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  • © 2022

Knowledge Graphs

Part of the book series: Synthesis Lectures on Data, Semantics, and Knowledge (SLDSK)

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  • ISBN: 978-3-031-01918-0
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Table of contents (11 chapters)

  1. Front Matter

    Pages i-xix
  2. Introduction

    • Aidan Hogan, Claudio Gutierrez, Michael Cochcz, Gerard de Melo, Sabrina Kirranc, Axel Pollcrcs et al.
    Pages 1-4
  3. Data Graphs

    • Aidan Hogan, Claudio Gutierrez, Michael Cochcz, Gerard de Melo, Sabrina Kirranc, Axel Pollcrcs et al.
    Pages 5-23
  4. Schema, Identity, and Context

    • Aidan Hogan, Claudio Gutierrez, Michael Cochcz, Gerard de Melo, Sabrina Kirranc, Axel Pollcrcs et al.
    Pages 25-45
  5. Deductive Knowledge

    • Aidan Hogan, Claudio Gutierrez, Michael Cochcz, Gerard de Melo, Sabrina Kirranc, Axel Pollcrcs et al.
    Pages 47-65
  6. Inductive Knowledge

    • Aidan Hogan, Claudio Gutierrez, Michael Cochcz, Gerard de Melo, Sabrina Kirranc, Axel Pollcrcs et al.
    Pages 67-104
  7. Creation and Enrichment

    • Aidan Hogan, Claudio Gutierrez, Michael Cochcz, Gerard de Melo, Sabrina Kirranc, Axel Pollcrcs et al.
    Pages 105-117
  8. Quality Assessment

    • Aidan Hogan, Claudio Gutierrez, Michael Cochcz, Gerard de Melo, Sabrina Kirranc, Axel Pollcrcs et al.
    Pages 119-125
  9. Refinement

    • Aidan Hogan, Claudio Gutierrez, Michael Cochcz, Gerard de Melo, Sabrina Kirranc, Axel Pollcrcs et al.
    Pages 127-131
  10. Publication

    • Aidan Hogan, Claudio Gutierrez, Michael Cochcz, Gerard de Melo, Sabrina Kirranc, Axel Pollcrcs et al.
    Pages 133-144
  11. Knowledge Graphs in Practice

    • Aidan Hogan, Claudio Gutierrez, Michael Cochcz, Gerard de Melo, Sabrina Kirranc, Axel Pollcrcs et al.
    Pages 145-151
  12. Conclusions

    • Aidan Hogan, Claudio Gutierrez, Michael Cochcz, Gerard de Melo, Sabrina Kirranc, Axel Pollcrcs et al.
    Pages 153-156
  13. Back Matter

    Pages 157-237

About this book

This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale. The book defines knowledge graphs and provides a high-level overview of how they are used. It presents and contrasts popular graph models that are commonly used to represent data as graphs, and the languages by which they can be queried before describing how the resulting data graph can be enhanced with notions of schema, identity, and context. The book discusses how ontologies and rules can be used to encode knowledge as well as how inductive techniques—based on statistics, graph analytics, machine learning, etc.—can be used to encode and extract knowledge. It covers techniques for the creation, enrichment, assessment, and refinement of knowledge graphs and surveys recent open and enterprise knowledge graphs and the industries or applications within which they have been most widely adopted. The book closes by discussing the current limitations and future directions along which knowledge graphs are likely to evolve. This book is aimed at students, researchers, and practitioners who wish to learn more about knowledge graphs and how they facilitate extracting value from diverse data at large scale. To make the book accessible for newcomers, running examples and graphical notation are used throughout. Formal definitions and extensive references are also provided for those who opt to delve more deeply into specific topics.

Authors and Affiliations

  • DCC, Universidad de Chile; IMFD, Chile

    Aidan Hogan, Claudio Gutierrez

  • Vrije Univerateit Amsterdam, Netherlands

    Michael Cochcz

  • Discovery Lab, Elsevier, Netherlands

    Michael Cochcz

  • HPI, University of Potsdam, Germany

    Gerard de Melo

  • Rutgers University, USA

    Gerard de Melo

  • WU Vienna, Austria

    Sabrina Kirranc, Axel Pollcrcs

  • Sapienza University of Rome, Italy

    Roberto Navigli

  • DICE, Universität Paderborn, Germany

    Axcl-Cyrille Ngonga Ngomo

  • Tetherless World Constellation, Rensselaer Polytechnic Institute, USA

    Sabbir M. Rashid

  • Universität Stuttgart, Germany

    Lukas Schmclzciscn, Steffen Staab

  • University of Southampton, United Kingdom

    Steffen Staab

  • Linköping University, Sweden

    Eva Blomqvist

  • University of Bari, Italy

    Claudia d’Amato

  • Universidad de Oviedo, Spain

    José Emilio Labra Gayo

  • St. Pölten University of Applied Sciences, Austria

    Sebastian Ncumaicr

  • University of Brescia, Italy

    Anisa Rula

  • data.world, USA

    Juan Scqucda

  • École des mines de Saint-Étienne, France

    Antoine Zimmermann

About the authors

Aidan Hogan is an Associate Professor at the Department of Computer Science, Universidad de Chile, where he also holds the position of Associate Researcher in the Millennium Institute for Foundational Research on Data (IMFD). He received a B.Eng. and Ph.D. from the National University of Ireland, Galway, in 2006 and 2011, respectively. His primary research interests center on the Semantic Web and Knowledge Graphs. He is the author of over 100 research publications on these topics, including 2 other books: Reasoning Techniques for the Web of Data and The Web of Data.
Eva Blomqvist is an Associate Professor at the Department of Computer and Information Science, Linköping University. She received a Ph.D. from Linköping University, Sweden, in 2009, in the area of Ontology Learning for the Semantic Web. After a postdoc at ISTC-CNR in Rome, Italy, she has been a member of the Semantic Web group at Linköping University since 2011. Her primary research interests include the SemanticWeb and Knowledge Graphs, more specifically the development and use of ontologies as schemas for Knowledge Graphs. She is the author of over 50 research publications in the area, and has served as scientific program chair of several of the top conferences in the field.
Michael Cochez is an Assistant Professor in the Knowledge Representation and Reasoning Group at the Computer Science department of the Vrije Universiteit, Amsterdam. He received his B.Sc. from the University of Antwerp, Belgium and his M.Sc. and Ph.D. degrees from the University of Jyväskylä, Finland. His research interests are in the intersection of Machine Learning and Knowledge Graphs.
Claudia d’Amato is an Associate Professor at the Department of Computer Science, University of Bari, Italy and a member of the Knowledge Acquisition and Machine Learning Lab. She also holds a habilitation as Full Professor for the scientific sectors: INF/01 and ING-INF/05. She received her Master’s Degree and Ph.D. from the University of Bari, Italy, in 2003 and 2007, respectively. Over the years, she has also spent several invited-researcher stays in different international universities and research institutes. Her primary research interests center on Machine Learning for the Semantic Web and Knowledge Graphs. She is the author of over 100 research publications on these topics.
Gerard deMelo is a Full Professor at the Hasso Plattner Institute for Digital Engineering and at the University of Potsdam, where he holds the Chair for Artificial Intelligence and Intelligent Systems and heads the corresponding research group. Previously, he was a faculty member at Rutgers University in New Jersey and at Tsinghua University in Beijing, and a Post-Doctoral Research Scholar at ICSI/UC Berkeley. He has published over 150 papers on Natural Language Processing, Knowledge Graphs, and AI, and received a number of best paper awards.

Buying options

eBook USD 59.99
Price excludes VAT (USA)
  • ISBN: 978-3-031-01918-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 74.99
Price excludes VAT (USA)