Ontology Alignment - Bridging the Semantic Gap

von: Marc Ehrig

Springer-Verlag, 2006

ISBN: 9780387365015 , 248 Seiten

Format: PDF, OL

Kopierschutz: Wasserzeichen

Windows PC,Mac OSX Apple iPad, Android Tablet PC's Online-Lesen für: Windows PC,Mac OSX,Linux

Preis: 154,69 EUR

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Mehr zum Inhalt

Ontology Alignment - Bridging the Semantic Gap


 

Contents

6

List of Figures

12

Preface

16

Introduction and Overview

19

1.1 Motivation

19

1.2 Contribution

21

1.2.1 Problem Outline

21

1.2.2 Solution Pathway

22

1.3 Overview

23

1.3.1 Structure

23

1.3.2 Reader's Guide

24

Part I Foundations

26

Definitions

27

2.1 Ontology

27

2.1.1 Ontology Definition

27

2.1.2 Semantic Web and Web Ontology Language (OWL)

30

2.1.3 Ontology Example

32

2.2 Ontology Alignment

35

2.2.1 Ontology Alignment Definition

35

2.2.2 Ontology Alignment Representation

36

2.2.3 Ontology Alignment Example

37

2.3 Related Terms

39

2.4 Ontology Similarity

41

2.4.1 Ontology Similarity Definition

41

2.4.2 Similarity Layers

42

2.4.3 Specific Similarity Measures

44

2.4.4 Similarity in Related Work

50

2.4.5 Heuristic Definition

50

Scenarios

52

3.1 Use Cases

52

3.1.1 Alignment Discovery

53

3.1.2 Agent Negotiation / Web Service Composition

53

3.1.3 Data Integration

54

3.1.4 Ontology Evolution / Versloning

55

3.1.5 Ontology Merging

55

3.1.6 Query and Answer Rewriting / Mapping

56

3.2 Requirements

57

Related Work

59

4.1 Theory of Alignment

59

4.1.1 Algebraic Approach

59

4.1.2 Information- Flow- based Approach

60

4.1.3 Translation Framework

61

4.2 Existing Alignment Approaches

61

4.2.1 Classification Guidelines for Alignment Approaches

61

4.2.2 Ontology Alignment Approaches

63

4.2.3 Schema Alignment Approaches

67

4.2.4 Global as View / Local as View

70

Part II Ontology Alignment Approach

73

Process

74

5.1 General Process

74

5.2 Alignment Approach

77

5.2.0 Input

77

5.2.1 Feature Engineering

78

5.2.2 Search Step Selection

80

5.2.3 Similarity Computation

81

5.2.5 Interpretation

85

5.2.6 Iteration

87

5.2.7 Output

88

5.3 Process Description of Related Approaches

89

5.3.1 PROMPT, Anchor- PROMPT

89

5.3.2 GLUE

91

5.3.3 OLA

92

5.4 Evaluation of Alignment Approach

94

5.4.1 Evaluation Scenario

94

5.4.2 Evaluation Measures

95

5.4.3 Absolute Quality

101

5.4.4 Data Sets

101

5.4.5 Strategies

104

5.4.6 Results

105

5.4.7 Discussion and Lessons Learned

108

Advanced Methods

110

6.1 Efficiency

110

6.1.1 Challenge

110

6.1.2 Complexity

111

6.1.5 Discussion and Lessons Learned

119

6.2 Machine Learning

120

6.2.1 Challenge

120

6.2.2 Machine Learning for Ontology Alignment

121

6.2.3 Runtime Alignment

126

6.2.4 Explanatory Component of Decision Trees

127

6.2.5 Evaluation Scenarios: Training and Test Data Sets

128

6.2.6 Discussion and Lessons Learned

130

6.3 Active Alignment

132

6.3.1 Challenge

132

6.3.2 Ontology Alignment with User Interaction

133

6.3.3 Evaluation

134

6.4 Adaptive Alignment

137

6.4.1 Challenge

137

6.4.2 Overview

138

6.4.3 Create Utility Function

138

6.4.4 Derive Requirements for Result Dimensions

140

6.4.5 Derive Parameters

141

6.4.6 Example

144

6.4.7 Evaluation

145

6.4.8 Discussion and Lessons Learned

146

6.5 Integrated Approach

148

6.5.1 Integrating the Individual Approaches

148

6.5.2 Summary of Ontology Alignment Approaches

149

6.5.3 Evaluation

149

6.5.4 Discussion and Lessons Learned

151

Part III Implementation and Application

156

Tools

157

7.1 Basic Infrastructure for Ontology Alignment and Mapping - FOAM

157

7.1.1 User Example

157

7.1.2 Process Implementation

158

7.1.3 Underlying Software

159

7.1.4 Availability and Open Usage

160

7.1.5 Summary

161

7.2 Ontology Mapping Based on Axioms

161

7.2.1 Logics and Inferencing

162

7.2.2 Formalization of Similarity Rules as Logical Axioms

163

7.2.3 Evaluation

164

7.3 Integration into Ontology Engineering Platform

165

7.3.1 OntoStudio

165

7.3.2 OntoMap

166

7.3.3 FOAM in OntoMap

167

Semantic Web and Peer-to-Peer — SWAP

168

8.1 Project Description

168

8.1.1 Core Technologies

169

8.2 Bibster

170

8.2.1 Scenario

171

8.2.2 Design

171

8.2.3 Ontology Alignment / Duplicate Detection

174

8.2.4 Application

177

8.3 Xarop

178

8.3.1 Scenario

178

8.3.2 Design

180

8.3.3 Ontology Alignment

184

8.3.4 Application

185

Semantically Enabled Knowledge Technologies - SEKT

186

9.1 Project Description

186

9.2 Intelligent Integrated Decision Support for Legal Professionals

188

9.2.1 Scenario

188

9.2.2 Use Cases

188

9.2.3 Design

189

9.3 Retrieving and Sharing Knowledge in a Digital Library

190

9.3.1 Scenario

190

9.3.2 Use Cases

190

Part IV Towards Next Generation Semantic Alignment

193

Next Steps

194

10.1 Generalization

194

10.1.1 Situation

194

10.1.2 Generalized Process

195

10.1.3 Alignment of Petri Nets

196

10.1.4 Summary

200

10.2 Complex Alignments

201

10.2.1 Situation

201

10.2.2 Types of Complex Alignments

202

10.2.3 Extended Process for Complex Alignments

203

10.2.4 Implementation and Discussion

204

Future

205

11.1 Outlook

205

11.2 Limits for Alignment

207

11.2.1 Errors

207

11.2.2 Points of Mismatch

208

11.2.3 Implications

209

Conclusion

211

12.1 Content Summary

211

12.2 Assessment of Contribution

213

Part V Appendix

217

A Ontologies

218

B Complete Evaluation Results

222

C FOAM Tool Details

228

References

233

Index

250