding of the Fourth Irish Neural Network Conference, Dublin, Ireland, pp.