Laura Po - Linked Data Visualization: Techniques, Tools, and Big Data

Linked Data Visualization

Techniques, Tools, and Big Data

Synthesis Lectures on Data, Semantics, and Knowledge

Editors

Ying Ding, University of Texas at Austin

Paul Groth, University of Amsterdam

Founding Editor Emeritus

James Hendler, Rensselaer Polytechnic Institute

Synthesis Lectures on Data, Semantics, and Knowledge is edited by Ying Ding of the University of Texas at Austin and Paul Groth of the University of Amsterdam. The series focuses on the pivotal role that data on the web and the emergent technologies that surround it play both in the evolution of the World Wide Web as well as applications in domains requiring data integration and semantic analysis. The large-scale availability of both structured and unstructured data on the Web has enabled radically new technologies to develop. It has impacted developments in a variety of areas including machine learning, deep learning, semantic search, and natural language processing. Knowledge and semantics are a critical foundation for the sharing, utilization, and organization of this data. The series aims both to provide pathways into the field of research and an understanding of the principles underlying these technologies for an audience of scientists, engineers, and practitioners.

Topics to be included:

Knowledge graphs, both public and private

Linked Data

Knowledge graph and automated knowledge base construction

Knowledge engineering for large-scale data

Machine reading

Uses of Semantic Web technologies

Information and knowledge integration, data fusion

Various forms of semantics on the web (e.g., ontologies, language models, and distributional semantics)

Terminology, Thesaurus, & Ontology Management

Query languages

Linked Data Visualization: Techniques, Tools, and Big Data

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

2019

Ontology Engineering

Elisa F. Kendall and Deborah L. McGuinness

2019

Demistifying OWL for the Enterprise

Michael Uschold

2018

Validating RDF Data

Jose Emilio Labra Gayo, Eric Prudhommeaux, Iovka Boneva, and Dimitris Kontokostas

2017

Natural Language Processing for the Semantic Web

Diana Maynard, Kalina Bontcheva, and Isabelle Augenstein

2016

The Epistemology of Intelligent Semantic Web Systems

Mathieu dAquin and Enrico Motta

2016

Entity Resolution in the Web of Data

Vassilis Christophides, Vasilis Efthymiou, and Kostas Stefanidis

2015

Library Linked Data in the Cloud: OCLCs Experiments with New Models of Resource Description

Carol Jean Godby, Shenghui Wang, and Jeffrey K. Mixter

2015

Semantic Mining of Social Networks

Jie Tang and Juanzi Li

2015

Social Semantic Web Mining

Tope Omitola, Sebastin A. Ros, and John G. Breslin

2015

Semantic Breakthrough in Drug Discovery

Bin Chen, Huijun Wang, Ying Ding, and David Wild

2014

Semantics in Mobile Sensing

Zhixian Yan and Dipanjan Chakraborty

2014

Provenance: An Introduction to PROV

Luc Moreau and Paul Groth

2013

Resource-Oriented Architecture Patterns for Webs of Data

Brian Sletten

2013

Aaron Swartzs A Programmable Web: An Unfinished Work

Aaron Swartz

2013

Incentive-Centric Semantic Web Application Engineering

Elena Simperl, Roberta Cuel, and Martin Stein

2013

Publishing and Using Cultural Heritage Linked Data on the Semantic Web

Eero Hyvnen

2012

VIVO: A Semantic Approach to Scholarly Networking and Discovery

Katy Brner, Michael Conlon, Jon Corson-Rikert, and Ying Ding

2012

Linked Data: Evolving the Web into a Global Data Space

Tom Heath and Christian Bizer

2011

Copyright 2020 by Morgan & Claypool

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any meanselectronic, mechanical, photocopy, recording, or any other except for brief quotations in printed reviews, without the prior permission of the publisher.

Linked Data Visualization: Techniques, Tools, and Big Data

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

www.morganclaypool.com

ISBN: 9781681737256paperback

ISBN: 9781681737263ebook

ISBN: 9781681738345epub

ISBN: 9781681737270hardcover

DOI 10.2200/S00967ED1V01Y201911WBE019

A Publication in the Morgan & Claypool Publishers series

SYNTHESIS LECTURES ON DATA, SEMANTICS, AND KNOWLEDGE

Lecture #19

Series Editors: Ying Ding, University of Texas at Austin

Paul Groth, University of Amsterdam

Founding Editor Emeritus: James Hendler, Rensselaer Polytechnic Institute

Series ISSN

Print 2160-4711Electronic 2160-472X

Linked Data Visualization

Techniques, Tools, and Big Data

Laura Po

University of Modena and Reggio Emilia, Italy

Nikos Bikakis

University of Ioannina, Greece

Federico Desimoni

University of Modena and Reggio Emilia, Italy

George Papastefanatos

ATHENA Research Center, Greece

SYNTHESIS LECTURES ON DATA, SEMANTICS, AND KNOWLEDGE #19

ABSTRACT

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.

KEYWORDS

linked data, data visualization, visual analytics, big data, visualization tools, web of data, semantic web, data exploration, information visualization, usability evaluation, human-computer interaction

Contents

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.