Linked Data Visualization

Book description

Linked Data (LD) is a well-established standard for publishing and managing structured information on the Web, gathering and bridging together knowledge from different scientific and commercial domains. The development of Linked Data Visualization techniques and tools has been adopted as the established practice for the analysis of this vast amount of information by data scientists, domain experts, business users, and citizens.

This book covers a wide spectrum of visualization topics, 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 core concepts related to data visualization and LD technologies, techniques employed for data visualization based on the characteristics of data, techniques for Big Data visualization, tools and use cases in the LD context, and, finally, a thorough assessment of the usability of these tools under different scenarios.

The purpose of this book is to offer a complete guide to the evolution of LD visualization for interested readers from any background and to empower them to get started with the visual analysis of such data. This book can serve as a course textbook or as a primer for all those interested in LD and data visualization.

Table of contents

  1. Cover
  2. Copyright
  3. Title Page
  4. Contents
  5. Preface
  6. Acknowledgments
  7. 1 Introduction
    1. 1.1 The Power of Visualization on Linked Data
    2. 1.2 The Web of Linked, Open, and Semantic Data
    3. 1.3 Principles of Linked Data
    4. 1.4 The Linked Open Data Cloud
    5. 1.5 Web of Data in Numbers
    6. 1.6 The Value and Impact of Linked and Open Data
    7. 1.7 Semantic Web Technologies
    8. 1.8 Conclusions
  8. 2 Principles of Data Visualization
    1. 2.1 Data Visualization Design Process
    2. 2.2 Data Visualization Types
      1. 2.2.1 Visualizing Patterns over Time
      2. 2.2.2 Visualizing Proportions
      3. 2.2.3 Visualizing Graph Relationships
      4. 2.2.4 Visualizing Data on Maps
    3. 2.3 Interactive Visualization
    4. 2.4 Visualization in Big Data Era
      1. 2.4.1 How Does the Visualization of Big Data Differ from Traditional Ones?
      2. 2.4.2 Visualization Systems and Techniques
    5. 2.5 Conclusions
  9. 3 Linked Data Visualization Tools
    1. 3.1 Evolution Over Time
    2. 3.2 Browsers and Exploratory Tools
    3. 3.3 Tools Using Multiple Visualization Types
    4. 3.4 Graph-Based Visualization Tools
    5. 3.5 Domain, Vocabulary-Specific, and Device-Oriented Visualization Tools
    6. 3.6 Ontology Visualization Tools
    7. 3.7 Conclusions
  10. 4 Visualization Use Cases
    1. 4.1 User Needs on LD Visual Exploration
    2. 4.2 Use Cases
    3. 4.3 Modeling Use Cases
      1. 4.3.1 T-Box Related Use Cases
      2. 4.3.2 A-Box Related Use Cases
      3. 4.3.3 T-Box and A-Box Related Use Cases
    4. 4.4 Conclusions
  11. 5 Empirical Evaluation of Linked Data Visualization Tools
    1. 5.1 Basic Characteristics of the Tools
    2. 5.2 Evaluation
      1. 5.2.1 Evaluation of T-Box Use Cases
      2. 5.2.2 Evaluation of A-Box Use Cases
      3. 5.2.3 Evaluation of A-Box and T-Box Uses Cases
      4. 5.2.4 Evaluation Summary
    3. 5.3 Different Tools for Different Tasks
    4. 5.4 Conclusions
  12. 6 Conclusions and Future Challenges
    1. 6.1 Future Challenges
  13. Bibliography
  14. Authors’ Biographies

Product information

  • Title: Linked Data Visualization
  • Author(s): Laura Po, Nikos Bikakis, Federico Desimoni, George Papastefanatos
  • Release date: March 2020
  • Publisher(s): Morgan & Claypool Publishers
  • ISBN: 9781681738345