Web 2.0 & Semantic Web (eBook)

Vladan Devedžic, Dragan Gaševic (Herausgeber)

eBook Download: PDF

2010 | 2009
XX, 206 Seiten
Springer US (Verlag)
978-1-4419-1219-0 (ISBN)

Infos

According to the W3C Semantic Web Activity [1]: The Semantic Web provides a common framework that allows data to be shared and reused across appli- tion, enterprise, and community boundaries. This statement clearly explains that the Semantic Web is about data sharing. Currently, the Web uses hyperlinks to connect Web pages. The Semantic Web goes beyond that and focuses on data and envisions the creation of the web of data. On the Semantic Web, anyone can say anything about any resource on the Web. This is fully based on the concept of semantic - notations, where each resource on the Web can have an assigned meaning. This is done through the use of ontologies as a formal and explicit representation of domain concepts and their relationships [2]. Ontologies are formally based on description logics. This enables agents and applications to reason over the data when searching the Web, which has not previously been possible. Web 2. 0 has gradually evolved from letting the Web users play a more active role. Unlike the initial version of the Web, where the users mainly 'consumed' content, users are now offered easy-to-use services for content production and publication. Mashups, blogs, wikis, feeds, interface remixes, and social networking/tagging s- tems are examples of these well-known services. The success and wide adoption of Web 2. 0 was in its reliance on social interactions as an inevitable characteristic of the use and life of the Web. In particular, Web 2.

Vladan Devedzic is a Professor of computer science at the University of Belgrade, FON - School of Business Administration, Department of Information Systems and Technologies. He has also taught at the University of Belgrade School of Electrical Engineering, Department of Computer Engineering, as well as at the Military Academy of Serbia (the former Yugoslav Military Academy).

His professional goal is to bring together the ideas from the broad fields of intelligent systems and software engineering. His current professional and scientific interests include knowledge modeling, ontologies, intelligent reasoning techniques, Semantic Web, software engineering, and the application of artificial intelligence to education and medicine. More information on Prof. Devedzic is available at the website: http://fon.fon.bg.ac.yu/-devedzic/

Dragan Gaševic taught at the University of Belgrade (2000-2005) before transferring to Simon Fraser University in Canada, where he was a Postdoctoral Fellow at the School of Interactive Arts and Technology involved in the LORNET project funded by the Natural Science and Engineering Research Council of Canada (NSERC). He is currently an Adjunct Professor at the Interactive School of Arts & Technology at Simon Fraser University as well as an Assistant Professor in the School of Computing and Information Systems at Athabasca University.

His research interests include model driven software engineering; knowledge management and Semantic Web; interoperability and integration of systems, data, and modeling technologies; technology-enhanced learning (e-learning); and Petri nets. For more information about Prof. Gaševic, please go the following website: http://scis.athabascau.ca/scis/staff/index.jsp?ct=dragang&sn=faculty

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According to the W3C Semantic Web Activity [1]: The Semantic Web provides a common framework that allows data to be shared and reused across appli- tion, enterprise, and community boundaries. This statement clearly explains that the Semantic Web is about data sharing. Currently, the Web uses hyperlinks to connect Web pages. The Semantic Web goes beyond that and focuses on data and envisions the creation of the web of data. On the Semantic Web, anyone can say anything about any resource on the Web. This is fully based on the concept of semantic - notations, where each resource on the Web can have an assigned meaning. This is done through the use of ontologies as a formal and explicit representation of domain concepts and their relationships [2]. Ontologies are formally based on description logics. This enables agents and applications to reason over the data when searching the Web, which has not previously been possible. Web 2. 0 has gradually evolved from letting the Web users play a more active role. Unlike the initial version of the Web, where the users mainly "e;consumed"e; content, users are now offered easy-to-use services for content production and publication. Mashups, blogs, wikis, feeds, interface remixes, and social networking/tagging s- tems are examples of these well-known services. The success and wide adoption of Web 2. 0 was in its reliance on social interactions as an inevitable characteristic of the use and life of the Web. In particular, Web 2.

Inhalt

Web 2.0 & Semantic Web
1 
Preface 
4 
Special Issue Theme 5
Selected Papers 5
Tagging and Semantics 6
Adaptability and User Interfaces 6
Knowledge Representation and User Interfaces 7
Data Mining, Software Engineering, and Semantic Web 8
Summary 9
Acknowledgments 10
References 10
Contents 
12 
Contributors 
14 
Section 1: Tagging and Semantics 16
1 TagFusion: A System for Integration and Leveraging of Collaborative Tags 17
1.1 Introduction 17
1.2 Background 19
1.2.1 Tagging 19
1.2.2 Folksonomies 21
1.2.3 Problem Description 21
1.3 Integrating Metadata from Collaborative Tagging Systems 23
1.3.1 The Architecture of the TagFusion System 23
1.3.1.1 TagFusion Core Implementation Details 25
1.3.2 Modalities of Use and Attracting Users 26
1.3.2.1 Web Sites Send Metadata to the TagFusion System 26
1.3.2.2 Harvesting Data from Social Web Sites 27
1.3.3 Leveraging Automatic Annotators 29
1.3.4 Example of Use 29
1.3.5 Advanced Scenario of Use 31
1.4 Related Work 33
1.4.1 RSS 33
1.4.2 SIOC 33
1.4.3 Twine 34
1.4.4 OpenTagging Platform 34
1.5 Conclusions and Future Work 35
References 36
2 Semantic Enhancement of Social Tagging Systems 38
2.1 Introduction 38
2.2 GroupMe! System 40
2.2.1 GroupMe! Architecture 43
2.2.2 Evaluation of the GroupMe! System 48
2.2.2.1 Results 50
2.3 GroupMe! Folksonomy 50
2.4 Ranking Strategies 52
2.4.1 FolkRank Algorithm 52
2.4.2 Group-Aware Ranking Strategies 53
2.4.3 Evaluation 56
2.4.3.1 Metrics 56
2.4.3.2 Measurements and Discussion 57
2.4.3.3 Results 59
2.5 Related Work 61
2.6 Conclusions and Future Work 64
References 66
Section 2: Adaptability and User Interfaces 68
3 Adaptation and Recommendation Techniques to Improve the Quality of Annotations and the Relevance of Resources in Web 2.0 and Semantic Web-Based Applications 69
3.1 Introduction 69
3.2 Adaptation and Recommendation Techniques 71
3.3 Annotations' Quality 72
3.3.1 Contribution of Recommendations to Web 2.0-Based Applications 75
3.3.2 Contribution of Recommendations to SW-Based Applications 77
3.3.3 Contribution of Recommendations to Combine Web 2.0 and SW Approaches 78
3.4 Relevance of Annotation-Enriched Retrieved Resources 81
3.4.1 Contribution of Adaptation and Recommendation Techniques to Web 2.0-Based Applications 81
3.4.2 Contribution of Adaptation and Recommendation Techniques to SW-Based Applications 82
3.4.3 Contribution of Adaptation and Recommendation Techniques to Web 2.0 and SW 84
3.5 Conclusions 85
References 86
4 Adaptive Reactive Rich Internet Applications 90
4.1 Introduction 90
4.2 Motivating Example 92
4.3 Logical System Architecture: The Adaptation Loop 93
4.4 The Adaptation Ontologies: The Paving Stones of the Personalization Highway 94
4.4.1 The RIA Design Patterns Ontology 95
4.4.2 The User Model Ontology 95
4.4.3 The Event Ontology 95
4.4.4 The Domain Ontologies 96
4.5 JSON-Rules: A Client-Side Rule Language 97
4.6 Design-Time Architecture 99
4.6.1 Ontology Creation and Annotation of RIAs 99
4.6.2 Semantic Web Usage Mining 99
4.6.3 Design of Adaptation Rules 100
4.6.4 Ontology and Rules Transformer 101
4.7 Run-Time Architecture 103
4.8 Evaluation 106
4.9 Related Work 108
4.10 Conclusions and Outlook 110
References 111
Section 3: Knowledge Representation and User Interfaces 114
5 Towards Enhanced Usability of Natural Language Interfaces to Knowledge Bases 115
5.1 Introduction 115
5.2 Natural Language Interfaces to Knowledge Bases 117
5.2.1 Habitability 117
5.2.2 Usability 118
5.2.3 The Aim and the Scope of the Survey 119
5.3 Customisation and Retrieval Performance 120
5.3.1 ORAKEL 120
5.3.2 AquaLog 122
5.3.3 E-Librarian 123
5.3.4 CPL 124
5.3.5 PANTO 124
5.3.6 Querix 125
5.3.7 NLP-Reduce 125
5.3.8 QuestIO 126
5.3.9 Summary and Discussion 126
5.4 Enhanced usability of Natural Language Interfaces: end-users' point of view 128
5.4.1 Vocabulary Restriction 128
5.4.2 Feedback 130
5.4.3 Guided Interfaces 132
5.4.4 Personalised Vocabulary 133
5.4.5 How to Deal with Ambiguities? 135
5.4.6 Summary and Discussion 138
5.5 Conclusion 139
References 140
6 Semantic Document Model to Enhance Data and Knowledge Interoperability 144
6.1 Introduction 144
6.2 From Paper-Based and Digital to Semantic Documents 146
6.3 Semantic Documents 148
6.3.1 Semantic Document Model (SDM) 149
6.3.1.1 Document Ontology 149
6.3.1.2 Annotation Ontology 150
6.3.1.3 Change Ontology 153
6.3.2 The MP and HR instances of semantic documents 154
6.3.3 Storage and Organization of Semantic Documents 156
6.4 Social Semantic Desktop (SSD) 158
6.4.1 Architecture of the NEPOMUK SSD 158
6.4.2 Semantic Document Management System (SDMS) 159
6.5 Application Examples 161
6.6 Discussion 164
6.7 Related Work 165
6.8 Conclusions 167
References 168
Section 4: Data Mining, Software Engineering, and Semantic Web 170
7 Ontology-Based Data Mining in Digital Libraries 171
7.1 Introduction 171
7.2 Related Work 172
7.3 Duplicate Record Detection 173
7.3.1 Data Collection and Cleaning 174
7.3.2 Matching Titles 174
7.3.2.1 Character-Based Similarity Metrics 175
7.3.2.2 Thesaurus 175
7.3.2.3 Clustering 176
7.3.2.4 Token-Based Similarity Metrics 177
7.3.3 Using External Sources 178
7.3.4 Incremental Matching 178
7.4 Experiment 178
7.5 Conclusion 182
References 182
8 An Assessment System on the Semantic Web 184
8.1 Introduction 184
8.2 Problem Statement 185
8.3 IMS QTI Standard: A Short Overview 186
8.4 Model Driven Architecture 187
8.5 Modeling the QTI-Based Assessment System Using MDA Standards 188
8.5.1 A QTI Metamodel 189
8.5.2 Creating the QTI Models Based on the QTI Metamodel 191
8.5.3 Model Transformation in QTI System 194
8.6 Reasoning with QTI Models 195
8.6.1 DL Reasoning in Intelligent Analysis of Student's Solutions 197
8.6.2 Examples of Applying DLs Reasoning in Intelligent Analysis of Student's Solutions 198
8.6.2.1 Example of an Unsatisfiable Student's Answer 202
8.6.2.2 Example of a Satisfiable Student's Answer 203
8.7 Related Work 204
8.8 Conclusions and Future Work 205
References 205
Author Index 208

Produkt-Details

• Erscheint lt. Verlag: 8.1.2010
• Reihe/Serie: Annals of Information Systems
• Reihe/Serie: Annals of Information Systems
• Zusatzinfo: XX, 206 p. 52 illus.
• Verlagsort: New York
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Netzwerke
• Themenwelt: Mathematik / Informatik ► Informatik ► Web / Internet
• Themenwelt: Informatik ► Weitere Themen ► Hardware
• Themenwelt: Mathematik / Informatik ► Mathematik ► Finanz- / Wirtschaftsmathematik
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Wirtschaftsinformatik
• Schlagworte: Data Mining • Internet • Model-Driven Engineering • Modeling • Ontology • semantic web • Service-Oriented Architecture • Web 2.0 • World Wide Web
• ISBN-10: 1-4419-1219-3 / 1441912193
• ISBN-13: 978-1-4419-1219-0 / 9781441912190