Geroimenko / Chen Visualizing the Semantic Web

XML-based Internet and Information Visualization
2. Auflage 2006
ISBN: 978-1-84628-290-4
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 257 Seiten

The Web is undergoing revolutionary changes - its second generation is emerging. The key player in the new generation is not HTML but XML (this is why it is also known as 'the XML-based Web'). If the appearance of web pages is a major concern in the first generation, then the meaning (or semantics) of information on the Web is the focus of the second generation, which is why it is also called 'the Semantic Web.' The new edition of the pioneering monograph on Visualising the Semantic Web has undergone a number of changes in order to reflect recent research results, web standards, developments and trends. In this new edition, 2 chapters have been removed, 4 new chapters have been added and the 10 remaining chapters have been completely revised and updated.

Autoren/Hrsg.

Geroimenko, Vladimir

Chen, Chaomei

Inhaltsverzeichnis

1;Preface;6
2;Contents;9
3;PART 1 Semantic, Visual, and Technological Facets of the Second-Generation Web;15
3.1;Chapter 1 The Concept and Architecture of the Semantic Web;17
3.1.1;1.1 From HTML to XML and the Semantic Web;17
3.1.2;1.2 The XML Family of Technologies;23
3.1.3;1.3 The Architecture of the Semantic Web;27
3.1.4;1.4 References;31
3.2;Chapter 2 Information Visualization and the Semantic Web;33
3.2.1;2.1 Introduction;33
3.2.2;2.2 The Semantic Web;33
3.2.2.1;2.2.1 Visualization Issues;34
3.2.2.2;2.2.2 Semantic Annotation;35
3.2.3;2.3 Information Visualization;41
3.2.3.1;2.3.1 Tracking Knowledge and Technology Trends;42
3.2.3.2;2.3.2 Citation Analysis;42
3.2.3.3;2.3.3 Patent Citation Analysis;43
3.2.4;2.4 A Harmonious Relationship?;44
3.2.4.1;2.4.1 Beyond Information Retrieval;44
3.2.4.2;2.4.2 Yin and Yang;47
3.2.4.3;2.4.3 An Illustrative Example;48
3.2.5;2.5 Conclusion;56
3.2.6;2.6 References;56
3.3;Chapter 3 Ontology-Based Information Visualization: Toward Semantic Web Applications;59
3.3.1;3.1 Introduction;59
3.3.2;3.2 Cluster Map Basics;60
3.3.3;3.3 Applications;62
3.3.3.1;3.3.1 The DOPE Browser;62
3.3.3.2;3.3.2 Xarop/SWAP: Peer-to-Peer Knowledge Management;64
3.3.3.3;3.3.3 Aduna AutoFocus;66
3.3.4;3.4 Uses of Ontology-Based Visualization;68
3.3.4.1;3.4.1 Data Analysis;68
3.3.4.2;3.4.2 Querying;68
3.3.4.3;3.4.3 Exploration;70
3.3.5;3.5 Future Work;71
3.3.6;3.6 Summary;71
3.3.7;3.7 References;72
3.4;Chapter 4 Topic Maps, RDF Graphs, and Ontologies Visualization;73
3.4.1;4.1 Introduction;73
3.4.2;4.2 Topic Maps, RDF, and Ontologies Basic Concepts;74
3.4.2.1;4.2.1 Topic Maps;74
3.4.2.2;4.2.2 RDF;75
3.4.2.3;4.2.3 Ontologies;77
3.4.3;4.3 Semantic Graphs Visualization;77
3.4.3.1;4.3.1 Visualization Goals;78
3.4.3.2;4.3.2 Visualization Techniques;79
3.4.4;4.4 Conclusion and Perspectives;91
3.4.5;4.5 References;92
3.5;Chapter 5 Web Services: Description, Interfaces, and Ontology;94
3.5.1;5.1 Introduction;94
3.5.2;5.2 Semantic Web and Web Services: A Comparison;94
3.5.3;5.3 Web Services Definition and Description Layers;95
3.5.3.1;5.3.1 Standardization Efforts;96
3.5.3.2;5.3.2 The Significance of SOAP;96
3.5.4;5.4 SOAP in Greater Detail;96
3.5.4.1;5.4.1 SOAP Message;96
3.5.4.2;5.4.2 The Structure of a SOAP Message;97
3.5.4.3;5.4.3 Examples of SOAP Messages;98
3.5.5;5.5 What Is It Like for a Programmer?;100
3.5.5.1;5.5.1 Axis SOAP Server and Tomcat Servlet Engine;100
3.5.5.2;5.5.2 From Java Class to SOAP Server;101
3.5.5.3;5.5.3 Constructing a SOAP Client;101
3.5.6;5.6 WSDL;102
3.5.6.1;5.6.1 WSDL Document Structure and Examples;103
3.5.6.2;5.6.2 Options and Alternatives;105
3.5.6.3;5.6.3 What Can One Do with WSDL?;105
3.5.7;5.7 UDDI;106
3.5.7.1;5.7.1 Components of a UDDI Entry;106
3.5.7.2;5.7.2 UDDI and WSDL;107
3.5.7.3;5.7.3 Semantical and Ontological Needs;108
3.5.8;5.8 References;114
3.6;Chapter 6 Recommender Systems for the Web;116
3.6.1;6.1 Introduction;116
3.6.2;6.2 The Beginning of Collaborative Filtering;117
3.6.3;6.3 Automated Collaborative Filtering;119
3.6.4;6.4 Enhancing Collaborative Filtering with Semantics;122
3.6.4.1;6.4.1 New Users and New Items;122
3.6.4.2;6.4.2 Integrated Content/Collaborative Filtering Solutions;123
3.6.4.3;6.4.3 Situational and Task-Focused Recommenders;125
3.6.5;6.5 Explanation and Inference;127
3.6.5.1;6.5.1 Explaining Recommendations;127
3.6.5.2;6.5.2 Focusing Implicit Ratings;128
3.6.6;6.6 Socially Aware Recommenders;130
3.6.6.1;6.6.1 Social Navigation;130
3.6.6.2;6.6.2 Recommending for Groups;131
3.6.7;6.7 Portable Recommenders;133
3.6.8;6.8 Cheating with Recommenders;134
3.6.9;6.9 Conclusion;134
3.6.10;6.10 Acknowledgments;135
3.6.11;6.11 References;135
3.7;Chapter 7 SVG and X3D: New XML Technologies for 2D and 3D Visualization;138
3.7.1;7.1 Introduction;138
3.7.2;7.2 SVG;138
3.7.3;7.3 X3D;141
3.7.4;7.4 The Use and Advantages of SVG and X3D;144
3.7.5;7.5 References;146
4;PART 2 Visual Techniques and Applications for the Semantic Web;149
4.1;Chapter 8 Using Graphically Represented Ontologies for Searching Content on the Semantic Web;151
4.1.1;8.1 Introduction;151
4.1.2;8.2 Visual Query Languages;151
4.1.3;8.3 The Graphical Ontology Designer Environment;152
4.1.3.1;8.3.1 Enabling Technologies;152
4.1.3.2;8.3.2 GODE GUI and Functionality;159
4.1.3.3;8.3.4 Advanced Search;163
4.1.3.4;8.3.5 Application Area of Advanced Graphical Ontologies;164
4.1.3.5;8.3.6 Intended Audience for Advanced Graphical Search;165
4.1.3.6;8.3.7 Possible Traps;165
4.1.4;8.4 Conclusion and Further Work;165
4.1.5;8.5 Acknowledgments;166
4.1.6;8.6 References;166
4.2;Chapter 9 Adapting Graph Visualization Techniques for the Visualization of RDF Data;168
4.2.1;9.1 Introduction;168
4.2.2;9.2 Background;169
4.2.3;9.3 GViz;171
4.2.3.1;9.3.1 Data Model;172
4.2.3.2;9.3.2 Operation Model;173
4.2.3.3;9.3.3 Visualization;174
4.2.4;9.4 Applications;175
4.2.4.1;9.4.1 Conceptual Model Visualization;177
4.2.4.2;9.4.2 Conceptual Model Instance Visualization;178
4.2.4.3;9.4.3 Application Model Visualization;179
4.2.4.4;9.4.4 Application Model Instance Visualization;182
4.2.5;9.5 Future Work;183
4.2.6;9.6 Summary;184
4.2.7;9.7 Acknowledgments;185
4.2.8;9.8 References;185
4.3;Chapter 10 Spring-Embedded Graphs for Semantic Visualization;186
4.3.1;10.1 Introduction;186
4.3.2;10.2 A Suitable Graph Drawing Algorithm;187
4.3.3;10.3 The Tool;189
4.3.4;10.4 Case Study 1: Visualizing Ontologies—Zoological Information Management System;190
4.3.5;10.5 Case Study 2: Visualizing Instance Data—Social Network Visualization;192
4.3.6;10.6 Future Work;195
4.3.7;10.7 Conclusions;195
4.3.8;10.8 References;195
4.4;Chapter 11 Semantic Association Networks: Using Semantic Web Technology to Improve Scholarly Knowledge and Expertise Management;197
4.4.1;11.1 Introduction;197
4.4.2;11.2 Scientific Trends and Current Means to Access Knowledge and Expertise;198
4.4.3;11.3 Semantic Association Networks;202
4.4.4;11.4 Implementing SANs: Opportunities and Challenges;208
4.4.5;11.5 Concluding Remarks;210
4.4.6;11.6 Acknowledgments;211
4.4.7;11.7 References;211
4.5;Chapter 12 Interactive Interfaces for Mapping E-Commerce Ontologies;213
4.5.1;12.1 XML-Based Communication between Companies: Visualizing a Mutual Understanding;213
4.5.2;12.2 The Process of Creating and Reading XML Documents and Its Native Visualizations;214
4.5.3;12.3 Technologies for Visualizing XML Documents;216
4.5.4;12.4 A Generalized Interface for Visualizing XML Metadata and Their Structural Relationships;219
4.5.5;12.5 A Web-Based Ontology Translator for E-Commerce Documents;220
4.5.6;12.6 Future Work;222
4.5.7;12.7 References;223
4.6;Chapter 13 Back Pain Data Collection Using Scalable Vector Graphics and Geographical Information Systems;224
4.6.1;13.1 Introduction;224
4.6.1.1;13.1.1 Back Pain Questionnaires;225
4.6.2;13.2 The Pain Drawing;225
4.6.2.1;13.2.1 Scoring Methods;227
4.6.2.2;13.2.2 Pain Drawings—Conclusions;228
4.6.3;13.3 Back Pain Data—Technological Solutions;228
4.6.3.1;13.3.1 SVG;229
4.6.3.2;13.3.2 ASP;229
4.6.3.3;13.3.3 GIS;229
4.6.3.4;13.3.4 Visualization for Mobile and Embedded Applications;230
4.6.4;13.4 System Requirements and Development;231
4.6.4.1;13.4.1 Regional Diagram for Visual Interaction;232
4.6.4.2;13.4.2 ASP with a GIF Image Map;233
4.6.4.3;13.4.3 ASP with SVG Image Map;233
4.6.4.4;13.4.4 GIS;235
4.6.5;13.5 Wireless-Enabled PDA Solution;235
4.6.6;13.6 Solutions Review and Comparison;237
4.6.6.1;13.6.1 Comparisons;239
4.6.7;13.7 Conclusions;240
4.6.8;13.8 References;241
4.7;Chapter 14 Social Network Analysis on the Semantic Web: Techniques and Challenges for Visualizing FOAF;243
4.7.1;14.1 Introduction;243
4.7.2;14.2 XML, the Semantic Web, and FOAF;244
4.7.3;14.3 Analyzing LiveJournal FOAF;246
4.7.4;14.4 Discussion and Conclusions;253
4.7.5;14.5 Acknowledgments;255
4.7.6;14.6 References;255
4.8;Chapter 15 Concluding Remarks: Today’s Vision of Envisioning the Semantic Future;257
5;Index;259

Leseproben

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).