Devedzic / Devedic / Gasevic Web 2.0 & Semantic Web

1. Auflage 2010
ISBN: 978-1-4419-1219-0
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 6, 206 Seiten

Reihe: Annals of Information Systems

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

Autoren/Hrsg.

Devedic, Vladan

Gaevic, Dragan

Devedzic, Vladan

Gasevic, Dragan

Deved#ic, Vladan

Ga#evic, Dragan

Inhaltsverzeichnis

1;Web 2.0 & Semantic Web
;1
1.1;Preface
;4
1.1.1;Special Issue Theme;5
1.1.2;Selected Papers;5
1.1.2.1;Tagging and Semantics;6
1.1.2.2;Adaptability and User Interfaces;6
1.1.2.3;Knowledge Representation and User Interfaces;7
1.1.2.4;Data Mining, Software Engineering, and Semantic Web;8
1.1.3;Summary;9
1.1.4;Acknowledgments;10
1.1.5;References;10
1.2;Contents
;12
1.3;Contributors
;14
1.4;Section 1: Tagging and Semantics;16
1.4.1;1 TagFusion: A System for Integration and Leveraging of Collaborative Tags;17
1.4.1.1;1.1 Introduction;17
1.4.1.2;1.2 Background;19
1.4.1.2.1;1.2.1 Tagging;19
1.4.1.2.2;1.2.2 Folksonomies;21
1.4.1.2.3;1.2.3 Problem Description;21
1.4.1.3;1.3 Integrating Metadata from Collaborative Tagging Systems;23
1.4.1.3.1;1.3.1 The Architecture of the TagFusion System;23
1.4.1.3.1.1;1.3.1.1 TagFusion Core Implementation Details;25
1.4.1.3.2;1.3.2 Modalities of Use and Attracting Users;26
1.4.1.3.2.1;1.3.2.1 Web Sites Send Metadata to the TagFusion System;26
1.4.1.3.2.2;1.3.2.2 Harvesting Data from Social Web Sites;27
1.4.1.3.3;1.3.3 Leveraging Automatic Annotators;29
1.4.1.3.4;1.3.4 Example of Use;29
1.4.1.3.5;1.3.5 Advanced Scenario of Use;31
1.4.1.4;1.4 Related Work;33
1.4.1.4.1;1.4.1 RSS;33
1.4.1.4.2;1.4.2 SIOC;33
1.4.1.4.3;1.4.3 Twine;34
1.4.1.4.4;1.4.4 OpenTagging Platform;34
1.4.1.5;1.5 Conclusions and Future Work;35
1.4.1.6;References;36
1.4.2;2 Semantic Enhancement of Social Tagging Systems;38
1.4.2.1;2.1 Introduction;38
1.4.2.2;2.2 GroupMe! System;40
1.4.2.2.1;2.2.1 GroupMe! Architecture;43
1.4.2.2.2;2.2.2 Evaluation of the GroupMe! System;48
1.4.2.2.2.1;2.2.2.1 Results;50
1.4.2.3;2.3 GroupMe! Folksonomy;50
1.4.2.4;2.4 Ranking Strategies;52
1.4.2.4.1;2.4.1 FolkRank Algorithm;52
1.4.2.4.2;2.4.2 Group-Aware Ranking Strategies;53
1.4.2.4.3;2.4.3 Evaluation;56
1.4.2.4.3.1;2.4.3.1 Metrics;56
1.4.2.4.3.2;2.4.3.2 Measurements and Discussion;57
1.4.2.4.3.3;2.4.3.3 Results;59
1.4.2.5;2.5 Related Work;61
1.4.2.6;2.6 Conclusions and Future Work;64
1.4.2.7;References;66
1.5;Section 2: Adaptability and User Interfaces;68
1.5.1;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
1.5.1.1;3.1 Introduction;69
1.5.1.2;3.2 Adaptation and Recommendation Techniques;71
1.5.1.3;3.3 Annotations' Quality;72
1.5.1.3.1;3.3.1 Contribution of Recommendations to Web 2.0-Based Applications;75
1.5.1.3.2;3.3.2 Contribution of Recommendations to SW-Based Applications;77
1.5.1.3.3;3.3.3 Contribution of Recommendations to Combine Web 2.0 and SW Approaches;78
1.5.1.4;3.4 Relevance of Annotation-Enriched Retrieved Resources;81
1.5.1.4.1;3.4.1 Contribution of Adaptation and Recommendation Techniques to Web 2.0-Based Applications;81
1.5.1.4.2;3.4.2 Contribution of Adaptation and Recommendation Techniques to SW-Based Applications;82
1.5.1.4.3;3.4.3 Contribution of Adaptation and Recommendation Techniques to Web 2.0 and SW;84
1.5.1.5;3.5 Conclusions;85
1.5.1.6;References;86
1.5.2;4 Adaptive Reactive Rich Internet Applications;90
1.5.2.1;4.1 Introduction;90
1.5.2.2;4.2 Motivating Example;92
1.5.2.3;4.3 Logical System Architecture: The Adaptation Loop;93
1.5.2.4;4.4 The Adaptation Ontologies: The Paving Stones of the Personalization Highway;94
1.5.2.4.1;4.4.1 The RIA Design Patterns Ontology;95
1.5.2.4.2;4.4.2 The User Model Ontology;95
1.5.2.4.3;4.4.3 The Event Ontology;95
1.5.2.4.4;4.4.4 The Domain Ontologies;96
1.5.2.5;4.5 JSON-Rules: A Client-Side Rule Language;97
1.5.2.6;4.6 Design-Time Architecture;99
1.5.2.6.1;4.6.1 Ontology Creation and Annotation of RIAs;99
1.5.2.6.2;4.6.2 Semantic Web Usage Mining;99
1.5.2.6.3;4.6.3 Design of Adaptation Rules;100
1.5.2.6.4;4.6.4 Ontology and Rules Transformer;101
1.5.2.7;4.7 Run-Time Architecture;103
1.5.2.8;4.8 Evaluation;106
1.5.2.9;4.9 Related Work;108
1.5.2.10;4.10 Conclusions and Outlook;110
1.5.2.11;References;111
1.6;Section 3: Knowledge Representation and User Interfaces;114
1.6.1;5 Towards Enhanced Usability of Natural Language Interfaces to Knowledge Bases;115
1.6.1.1;5.1 Introduction;115
1.6.1.2;5.2 Natural Language Interfaces to Knowledge Bases;117
1.6.1.2.1;5.2.1 Habitability;117
1.6.1.2.2;5.2.2 Usability;118
1.6.1.2.3;5.2.3 The Aim and the Scope of the Survey;119
1.6.1.3;5.3 Customisation and Retrieval Performance;120
1.6.1.3.1;5.3.1 ORAKEL;120
1.6.1.3.2;5.3.2 AquaLog;122
1.6.1.3.3;5.3.3 E-Librarian;123
1.6.1.3.4;5.3.4 CPL;124
1.6.1.3.5;5.3.5 PANTO;124
1.6.1.3.6;5.3.6 Querix;125
1.6.1.3.7;5.3.7 NLP-Reduce;125
1.6.1.3.8;5.3.8 QuestIO;126
1.6.1.3.9;5.3.9 Summary and Discussion;126
1.6.1.4;5.4 Enhanced usability of Natural Language Interfaces: end-users' point of view;128
1.6.1.4.1;5.4.1 Vocabulary Restriction;128
1.6.1.4.2;5.4.2 Feedback;130
1.6.1.4.3;5.4.3 Guided Interfaces;132
1.6.1.4.4;5.4.4 Personalised Vocabulary;133
1.6.1.4.5;5.4.5 How to Deal with Ambiguities?;135
1.6.1.4.6;5.4.6 Summary and Discussion;138
1.6.1.5;5.5 Conclusion;139
1.6.1.6;References;140
1.6.2;6 Semantic Document Model to Enhance Data and Knowledge Interoperability;144
1.6.2.1;6.1 Introduction;144
1.6.2.2;6.2 From Paper-Based and Digital to Semantic Documents;146
1.6.2.3;6.3 Semantic Documents;148
1.6.2.3.1;6.3.1 Semantic Document Model (SDM);149
1.6.2.3.1.1;6.3.1.1 Document Ontology;149
1.6.2.3.1.2;6.3.1.2 Annotation Ontology;150
1.6.2.3.1.3;6.3.1.3 Change Ontology;153
1.6.2.3.2;6.3.2 The MP and HR instances of semantic documents;154
1.6.2.3.3;6.3.3 Storage and Organization of Semantic Documents;156
1.6.2.4;6.4 Social Semantic Desktop (SSD);158
1.6.2.4.1;6.4.1 Architecture of the NEPOMUK SSD ;158
1.6.2.4.2;6.4.2 Semantic Document Management System (SDMS);159
1.6.2.5;6.5 Application Examples;161
1.6.2.6;6.6 Discussion;164
1.6.2.7;6.7 Related Work;165
1.6.2.8;6.8 Conclusions;167
1.6.2.9;References;168
1.7;Section 4: Data Mining, Software Engineering, and Semantic Web;170
1.7.1;7 Ontology-Based Data Mining in Digital Libraries;171
1.7.1.1;7.1 Introduction;171
1.7.1.2;7.2 Related Work;172
1.7.1.3;7.3 Duplicate Record Detection;173
1.7.1.3.1;7.3.1 Data Collection and Cleaning;174
1.7.1.3.2;7.3.2 Matching Titles;174
1.7.1.3.2.1;7.3.2.1 Character-Based Similarity Metrics;175
1.7.1.3.2.2;7.3.2.2 Thesaurus;175
1.7.1.3.2.3;7.3.2.3 Clustering;176
1.7.1.3.2.4;7.3.2.4 Token-Based Similarity Metrics;177
1.7.1.3.3;7.3.3 Using External Sources;178
1.7.1.3.4;7.3.4 Incremental Matching;178
1.7.1.4;7.4 Experiment;178
1.7.1.5;7.5 Conclusion;182
1.7.1.6;References;182
1.7.2;8 An Assessment System on the Semantic Web;184
1.7.2.1;8.1 Introduction;184
1.7.2.2;8.2 Problem Statement;185
1.7.2.3;8.3 IMS QTI Standard: A Short Overview;186
1.7.2.4;8.4 Model Driven Architecture;187
1.7.2.5;8.5 Modeling the QTI-Based Assessment System Using MDA Standards;188
1.7.2.5.1;8.5.1 A QTI Metamodel;189
1.7.2.5.2;8.5.2 Creating the QTI Models Based on the QTI Metamodel;191
1.7.2.5.3;8.5.3 Model Transformation in QTI System;194
1.7.2.6;8.6 Reasoning with QTI Models;195
1.7.2.6.1;8.6.1 DL Reasoning in Intelligent Analysis of Student's Solutions;197
1.7.2.6.2;8.6.2 Examples of Applying DLs Reasoning in Intelligent Analysis of Student's Solutions;198
1.7.2.6.2.1;8.6.2.1 Example of an Unsatisfiable Student's Answer;202
1.7.2.6.2.2;8.6.2.2 Example of a Satisfiable Student's Answer;203
1.7.2.7;8.7 Related Work;204
1.7.2.8;8.8 Conclusions and Future Work;205
1.7.2.9;References;205
1.8;Author Index;208