Linked Data (LD) is nowadays a well established standard for publishing and managing structured information on the Web, gathering and bridging together knowledge from very different scientific and commercial domains. The development of Linked Data Visualization techniques and tools has been followed as the primary means for the analysis of this vast amount of information by data scientists, domain experts, business users and citizens. This book aims at providing an overview of the recent advances in this area, focusing on techniques, tools and use cases of visualization and visual analysis of LD. It presents all necessary preliminary concepts related to the LD technology, the main techniques employed for data visualization based on the characteristics of the underlying data, use cases and tools for LD visualization and finally a thorough assessment of the usability of these tools, under different business scenarios. The goal of this book is to offer interested readers a complete guide on the evolution of LD visualization and empower them to get started with the visual analysis of such data.
Keywords: Linked Data, Visual Analytics, Big Data, Visualization Tools, Web of Data, Semantic Web, Data Exploration, Information Visualization, Human Computer Interaction, Usability
Book Citation: Laura Po, Nikos Bikakis, Federico Desimoni, and George Papastefanatos, "Linked Data Visualization: Techniques, Tools and Big Data" Morgan & Claypool, 2020
Book Homepage: http://www.linkeddatavisualization.com
1 Introduction
1.1 The Power of Visualization on Linked Data
1.2 TheWeb of Linked, Open, and Semantic Data
1.3 Principles of Linked Data
1.4 The Linked Open Data Cloud
1.5 Web of Data in Numbers
1.6 The Value and Impact of Linked and Open Data
1.7 Semantic Web Technologies
1.8 Conclusions
2 Principles of Data Visualization
2.1 Data Visualization Design Process
2.2 Data Visualization Types
2.2.1 Visualizing Patterns over Time
2.2.2 Visualizing Proportions
2.2.3 Visualizing Graph Relationships
2.2.4 Visualizing Data on Maps
2.3 Interactive Visualization
2.4 Visualization in Big Data era
2.4.1 How does the visualization of Big Data differs from traditional ones?
2.4.2 Visualization Systems and Techniques
2.5 Conclusions
3 Linked Data Visualization Tools
3.1 Evolution Over Time
3.2 Browsers & Exploratory Tools
3.3 Tools using Multiple Visualization Types
3.4 Graph-based Visualization Tools
3.5 Domain, Vocabulary-specific; and Device-oriented Visualization Tools
3.6 Ontology Visualization Tools
3.7 Conclusions
4 Visualization Use Cases
4.1 RelatedWork on Users’ Requirements for LD Consumption
4.2 Definition of a Set of Visualization Use Cases
4.3 Activity Diagrams to Model Use Cases
4.3.1 T-Box related use cases
4.3.2 A-Box related use cases
4.3.3 T-Box and A-Box related use cases
4.4 Conclusions
5 Empirical Evaluation of Linked Data Visualization Tools
5.1 The Basic Characteristics of the Tools
5.2 Evaluation of the tools w.r.t. the Use Cases
5.2.1 Evaluation of T-Box use cases
5.2.2 Evaluation of A-Box use cases
5.2.3 Evaluation of A-Box and T-Box uses cases
5.2.4 Overall evaluation
5.3 Different tools for different goals
5.4 Conclusions
6 Conclusions and Future Challenges