Linked Data Visualization. Laura Po

Evaluation Summary

       5.3 Different Tools for Different Tasks

       5.4 Conclusions

       6 Conclusions and Future Challenges

       6.1 Future Challenges

       Bibliography

       Authors’ Biographies

       Preface

      The Linked Data Principles defined by Tim Berners-Lee promise that a large portion of Web Data will be usable as one big interlinked RDF database. Today, we are assisting the staggering growth in both the production and consumption of Linked Data (LD) coming from diverse domains such as health and biology, humanities and social sciences, or open government. In the early phases of LD adoption, most efforts focused on the representation and publication of large volumes of privately held data in the form of Linked Open Data (LOD), contributing to the generation of the Linked Open Data Cloud.

      Nowadays, given the wide adoption and availability of a very large number of LD sources, it is crucial to provide intuitive tools for researchers, data scientists, and domain experts as well as business users and citizens to visualize and interact with increasingly large datasets. Visual analytics integrates the analytic capabilities of the computer and the abilities of the human analyst, allowing novel discoveries and empowering individuals to take control of the analytical process. LD visualization aims to provide graphical representations of datasets or of some information of interest selected by a user, with the aim of facilitating their analysis and generating insights into complex interconnected information. Visualization techniques can vary according to the domain, the type of data, the task that the user is trying to perform, as well as the characteristics of the user (e.g., skills).

      This book presents the principles of LD visualization, as well as demonstrates and evaluates state-of-the-art LD visualization tools. Moreover, future challenges and opportunities in the field of Big (Linked) Data visualization are presented.

      The book is written for everyone who wants to explore and exploit LD, whether undergraduate and post-graduate students, data scientists, semantic technology developers, or UI & UX designers who wish to gain some practical experience with LD tools. Previous knowledge of Semantic Web technologies such as RDF, OWL, SPARQL, or programming skills is not required. The purpose of this book is to empower readers of any background to get started with their own experiments on the LOD Cloud, select the most appropriate LD tool for each scenario, and be aware of the challenges and techniques related to Big Linked Data exploration.

      Since readers are likely to have a wide variety of different backgrounds, each chapter presents an overview of its content at the beginning. A reader who wishes to have a quick overview can start with the first page of each chapter. When the material in any section becomes more advanced, the reader can skip to the beginning of the next section without losing continuity. Chapter 1 introduces the Web of Linked Data, describing the phenomenon of the production and consumption of LD, the social and economic impact that this data has, and the effect that visualization tools can have in facilitating the understanding and exploitation of such data. Moreover, it presents the principles of LD and the technologies of the Semantic Web Stack. Chapter 2 addresses how data can be presented in visual form, focusing on interactive and specialized visualizations of proportions, relationships, and spatial data. Further, it introduces the new challenges and methods related to Big Data Visualization. Chapter 3 surveys the variety of linked data visualization tools. Chapter 4 defines and models a set of visualization use cases based on the users’ requirements in LD exploration. Chapter 5 describes a wide empirical evaluation of the tools introduced in Chapter 3. Here, a practical evaluation of the tools will be shown in order to describe their characteristics and limitations as well as formalize how the tools handle the use cases described in Chapter 4. Chapter 6 reports some conclusions and open issues and suggests research challenges and promising trends for the future.

      Laura Po, Nikos Bikakis, Federico Desimoni, and George Papastefanatos

      March 2020

       Acknowledgments

      Sincere thanks go to Dr. Jakub Klímek for his careful reading of the manuscript and his constructive remarks. The work carried out in this book was partially supported by the Networking on Linked Data project funded by the “Enzo Ferrari” Engineering Department of the University of Modena and Reggio Emilia within FAR2019 as well as by the VisualFacts project (#1614) funded by the 1st Call of the Hellenic Foundation for Research and Innovation Research Projects for the support of post-doctoral researchers.

      Laura Po, Nikos Bikakis, Federico Desimoni, and George Papastefanatos

      March 2020

      CHAPTER 1

       Introduction

      Linked data provides the basis for knowledge to be distributed, networked, and shared. The term Linked Data (LD) refers to a set of best practices for publishing and interlinking structured data on the Web. Creating a connection between data and its contexts could lead to the development of intelligent search engines which could explore the Web, moving from a keyword-based approach to a meaning-based approach. Researches can be more accurate by exploiting the relations between words. LD can provide a benefit in several research areas like in the medical field for structuring the connections between various illness and the relative cures, in the scientific literature for structuring the citations between the million of documents published online. The potentialities of exploitation of LD are countless.

      On the other hand, given the wide availability of LD sources, it is crucial to provide intuitive tools enabling users without semantic technology background to explore, analyze, and interact with increasingly large datasets. Visual analytics integrates the analytic capabilities of the computer and the abilities of the human analyst, allowing novel discoveries and empowering individuals to take control of the analytical process. LD visualization aims to provide graphical representations of datasets with the aim to facilitate their analysis and the generation of insights out of complex interconnected information.

      In this chapter, we will introduce why visualization is a powerful means for linked data exploration, then, the principles and technologies that are the bases for the creation of LD are presented, and we also depict the incredible impact that LD can have in the real world.

      In the next section, we start illustrating how visualization is good way of interacting with the corresponding very large amounts of complex, interlinked, multi-dimensional data. The evolution of the web from Web 1.0 to Web 4.0. is depicted in Section 1.2. We highlight the principles of LD in Section 1.3; after this, we describe the Linked Data Cloud (Section 1.4) that draws datasets that have been published according to those principles. Sections 1.5 and 1.6