Model Driven Architecture And... | Buy

Defining a formal domain ontology is generally considered a useful, not to say necessary step in almost every software project. This is because software deals with ideas rather than with self-evident physical artefacts. However, this development step is hardly ever done, as ontologies rely on well-defined and semantically powerful AI concepts such as description logics or rule-based systems, and most software engineers are largely unfamiliar with these. Ga?!evic and his co-authors try to fill this gap by covering the subject of MDA application for ontology development on the Semantic Web. Part I of their book describes existing technologies, tools, and standards like XML, RDF, OWL, MDA, and UML. Part II presents the first detailed description of OMGa??s new ODM (Ontology Definition Metamodel) initiative, a specification which is expected to be in the form of an OMG language like UML. Finally, Part III is dedicated to applications and practical aspects of developing ontologies using MDA-based languages. The book is supported by a website showing many ontologies, UML and other MDA-based models, and the transformations between them. "The book is equally suited to those who merely want to be informed of the relevant technological landscape, to practitioners dealing with concrete problems, and to researchers seeking pointers to potentially fruitful areas of research. The writing is technical yet clear and accessible, illustrated throughout with useful and easily digestible examples." from the Foreword by Bran Selic, IBM Rational Software, Canada. "I do not know another book that offers such a high quality insight into UML and ontologies." Steffen Staab, U Koblenz, Germany

Dragan Ga+íevic is a lecturer of Computer Science with the Military academy, Belgrade Serbia and Montenegro, as well as a researcher with the GOOD OLD AI research group, University of Belgrade. He has received his BS, MS, and PhD degrees in computer science from the University of Belgrade in 2000, 2002, and 2004, respectively. His research interests mostly include Semantic Web, ontologies, MDA, and applications of artificial intelligence techniques to education. Dragan Djuric is a PhD candidate at FON - School of Business Administration, University of Belgrade, and also a researcher with the GOOD OLD AI research group. His interests mostly include Enterprise software architecture, Object-Oriented development, Java platform and Intelligent Information Systems. Vladan Deved++ic is an associate professor of computer science at the Department of Information Systems, FON - School of Business Administration, University of Belgrade, Serbia and Montenegro. He is also the head of the GOOD OLD AI research group. His main research interests include software engineering, intelligent systems, knowledge representation, ontologies, Semantic Web, intelligent reasoning, and applications of artificial intelligence techniques to education and medicine.

Part I Basics

1. Knowledge Representation

3

(42)

1.1 Basic Concepts

4

(3)

1.2 Cognitive Science

7

(4)

1.3 Types of Human Knowledge

11

(3)

1.4 Knowledge Representation Techniques

14

(5)

1.4.1 Object—Attribute—Value Triplets

15

(1)

1.4.2 Uncertain Facts

15

(1)

1.4.3 Fuzzy Facts

16

(1)

1.4.4 Rules

17

(1)

1.4.5 Semantic networks

18

(1)

1.4.6 Frames

19

(1)

1.5 Knowledge Representation Languages

19

(17)

1.5.1 Logic-Based Representation Languages

20

(7)

1.5.2 Frame-Based Representation Languages

27

(2)

1.5.3 Rule-Based Representation Languages

29

(3)

1.5.4 Visual Languages for Knowledge Representation

32

(3)

1.5.5 Natural Languages and Knowledge Representation

35

(1)

1.6 Knowledge Engineering

36

(3)

1.7 Open Knowledge Base Connectivity (OKBC)

39

(2)

1.8 The Knowledge Level

41

(4)

2. Ontologies

45

(34)

2.1 Basic Concepts

46

(12)

2.1.1 Definitions

46

(2)

2.1.2 What Do Ontologies Look Like?

48

(2)

2.1.3 Why Ontologies?

50

(5)

2.1.4 Key Application Areas

55

(2)

2.1.5 Examples

57

(1)

2.2 Ontological Engineering

58

(11)

2.2.1 Ontology Development Tools

58

(7)

2.2.2 Ontology Development Methodologies

65

(4)

2.3 Applications

69

(3)

2.3.1 Magpie

69

(1)

2.3.2 Briefing Associate

70

(1)

2.3.3 Quickstep and Foxtrot

71

(1)

2.4 Advanced Topics

72

(7)

2.4.1 Metadata, Metamodeling, and Ontologies

72

(2)

2.4.2 Standard Upper Ontology

74

(2)

2.4.3 Ontological Level

76

(3)

3. The Semantic Web

79

(30)

3.1 Rationale

80

(1)

3.2 Semantic Web Languages

81

(14)

3.2.1 XML and XML Schema

81

(3)

3.2.2 RDF and RDF Schema

84

(3)

3.2.3 DAML+OIL

87

(3)

3.2.4 OWL

90

(2)

3.2.5 SPARQL

92

(3)

3.3 The Role of Ontologies

95

(1)

3.4 Semantic Markup

96

(4)

3.5 Semantic Web Services

100

(4)

3.6 Open Issues

104

(3)

3.7 Quotations

107

(2)

4. The Model Driven Architecture (MDA)

109

(18)

4.1 Models and Metamodels

109

(1)

4.2 Platform-Independent Models

110

(2)

4.3 Four-Layer Architecture

112

(2)

4.4 The Meta-Object Facility

114

(3)

4.5 Specific MDA Metamodels

117

(3)

4.5.1 Unified Modeling Language

117

(1)

4.5.2 Common Warehouse Metamodel (CWM)

118

(1)

4.5.3 Ontology Definition Metamodel

119

(1)

4.6 UML Profiles

120

(3)

4.6.1 Examples of UML Profiles

121

(2)

4.7 An XML for Sharing MDA Artifacts

123

(3)

4.8 The Need for Modeling Spaces

126

(1)

5. Modeling Spaces

127

(18)

5.1 Modeling the Real World

128

(1)

5.2 The Real World, Models, and Metamodels

129

(2)

5.3 The Essentials of Modeling Spaces

131

(3)

5.4 Modeling Spaces Illuminated

134

(3)

5.5 A Touch of RDF(S) and MOF Modeling Spaces

137

(2)

5.6 A Touch of the Semantic Web and MDA Technical Spaces

139

(2)

5.7 Instead of Conclusions

141

(4)

Part II The Model Driven Architecture and Ontologies

6. Software Engineering Approaches to Ontology Development

145

(28)

6.1 A Brief History of Ontology Modeling

145

(15)

6.1.1 Networked Knowledge Representation and Exchange Using UML and RDF

145

(5)

6.1.2 Extending the Unified Modeling Language for Ontology Development

150

(5)

6.1.3 The Unified Ontology Language

155

(1)

6.1.4 UML for the Semantic Web: Transformation-Based Approach

156

(3)

6.1.5 The AIFB OWL DL Metamodel

159

(1)

6.1.6 The GOOD OLD AI ODM Proposal

160

(1)

6.2 Ontology Development Tools Based on Software Engineering Techniques

160

(8)

6.2.1 Protégé

161

(3)

6.2.2 DUET (DAML UML Enhanced Tool)

164

(1)

6.2.3 An Ontology Tool for IBM Rational Rose UML Models

165

(2)

6.2.4 Visual Ontology Modeler (VOM)

167

(1)

6.3 Summary of Relations Between UML and Ontologies

168

(5)

6.3.1 Summary of Approaches and Tools for Software Engineering-Based Ontology Development

169

(1)

6.3.2 Summary of Differences Between UML and Ontology Languages

169

(3)

6.3.3 Future Development

172

(1)

7. The MDA-Based Ontology Infrastructure

173

(8)

7.1 Motivation

173

(1)

7.2 Overview

174

(2)

7.3 Bridging RDF(S) and MOF

176

(2)

7.4 Design Rationale for the Ontology UML Profile

178

(3)

8. The Ontology Definition Metamodel (ODM)

181

(20)

8.1 ODM Metamodels

181

(2)

8.2 A Few Issues Regarding the Revised Joint Submission

183

(1)

8.3 The Resource Description Framework Schema (RDFS) metamodel

184

(6)

8.4 The Web Ontology Language (OWL) Metamodel

190

(11)

9. The Ontology UML Profile

201

(10)

9.1 Classes and Individuals in Ontologies

201

(3)

9.2 Properties of Ontologies

204

(2)

9.3 Statements

206

(1)

9.4 Different Versions of the Ontology UML Profile

207

(4)

10. Mappings of MDA-Based Languages and Ontologies

211

(18)

10.1 Relations Between Modeling Spaces

211

(3)

10.2 Transformations Between Modeling Spaces

214

(3)

10.3 Example of an Implementation: an XSLT-Based Approach

217

(12)

10.3.1 Implementation Details

218

(1)

10.3.2 Transformation Example

219

(3)

10.3.3 Practical Experience

222

(3)

10.3.4 Discussion

225

(4)

Part III Applications

11. Using UML Tools for Ontology Modeling

229

(26)

11.1 MagicDraw

230

(17)

11.1.1 Starting with MagicDraw

230

(2)

11.1.2 Things You Should Know when Working with UML Profiles

232

(2)

11.1.3 Creating a New Ontology

234

(3)

11.1.4 Working with Ontology Classes

237

(3)

11.1.5 Working with Ontology Properties

240

(4)

11.1.6 Working with Individuals

244

(2)

11.1.7 Working with Statements

246

(1)

11.2 Poseidon for UML

247

(4)

11.2.1 Modeling Ontology Classes in Poseidon

249

(1)

11.2.2 Modeling Ontology Individuals and Statements in Poseidon

250

(1)

11.3 Sharing UML Models Between UML tools and Protégé Using the UML Back End

251

(4)

12. An MDA Based Ontology Platform: AIR

255

(12)

12.1 Motivation

255

(1)

12.2 The Basic Idea

256

(2)

12.3 Metamodel – the Conceptual Building Block of AIR

258

(1)

12.4 The AIR Metadata Repository

259

(3)

12.5 The AIR Workbench

262

(2)

12.6 The Role of XML Technologies

264

(1)

12.7 Possibilities

265

(2)

13. Examples of Ontology

267

(11)

13.1 Petri Net Ontology

267

(11)

13.1.1 Organization of the Petri Net Ontology

269

(3)

13.1.2 The Core Petri Net Ontology in the Ontology UML Profile

272

(3)

13.1.3 Example of an Extension: Upgraded Petri Nets

275

(3)

13.2 Educational Ontologies

278

(13)

13.2.1 Conceptual Solution

279

(2)

13.2.2 Mapping the Conceptual Model to Ontologies

281

(10)

References

291

(14)

Index

305

What is included with this book?

The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.

The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.