Programming the Semantic Web

Table of Contents
Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
Part I. Semantic Data
1. Why Semantics? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Data Integration Across the Web 4
Traditional Data-Modeling Methods 5
Tabular Data 6
7
Relational Data
Evolving and Refactoring Schemas 9
Very Complicated Schemas 11
Getting It Right the First Time 12
Semantic Relationships 14
Metadata Is Data 16
Building for the Unexpected 16
“Perpetual Beta” 17
2. Expressing Meaning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
An Example: Movie Data 21
Building a Simple Triplestore 23
23
Indexes
The add and remove Methods 24
Querying 25
Merging Graphs 26
Adding and Querying Movie Data 28
Other Examples 29
Places 29
Celebrities 31
Business 33
3. Using Semantic Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
A Simple Query Language 37
Variable Binding 38
Implementing a Query Language 40
Feed-Forward Inference 43
Inferring New Triples 43
Geocoding 45
Chains of Rules 47
A Word About “Artificial Intelligence” 50
Searching for Connections 50
Six Degrees of Kevin Bacon 51
Shared Keys and Overlapping Graphs 53
Example: Joining the Business and Places Graphs 53
Querying the Joined Graph 54
Basic Graph Visualization 55
Graphviz 55
Displaying Sets of Triples 56
Displaying Query Results 57
Semantic Data Is Flexible 59
Part II. Standards and Sources
4. Just Enough RDF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
What Is RDF? 63
The RDF Data Model 64
URIs As Strong Keys 64
65
Resources
66
Blank Nodes
Literal Values 68
RDF Serialization Formats 68
A Graph of Friends 69
70
N-Triples
N3 72
RDF/XML 73
RDFa 76
Introducing RDFLib 80
Persistence with RDFLib 83
SPARQL 84
SELECT Query Form 86
OPTIONAL and FILTER Constraints 87
Multiple Graph Patterns 89
CONSTRUCT Query Form 91

ASK and DESCRIBE Query Forms 91
SPARQL Queries in RDFLib 92
Useful Query Modifiers 94
5. Sources of Semantic Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
Friend of a Friend (FOAF) 97
Graph Analysis of a Social Network 101
Linked Data 105
106
The Cloud of Data
Are You Your FOAF file? 107
Consuming Linked Data 110
Freebase 116
An Identity Database 117
RDF Interface 118
Freebase Schema 119
MQL Interface 121
Using the metaweb.py Library 123
Interacting with Humans 125
6. What Do You Mean, “Ontology”? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
What Is It Good For? 127
A Contract for Meaning 128
Models Are Data 128
129
An Introduction to Data Modeling
Classes and Properties 129
Modeling Films 132
Reifying Relationships 134
Just Enough OWL 135
140
Using Protégé
Creating a New Ontology 140
Editing an Ontology 141
Just a Bit More OWL 145
Functional and Inverse Functional Properties 146
Inverse Properties 146
Disjoint Classes 146
Keepin’ It Real 148
Some Other Ontologies 148
Describing FOAF 148
A Beer Ontology 149
This Is Not My Beautiful Relational Schema! 152
7. Publishing Semantic Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
Embedding Semantics 155
Microformats 156
RDFa 158
Yahoo! SearchMonkey 160
Google’s Rich Snippets 161
Dealing with Legacy Data 162
Internet Video Archive 162
Tables and Spreadsheets 167
Legacy Relational Data 169
RDFLib to Linked Data 172
Part III. Putting It into Practice
8. Overview of Toolkits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
Sesame 183
Using the Sesame Java API 184
RDFS Inferencing in Sesame 193
A Servlet Container for the Sesame Server 196
Installing the Sesame Web Application 196
The Workbench 197
Adding Data 199
200
SPARQL Queries
REST API 202
Other RDF Stores 203
Jena (Open Source) 204
Redland (Open Source) 204
Mulgara (Open Source) 204
OpenLink Virtuoso (Commercial and Open Source) 204
Franz AllegroGraph (Commercial) 205
Oracle (Commercial) 205
SIMILE/Exhibit 205
A Simple Exhibit Page 206
Searching, Filtering, and Prettier Views 209
Linking Up to Sesame 211
Timelines 212
9. Introspecting Objects from Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
RDFObject Examples 215
RDFObject Framework 217
How RDFObject Works 225
10. Tying It All Together . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227
A Job Listing Application 227
Application Requirements 228
Job Listing Data 228
Converting to RDF 228
Loading the Data into Sesame 231
Serving the Website 232
CherryPy 232
Mako Page Templates 233
A Generic Viewer 234
Getting Data from Sesame 236
The Generic Template 236
Getting Company Data 237
Crunchbase 238
Yahoo! Finance 241
Reconciling Freebase Connections 243
Specialized Views 244
Publishing for Others 248
RDFa 248
RDF/XML 250
Expanding the Data 251
Locations 251
Geography, Economy, Demography 252
Sophisticated Queries 253
Visualizing the Job Data 255
Further Expansion 258
Part IV. Epilogue
11. The Giant Global Graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261
Vision, Hype, and Reality 262
Participating in the Global Graph Community 264
Releasing Data into the Commons 265
License Considerations 266
267
The Data Cycle
Bracing for Continuous Change 268
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271


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Social Web Evolution

Table of Contents
Preface . .............................................................................................................................................. xvii
Chapter I
Exploring a Professional Social Network System to Support Learning in the Workplace...................... 1
Anthony “Skip” Basiel, Middlesex University – IWBL, UK

Paul Coyne, Emerald Group Publishing Ltd, UK
Chapter II
Knowledge Producing Megamachines: The Biggest Web 2.0 Communities of the Future ................. 17
Laszlo Z. Karvalics, University of Szeged, Hungary

Chapter III
Web 2.0 Driven Sustainability Reporting.............................................................................................. 31
Daniel Süpke, Carl von Ossietzky Universität Oldenburg, Germany

Jorge Marx Gómez, Carl von Ossietzky Universität Oldenburg, Germany
Ralf Isenmann, Fraunhofer Institute for Systems and Innovation Research Karlsruhe, Germany

Chapter IV
Mailing Lists and Social Semantic Web................................................................................................ 42
Sergio Fernández, Fundación CTIC, Spain

Diego Berrueta, Fundación CTIC, Spain
Lian Shi, Fundación CTIC, Spain
Jose E. Labra, University of Oviedo, Spain
Patricia Ordóñez de Pablos, University of Oviedo, Spain

Chapter V
Web 2.0 Social Networking Sites.......................................................................................................... 57
D. Sandy Staples, Queens University, Canada

Chapter VI
Teachers’ Personal Knowledge Management in China Based Web 2.0 Technologies.......................... 76
Jingyuan Zhao, Harbin Institute of Technology, China

Chapter VII
CUSENT: Social Sentiment Analysis Using Semantics for Customer Feedback ................................ 89
Ángel García-Crespo, Universidad Carlos III de Madrid, Spain

Ricardo Colomo-Palacios, Universidad Carlos III de Madrid, Spain

Myriam Mencke, Universidad Carlos III de Madrid, Spain
Juan M. Gómez-Berbís, Universidad Carlos III de Madrid, Spain
Chapter VIII
Can Knowledge Management Assist Firms to Move from Traditional to E-Commerce:
The Case of Greek Firms..................................................................................................................... 102
Irene Samanta, Technological Education Institute of Piraeus, Greece

Chapter IX
Knowledge Management and Lifelong Learning in Archival Heritage: Digital Collections
on a Semantic Scope for Educational Potential .................................................................................. 116
Trianta.llia Kourtoumi, General State Archives of Greece, Greece

Chapter X
Application of Web 2.0 Technology for Clinical Training ................................................................. 132
Adela Lau, The Hong Kong Polytechnic University, China

Eric Tsui, The Hong Kong Polytechnic University, China

Chapter XI
Pattern Matching Techniques to Identify Syntactic Variations of Tags in Folksonomies . ................. 138
F. Echarte, Universidad Pública de Navarra, Spain

J. J. Astrain, Universidad Pública de Navarra, Spain
A. Córdoba, Universidad Pública de Navarra, Spain
J. Villadangos, Universidad Pública de Navarra, Spain
Chapter XII
Insights into the Impact of Social Networks on Evolutionary Games................................................. 150
Katia Sycara, Carnegie Mellon University, USA

Paul Scerri, Carnegie Mellon University, USA

Anton Chechetka, Carnegie Mellon University, USA

Chapter XIII
Application of Semantic Web Based on the Domain-Specific Ontology for Global KM................... 160
Jaehun Joo, Dongguk University, Korea

Sang M. Lee, University of Nebraska – Lincoln, USA
Yongil Jeong, Saltlux, Inc., Korea

Chapter XIV
Designing Online Learning Communities to Encourage Cooperation................................................ 177
Miranda Mowbray, HP Laboratories Bristol, UK

Chapter XV
Building Virtual Learning Communities............................................................................................. 192
Naomi Augar, Deakin University, Australia

Ruth Raitman, Deakin University, Australia

Elicia Lanham, Deakin University, Australia
Wanlei Zhou, Deakin University, Australia
Chapter XVI
Support Networks for Rural and Regional Communities.................................................................... 216
Tom Denison, Monash University, Australia

Chapter XVII
Building Virtual Communities through a De-Marginalized View of Knowledge Networking........... 233
Kam Hou Vat, University of Macau, Macau

Chapter XVIII
A Basis for the Semantic Web and E-Business: Efficient Organization of Ontology Languages
and Ontologies..................................................................................................................................... 249
Changqing Li, National University of Singapore, Singapore

Tok Wang Ling, National University of Singapore, Singapore

Chapter XIX
User-Centered Design Principles for Online Learning Communities: A Sociotechnical
Approach for the Design of a Distributed Community of Practice..................................................... 267
Ben K. Daniel, University of Saskatchewan, Canada

David O’Brien, University of Saskatchewan, Canada
Asit Sarkar, University of Saskatchewan, Canada

Compilation of References................................................................................................................ 280
About the Contributors..................................................................................................................... 309
Index.................................................................................................................................................... 318


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Social Networks and the Semantic Web

In this book we provide two major case studies to demonstrate each of these opportunities. The first case study shows the possibilities of tracking a research community over the Web, combining the information obtained from the Web with other
data sources (publications, emails). The results are analyzed and correlated with performance measures, trying to predict what kind of social networks help researchers
succeed (Chapter 8). Social network mining from theWeb plays an impotant role in this case study for obtaining large scale, dynamic network data beyond the possibilities of survey methods. In turn semantic technology is the key to the representation and aggregation of information from multiple heterogeneous information sources (Chapters 4 and 5).

As the methods we are proposing are more generally applicable than the context of our scientometric study, most of this volume is spent on describing our methods rather than discussing the results. We summarize the possibilities for (re)using electronic data for network analysis in Chapter 3 and evaluate two methods of social network mining from theWeb in a separate study described in Chapter 7.We discuss semantic technology for social network data aggregation in Chapters 4 and 5. Lastly, we describe the implementation of our methods in the award-winning Flink system in Chapter 6. In fact these descriptions should not only allow the reader to reproduce
our work, but to apply our methods in a wide range of settings. This includes
adapting our methods to other social settings and other kinds of information sources, while preserving the advantages of a fully automated analysis process based on electronic data.

Our second study highlights the role of the social context in user-generated classifications of content, in particular in the tagging systems known as folksonomies
(Chapter 9). Tagging is widely applied in organizing the content in many Web 2.0
services, including the social bookmarking application del.icio.us and the photo sharing site Flickr. We consider folksonomies as lightweight semantic structures where
the semantics of tags emerges over time from the way tags are applied. We study
tagging systems using the concepts and methodology of network analysis. We establish
that folksonomies are indeed much richer in semantics than it might seem at
first and we show the dependence of semantics on the social context of application.
These results are particularly relevant for the development of the Semantic Web using
bottom-up, collaborative approaches. Putting the available knowledge in a social
context also opens the way to more personalized applications such as social search.

As the above descriptions show, both studies are characterized by an interdisciplinary
approach where we combine the concepts and methods of Artificial Intelligence with those of Social Network Analysis. However, we will not assume any particularly knowledge of these fields on the part of the reader and provide the necessary
introductions to both (Chapters 1 and 2). These introductions should allow access to our work for both social scientists with an interest in electronic data and for information scientists with an interest in social-semantic applications.

Our primary goal is not to teach any of these disciplines in detail but to provide an insight for both Social and Information Scientists into the concepts and methods from outside their respective fields. We show a glimpse of the benefits that this understanding could bring in addressing complex outstanding issues that are inherently
interdisciplinary in nature. Our hope is then to inspire further creative experimentation toward a better understanding of both online social interaction and the nature of human knowledge. Such understanding will be indispensable in a world where the border between these once far-flung disciplines is expected to shrink rapidly through more and more socially immersive online environments such as the virtual worlds of Second Life. Only when equipped with the proper understanding will we succeed in designing systems that show true intelligence in both reasoning and social capabilities and are thus able to guide us through an ever more complex online universe.

The Author would like to acknowledge the support of the Vrije Universiteit Research
School for Business Information Sciences (VUBIS) in conducting the research contained in this volume.

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TOWARDS THE SEMANTIC WEB

Contents
Foreword xiii
Biographies xv
List of Contributors xix
Acknowledgments xxi
1 Introduction 1
John Davies, Dieter Fensel and Frank van Harmelen
1.1 The Semantic Web and Knowledge Management 2
1.2 The Role of Ontologies 4
1.3 An Architecture for Semantic Web-based Knowledge Management 5
1.3.1 Knowledge Acquisition 5
1.3.2 Knowledge Representation 6
1.3.3 Knowledge Maintenance 7
1.3.4 Knowledge Use 7
1.4 Tools for Semantic Web-based Knowledge Management 7
1.4.1 Knowledge Acquisition 8
1.4.2 Knowledge Representation 8
1.4.3 Knowledge Maintenance 8
1.4.4 Knowledge Use 8
2 OIL and DAML1OIL: Ontology Languages for the Semantic Web 11
Dieter Fensel, Frank van Harmelen and Ian Horrocks
2.1 Introduction 11
2.2 The Semantic Web Pyramid of Languages 12
2.2.1 XML for Data Exchange 12
2.2.2 RDF for Assertions 13
2.2.3 RDF Schema for Simple Ontologies 14
2.3 Design Rationale for OIL 15
2.3.1 Frame-based Systems 16
2.3.2 Description Logics 17
2.3.3 Web Standards: XML and RDF 17
2.4 OIL Language Constructs 17
2.4.1 A Simple Example in OIL 18
2.5 Different Syntactic Forms 20
2.6 Language Layering 23
2.7 Semantics 26
2.8 From OIL to DAML1OIL 26
2.8.1 Integration with RDFS 26
2.8.2 Treatment of Individuals 29
2.8.3 DAML1OIL Data Types 29
2.9 Experiences and Future Developments 31
3 A Methodology for Ontology-based Knowledge Management 33
York Sure and Rudi Studer
3.1 Introduction 33
3.2 Feasibility Study 34
3.3 Kick Off Phase 38
3.4 Refinement Phase 41
3.5 Evaluation Phase 41
3.6 Maintenance and Evolution Phase 42
3.7 Related Work 42
3.7.1 Skeletal Methodology 43
3.7.2 KACTUS 44
3.7.3 Methontology 44
3.7.4 Formal Tools of Ontological Analysis 45
3.8 Conclusion 45
4 Ontology Management: Storing, Aligning and Maintaining Ontologies 47
Michel Klein, Ying Ding, Dieter Fensel and Borys Omelayenko
4.1 The Requirement for Ontology Management 47
4.2 Aligning Ontologies 48
4.2.1 Why is Aligning Needed 48
4.2.2 Aligning Annotated XML Documents 49
4.2.3 Mapping Meta-ontology 50
4.2.4 Mapping in OIL 53
4.3 Supporting Ontology Change 54
4.3.1 Ontologies are Changing 54
4.3.2 Changes in Ontologies Involve Several Problems 55
4.3.3 Change Management 58
4.4 Organizing Ontologies 61
4.4.1 Sesame Requirements 62
4.4.2 Functionality of an Ontology Storage System 62
4.4.3 Current Storage Systems 64
4.4.4 Requirements for a Storage System 66
4.5 Summary 69
5 Sesame: A Generic Architecture for Storing and Querying RDF and RDF
Schema 71
Jeen Broekstra, Arjohn Kampman and Frank van Harmelen
5.1 The Need for an RDFS Query Language 72
5.1.1 Querying at the Syntactic Level 72
5.1.2 Querying at the Structure Level 73
5.1.3 Querying at the Semantic Level 75
5.2 Sesame Architecture 76
5.2.1 The RQL Query module 78
5.2.2 The Admin Module 79
5.2.3 The RDF Export Module 80
5.3 The SAIL API 80
5.4 Experiences 82
5.4.1 Application: On-To-Knowledge 82
5.4.2 RDFS in Practice 84
5.4.3 PostgreSQL and SAIL 84
5.4.4 MySQL 86
5.5 Future Work 87
5.5.1 Transaction Rollback Support 87
5.5.2 Versioning Support 88
5.5.3 Adding and Extending Functional Modules 88
5.5.4 DAML1OIL Support 88
5.6 Conclusions 88
6 Generating Ontologies for the Semantic Web: OntoBuilder 91
R.H.P. Engels and T.Ch. Lech
6.1 Introduction 91
6.1.1 OntoBuilder and its Relation to the CORPORUM System 92
6.1.2 OntoExtract 93
6.1.3 OntoWrapper and TableAnalyser 96
6.2 Reading the Web 97
6.2.1 Semantics on the Internet 97
6.2.2 Problems with Retrieving Natural Language Texts from Documents 99
6.2.3 Document Handling 100
6.2.4 Normalization 100
6.2.5 Multiple Discourses 101
6.2.6 Document Class Categorization 102
6.2.7 Writing Style 102
6.2.8 Layout Issues 102
6.3 Information Extraction 103
6.3.1 Content-driven Versus Goal-driven 104
6.3.2 Levels of Linguistic Analysis 104
6.3.3 CognIT Vision 107
6.4 Knowledge Generation from Natural Language Documents 108
6.4.1 Syntax Versus Semantics 108
6.4.2 Generating Semantic Structures 109
6.4.3 Generating Ontologies from Textual Resources 110
6.4.4 Visualization and Navigation 111
6.5 Issues in Using Automated Text Extraction for Ontology Building using IE
on Web Resources 111
7 OntoEdit: Collaborative Engineering of Ontologies 117
York Sure, Michael Erdmann and Rudi Studer
7.1 Introduction 117
7.2 Kick Off Phase 118
7.3 Refinement Phase 123
7.3.1 Transaction Management 124
7.3.2 Locking Sub-trees of the Concept Hierarchy 126
7.3.3 What Does Locking a Concept Mean? 127
7.4 Evaluation Phase 128
7.4.1 Analysis of Typical Queries 128
7.4.2 Error Avoidance and Location 129
7.4.3 Usage of Competency Questions 129
7.4.4 Collaborative Evaluation 130
7.5 Related Work 130
7.6 Conclusion 131
8 QuizRDF: Search Technology for the Semantic Web 133
John Davies, Richard Weeks and Uwe Krohn
8.1 Introduction 133
8.2 Ontological Indexing 135
8.3 Ontological Searching 138
8.4 Alternative data models 141
8.4.1 Indexing in the New Model 141
8.4.2 Searching in the New Model 142
8.5 Further Work 142
8.5.1 Technical Enhancements 142
8.5.2 Evaluation 143
8.6 Concluding Remarks 143
9 Spectacle 145
Christiaan Fluit, Herko ter Horst, Jos van der Meer, Marta Sabou
and Peter Mika
9.1 Introduction 145
9.2 Spectacle Content Presentation Platform 145
9.2.1 Ontologies in Spectacle 146
9.3 Spectacle Architecture 147
9.4 Ontology-based Mapping Methodology 147
9.4.1 Information Entities 149
9.4.2 Ontology Mapping 149
9.4.3 Entity Rendering 150
9.4.4 Navigation Specification 150
9.4.5 Navigation Rendering 151
9.4.6 Views 152
9.4.7 User Profiles 152
9.5 Ontology-based Information Visualization 153
9.5.1 Analysis 153
9.5.2 Querying 156
9.5.3 Navigation 158
9.6 Summary: Semantics-based Web Presentations 159
10 OntoShare: Evolving Ontologies in a Knowledge Sharing System 161
John Davies, Alistair Duke and Audrius Stonkus
10.1 Introduction 161
10.2 Sharing and Retrieving Knowledge in OntoShare 162
10.2.1 Sharing Knowledge in OntoShare 163
10.2.2 Ontological Representation 164
10.2.3 Retrieving Explicit Knowledge in OntoShare 167
10.3 Creating Evolving Ontologies 169
10.4 Expertise Location and Tacit Knowledge 170
10.5 Sociotechnical Issues 172
10.5.1 Tacit and Explicit Knowledge Flows 172
10.5.2 Virtual Communities 173
10.6 Evaluation and Further Work 175
10.7 Concluding Remarks 176
11 Ontology Middleware and Reasoning 179
Atanas Kiryakov, Kiril Simov and Damyan Ognyanov
11.1 Ontology Middleware: Features and Architecture 179
11.1.1 Place in the On-To-Knowledge Architecture 181
11.1.2 Terminology 182
11.2 Tracking Changes, Versioning and Meta-information 183
11.2.1 Related Work 184
11.2.2 Requirements 184
11.3 Versioning Model for RDF(S) Repositories 185
11.3.1 History, Passing through Equivalent States 188
11.3.2 Versions are Labelled States of the Repository 188
11.3.3 Implementation Approach 188
11.3.4 Meta-information 190
11.4 Instance Reasoning for DAML1OIL 192
11.4.1 Inference Services 194
11.4.2 Functional Interfaces to a DAML1OIL Reasoner 195
12 Ontology-based Knowledge Management at Work: The Swiss Life Case
Studies 197
Ulrich Reimer, Peter Brockhausen, Thorsten Lau and Jacqueline R. Reich
12.1 Introduction 197
12.2 Skills Management 198
12.2.1 What is Skills Management? 198
12.2.2 SkiM: Skills Management at Swiss Life 200
12.2.3 Architecture of SkiM 202
12.2.4 SkiM as an Ontology-based Approach 203
12.2.5 Querying Facilities 207
12.2.6 Evaluation and Outlook 208
12.3 Automatically Extracting a ‘Lightweight Ontology’ from Text 209
12.3.1 Motivation 209
12.3.2 Automatic Ontology Extraction 210
12.3.3 Employing the Ontology for Querying 213
12.3.4 Evaluation and Outlook 215
12.4 Conclusions 217
13 Field Experimenting with Semantic Web Tools in a Virtual Organization 219
Victor Iosif, Peter Mika, Rikard Larsson and Hans Akkermans
13.1 Introduction 219
13.2 The EnerSearch Industrial Research Consortium as a Virtual Organization 219
13.3 Why Might Semantic Web Methods Help? 222
13.4 Design Considerations of Semantic Web Field Experiments 223
13.4.1 Different Information Modes 224
13.4.2 Different Target User Groups 224
13.4.3 Different Individual Cognitive Styles 225
13.4.4 Hypotheses to be Tested 228
13.5 Experimental Set-up in a Virtual Organization 229
13.5.1 Selecting Target Test Users 229
13.5.2 Tools for Test 230
13.5.3 Test Tasks and their Organization 230
13.5.4 Experimental Procedure 231
13.5.5 Determining What Data to Collect 232
13.5.6 Evaluation Matrix and Measurements 233
13.6 Technical and System Aspects of Semantic Web Experiments 234
13.6.1 System Design 234
13.6.2 Ontology Engineering, Population, Annotation 235
13.7 Ontology-based Information Retrieval: What Does it Look Like? 236
13.7.1 Ontology and Semantic Sitemaps 236
13.7.2 Semantics-based Information Retrieval 239
13.8 Some Lessons Learned 241
14 A Future Perspective: Exploiting Peer-to-Peer and the Semantic Web for
Knowledge Management 245
Dieter Fensel, Steffen Staab, Rudi Studer, Frank van Harmelen
and John Davies
14.1 Introduction 245
14.2 A Vision of Modern Knowledge Management 247
14.2.1 Knowledge Integration 247
14.2.2 Knowledge Categorization 247
14.2.3 Context Awareness 248
14.2.4 Personalization 248
14.2.5 Knowledge Portal Construction 249
14.2.6 Communities of Practice 249
14.2.7 P2P Computing and its Implications for KM 250
14.2.8 Virtual Organizations and their Impact 251
14.2.9 eLearning Systems 251
14.2.10 The Knowledge Grid 251
14.2.11 Intellectual Capital Valuation 252
14.3 A Vision of Ontologies: Dynamic Networks of Meaning 252
14.3.1 Ontologies or How to Escape a Paradox 253
14.3.2 Heterogeneity in Space: Ontology as Networks of Meaning 254
14.3.3 Development in Time: Living Ontologies 255
14.4 Peer-2-Peer, Ontologies and Knowledge 256
14.4.1 Shortcomings of Peer-2-Peer and Ontologies as Isolated Paradigms
256
14.4.2 Challenges in Integrating Peer-2-Peer and Ontologies 258
14.5 Conclusions 263
14.5.1 P2P for Knowledge Management 263
14.5.2 P2P for Ontologies 263
14.5.3 Ontologies for P2P and Knowledge Management 264
14.5.4 Community Building 264
15 Conclusions: Ontology-driven Knowledge Management – Towards the
Semantic Web? 265
John Davies, Dieter Fensel and Frank van Harmelen
References 267
Index 281

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