Visualizing the Semantic Web (eBook)

Preface 6
Contents 9
PART 1 Semantic, Visual, and Technological Facets of the Second-Generation Web 15
Chapter 1 The Concept and Architecture of the Semantic Web 17
1.1 From HTML to XML and the Semantic Web 17
1.2 The XML Family of Technologies 23
1.3 The Architecture of the Semantic Web 27
1.4 References 31
Chapter 2 Information Visualization and the Semantic Web 33
2.1 Introduction 33
2.2 The Semantic Web 33
2.2.1 Visualization Issues 34
2.2.2 Semantic Annotation 35
2.3 Information Visualization 41
2.3.1 Tracking Knowledge and Technology Trends 42
2.3.2 Citation Analysis 42
2.3.3 Patent Citation Analysis 43
2.4 A Harmonious Relationship? 44
2.4.1 Beyond Information Retrieval 44
2.4.2 Yin and Yang 47
2.4.3 An Illustrative Example 48
2.5 Conclusion 56
2.6 References 56
Chapter 3 Ontology-Based Information Visualization: Toward Semantic Web Applications 59
3.1 Introduction 59
3.2 Cluster Map Basics 60
3.3 Applications 62
3.3.1 The DOPE Browser 62
3.3.2 Xarop/SWAP: Peer-to-Peer Knowledge Management 64
3.3.3 Aduna AutoFocus 66
3.4 Uses of Ontology-Based Visualization 68
3.4.1 Data Analysis 68
3.4.2 Querying 68
3.4.3 Exploration 70
3.5 Future Work 71
3.6 Summary 71
3.7 References 72
Chapter 4 Topic Maps, RDF Graphs, and Ontologies Visualization 73
4.1 Introduction 73
4.2 Topic Maps, RDF, and Ontologies Basic Concepts 74
4.2.1 Topic Maps 74
4.2.2 RDF 75
4.2.3 Ontologies 77
4.3 Semantic Graphs Visualization 77
4.3.1 Visualization Goals 78
4.3.2 Visualization Techniques 79
4.4 Conclusion and Perspectives 91
4.5 References 92
Chapter 5 Web Services: Description, Interfaces, and Ontology 94
5.1 Introduction 94
5.2 Semantic Web and Web Services: A Comparison 94
5.3 Web Services Definition and Description Layers 95
5.3.1 Standardization Efforts 96
5.3.2 The Significance of SOAP 96
5.4 SOAP in Greater Detail 96
5.4.1 SOAP Message 96
5.4.2 The Structure of a SOAP Message 97
5.4.3 Examples of SOAP Messages 98
5.5 What Is It Like for a Programmer? 100
5.5.1 Axis SOAP Server and Tomcat Servlet Engine 100
5.5.2 From Java Class to SOAP Server 101
5.5.3 Constructing a SOAP Client 101
5.6 WSDL 102
5.6.1 WSDL Document Structure and Examples 103
5.6.2 Options and Alternatives 105
5.6.3 What Can One Do with WSDL? 105
5.7 UDDI 106
5.7.1 Components of a UDDI Entry 106
5.7.2 UDDI and WSDL 107
5.7.3 Semantical and Ontological Needs 108
5.8 References 114
Chapter 6 Recommender Systems for the Web 116
6.1 Introduction 116
6.2 The Beginning of Collaborative Filtering 117
6.3 Automated Collaborative Filtering 119
6.4 Enhancing Collaborative Filtering with Semantics 122
6.4.1 New Users and New Items 122
6.4.2 Integrated Content/Collaborative Filtering Solutions 123
6.4.3 Situational and Task-Focused Recommenders 125
6.5 Explanation and Inference 127
6.5.1 Explaining Recommendations 127
6.5.2 Focusing Implicit Ratings 128
6.6 Socially Aware Recommenders 130
6.6.1 Social Navigation 130
6.6.2 Recommending for Groups 131
6.7 Portable Recommenders 133
6.8 Cheating with Recommenders 134
6.9 Conclusion 134
6.10 Acknowledgments 135
6.11 References 135
Chapter 7 SVG and X3D: New XML Technologies for 2D and 3D Visualization 138
7.1 Introduction 138
7.2 SVG 138
7.3 X3D 141
7.4 The Use and Advantages of SVG and X3D 144
7.5 References 146
PART 2 Visual Techniques and Applications for the Semantic Web 149
Chapter 8 Using Graphically Represented Ontologies for Searching Content on the Semantic Web 151
8.1 Introduction 151
8.2 Visual Query Languages 151
8.3 The Graphical Ontology Designer Environment 152
8.3.1 Enabling Technologies 152
8.3.2 GODE GUI and Functionality 159
8.3.4 Advanced Search 163
8.3.5 Application Area of Advanced Graphical Ontologies 164
8.3.6 Intended Audience for Advanced Graphical Search 165
8.3.7 Possible Traps 165
8.4 Conclusion and Further Work 165
8.5 Acknowledgments 166
8.6 References 166
Chapter 9 Adapting Graph Visualization Techniques for the Visualization of RDF Data 168
9.1 Introduction 168
9.2 Background 169
9.3 GViz 171
9.3.1 Data Model 172
9.3.2 Operation Model 173
9.3.3 Visualization 174
9.4 Applications 175
9.4.1 Conceptual Model Visualization 177
9.4.2 Conceptual Model Instance Visualization 178
9.4.3 Application Model Visualization 179
9.4.4 Application Model Instance Visualization 182
9.5 Future Work 183
9.6 Summary 184
9.7 Acknowledgments 185
9.8 References 185
Chapter 10 Spring-Embedded Graphs for Semantic Visualization 186
10.1 Introduction 186
10.2 A Suitable Graph Drawing Algorithm 187
10.3 The Tool 189
10.4 Case Study 1: Visualizing Ontologies—Zoological Information Management System 190
10.5 Case Study 2: Visualizing Instance Data—Social Network Visualization 192
10.6 Future Work 195
10.7 Conclusions 195
10.8 References 195
Chapter 11 Semantic Association Networks: Using Semantic Web Technology to Improve Scholarly Knowledge and Expertise Management 197
11.1 Introduction 197
11.2 Scientific Trends and Current Means to Access Knowledge and Expertise 198
11.3 Semantic Association Networks 202
11.4 Implementing SANs: Opportunities and Challenges 208
11.5 Concluding Remarks 210
11.6 Acknowledgments 211
11.7 References 211
Chapter 12 Interactive Interfaces for Mapping E-Commerce Ontologies 213
12.1 XML-Based Communication between Companies: Visualizing a Mutual Understanding 213
12.2 The Process of Creating and Reading XML Documents and Its Native Visualizations 214
12.3 Technologies for Visualizing XML Documents 216
12.4 A Generalized Interface for Visualizing XML Metadata and Their Structural Relationships 219
12.5 A Web-Based Ontology Translator for E-Commerce Documents 220
12.6 Future Work 222
12.7 References 223
Chapter 13 Back Pain Data Collection Using Scalable Vector Graphics and Geographical Information Systems 224
13.1 Introduction 224
13.1.1 Back Pain Questionnaires 225
13.2 The Pain Drawing 225
13.2.1 Scoring Methods 227
13.2.2 Pain Drawings—Conclusions 228
13.3 Back Pain Data—Technological Solutions 228
13.3.1 SVG 229
13.3.2 ASP 229
13.3.3 GIS 229
13.3.4 Visualization for Mobile and Embedded Applications 230
13.4 System Requirements and Development 231
13.4.1 Regional Diagram for Visual Interaction 232
13.4.2 ASP with a GIF Image Map 233
13.4.3 ASP with SVG Image Map 233
13.4.4 GIS 235
13.5 Wireless-Enabled PDA Solution 235
13.6 Solutions Review and Comparison 237
13.6.1 Comparisons 239
13.7 Conclusions 240
13.8 References 241
Chapter 14 Social Network Analysis on the Semantic Web: Techniques and Challenges for Visualizing FOAF 243
14.1 Introduction 243
14.2 XML, the Semantic Web, and FOAF 244
14.3 Analyzing LiveJournal FOAF 246
14.4 Discussion and Conclusions 253
14.5 Acknowledgments 255
14.6 References 255
Chapter 15 Concluding Remarks: Today’s Vision of Envisioning the Semantic Future 257
Index 259

Chapter 8
Using Graphically Represented Ontologies for Searching Content on the Semantic Web (p. 137-138)

LeendertW.M.Wienhofen

8.1 Introduction

The SemanticWeb (Berners-Lee, 1998) is a revolution for machine-understandability of Web pages, yet for the typical kind of user, the nontechnical one, the bene.ts may not be as obvious as for researchers. In order to enable "naive" users to bene- .t from the Semantic Web, this chapter proposes a search paradigm using graphical ontologies to retrieve content. Retrieval problems started when the Internet became available for everyone.

The ease of publishing led to an abundance of mostly unstructured data, since HTML is meant to display content for humans and not machines. If we wish that all Web pages become Semantic Web enabled, publishing needs to be as easy as it currently is, and retrieval methods need to be as easy as they currently are, but of course the relevance of the retrieved content needs to be much better. The paradigm presented, called GODE (Graphical Ontology Designer Environment) (Wienhofen, 2003), gives users the possibility to search both the Web and the SemanticWeb.

This chapter describes how to prepare users for the new .ow of information, by introducing them to the concept of graphical search step by step. A bene.t of using graphical search is that it is query language independent.Users have a uniformmethod of accessing information; a conversion algorithm can be made and used as a plug-in for the search language for each query language available. A variety of dif.culty levels are identi.ed to make sure that everybody can bene.t from this approach in different situations. Application areas are discussed for both the simple and the advanced version of GODE.

8.2 Visual Query Languages

An experimental proof by Catarci and Santucci (1995) shows that QBD. (Query By Diagram.), a visual query language, is easier to use and gives better results than text based SQL queries. The experiment de.ned three groups of users: naive, medium, and expert. All three groups got better results faster by using this visual approach. Other visual query languages, such VISUAL (Balkir et al., 2002) and GLASS (Ni and Ling, 2003), are available.

Even though most are designed for use with databases or XML .les, the type of use presented in this chapter is not that much different, as most SemanticWeb languages areXMLbased and de.ne semantic relations. Database queries (SQL) are based on relations, andXML.le queries are done on structured data. The available visual query languages, however, are generally not focused toward the naive users, though they are no doubt the largest group of users. This chapter presents a search method that is aimed at the naive user, yet having enough possibilities for it to be useful to expert users as well. In fact, it is built up with the goal that naive users gradually can become medium-level users and eventually expert users (Wienhofen, 2004). 8.3 The Graphical Ontology Designer Environment Different building blocks and ideas are presented, which are used as a foundation for building the Graphical Ontology Designer Environment (GODE).