Mika Social Networks and the Semantic Web

1. Auflage 2007
ISBN: 978-0-387-71001-3
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 5, 234 Seiten

Reihe: Semantic Web and Beyond

Social Networks and the Semantic Web offers valuable information to practitioners developing social-semantic software for the Web. It provides two major case studies. The first case study shows the possibilities of tracking a research community over the Web. It reveals how social network mining from the web plays an important role for obtaining large scale, dynamic network data beyond the possibilities of survey methods. The second case study highlights the role of the social context in user-generated classifications in content, such as the tagging systems known as folksonomies.

Autoren/Hrsg.

Mika, Peter

Inhaltsverzeichnis

1;Foreword;6
2;Preface;8
3;Contents;11
4;Part I Introduction to the Semantic Web and Social Networks;14
4.1;The Semantic Web;15
4.1.1;Limitations of the current Web;16
4.1.1.1;What's wrong with the Web?;16
4.1.1.2;Diagnosis: A lack of knowledge;20
4.1.2;The semantic solution;21
4.1.3;Development of the Semantic Web;25
4.1.3.1;Research, development and standardization;25
4.1.3.2;Technology adoption;28
4.1.4;The emergence of the social web;33
4.1.4.1;Web 2.0 + Semantic Web = Web 3.0?;35
4.1.5;Discussion;37
4.2;Social Network Analysis;39
4.2.1;What is network analysis?;39
4.2.2;Development of Social Network Analysis;41
4.2.3;Key concepts and measures in network analysis;43
4.2.3.1;The global structure of networks;44
4.2.3.2;The macro-structure of social networks;49
4.2.3.3;Personal networks;53
4.2.4;Discussion;58
5;Part II Web data and semantics in social network applications;60
5.1;Electronic sources for network analysis;61
5.1.1;Electronic discussion networks;62
5.1.2;Blogs and online communities;63
5.1.3;Web-based networks;65
5.1.4;Discussion;72
5.2;Knowledge Representation on the Semantic Web;74
5.2.1;Ontologies and their role in the Semantic Web;76
5.2.1.1;Ontology-based Knowledge Representation;76
5.2.1.2;Ontologies and ontology languages for the Semantic Web;79
5.2.2;Ontology languages for the Semantic Web;80
5.2.2.1;The Resource Description Framework (RDF) and RDF Schema;81
5.2.2.2;The Web Ontology Language (OWL);88
5.2.2.3;Comparison to the Unified Modelling Language (UML);90
5.2.2.4;Comparison to the Entity/Relationship (E/R) model and the relational model;93
5.2.2.5;Comparison to the Extensible Markup Language (XML) and XML Schema;95
5.2.3;Discussion: Web-based knowledge representation;99
5.3;Modelling and aggregating social network data;102
5.3.1;State-of-the-art in network data representation;103
5.3.2;Ontological representation of social individuals;105
5.3.3;Ontological representation of social relationships;110
5.3.3.1;Conceptual model;112
5.3.4;Aggregating and reasoning with social network data;118
5.3.4.1;Representing identity;119
5.3.4.2;On the notion of equality;120
5.3.4.3;Determining equality;122
5.3.4.4;Reasoning with instance equality;123
5.3.4.5;Evaluating smushing;127
5.3.5;Discussion;128
5.3.5.1;Advanced representations;128
5.4;Developing social-semantic applications;130
5.4.1;Building Semantic Web applications with social network features;132
5.4.1.1;The generic architecture of Semantic Web applications;133
5.4.1.2;Sesame;135
5.4.1.3;Elmo;137
5.4.1.4;GraphUtil;142
5.4.2;Flink: the social networks of the Semantic Web community;143
5.4.2.1;The features of Flink;144
5.4.2.2;System design;146
5.4.3;openacademia: distributed, semantic-based publication management;150
5.4.3.1;The features of openacademia;151
5.4.3.2;System design;152
5.4.4;Discussion;159
6;Part III Case studies;161
6.1;Evaluation of web-based social network extraction;162
6.1.1;Differences between survey methods and electronic data extraction;164
6.1.2;Context of the empirical study;166
6.1.3;Data collection;167
6.1.4;Preparing the data;168
6.1.5;Optimizing goodness of fit;169
6.1.6;Comparison across methods and networks;172
6.1.7;Predicting the goodness of fit;174
6.1.8;Evaluation through analysis;177
6.1.9;Discussion;178
6.2;Semantic-based Social Network Analysis in the sciences;181
6.2.1;Context;183
6.2.2;Methodology;184
6.2.2.1;Data acquisition;184
6.2.2.2;Representation, storage and reasoning;186
6.2.2.3;Visualization and Analysis;187
6.2.3;Results;188
6.2.3.1;Descriptive analysis;188
6.2.3.2;Structural and cognitive effects on scientific performance;190
6.2.4;Conclusions and Future Work;196
6.3;Ontologies are us: emergent semantics in folksonomy systems;198
6.3.1;A tripartite model of ontologies;199
6.3.1.1;Ontology enrichment;201
6.3.2;Case studies;203
6.3.2.1;Ontology emergence in del.icio.us;203
6.3.2.2;Community-based ontology extraction from Web pages;208
6.3.3;Evaluation;210
6.3.4;Conclusions and Future Work;211
7;Part IV Conclusions;213
7.1;The perfect storm;214
7.1.1;Looking back: the story of Katrina PeopleFinder;215
7.1.1.1;The Semantic Web;219
7.1.1.2;Social Networks;222
7.1.2;Looking ahead: a Second Life;224
8;References;228
9;Index;236