Search and Find
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Ontology Alignment - Bridging the Semantic Gap
of: Marc Ehrig
Springer-Verlag, 2006
ISBN: 9780387365015 , 252 Pages
Format: PDF, Read online
Copy protection: DRM
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
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