Linked Data Visualization. Laura Po

      

       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 Prud’hommeaux, 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 d’Aquin 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: OCLC’s 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, Sebastián A. Ríos, 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 Swartz’s 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 Hyvönen

      2012

      VIVO: A Semantic Approach to Scholarly Networking and Discovery

      Katy Börner, 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 means—electronic, 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: 9781681737256 paperback

      ISBN: 9781681737263 ebook

      ISBN: 9781681737270 hardcover

      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 2160-4711 Electronic 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