Aidan Hogan | Eva Blomqvist | Michael Cochez
Claudia d’Amato | Gerard de Melo | Claudio Gutierrez
Sabrina Kirrane | José Emilio Labra Gayo | Roberto Navigli
Sebastian Neumaier | Axel-Cyrille Ngonga Ngomo
Axel Polleres | Sabbir M. Rashid | Anisa Rula
Lukas Schmelzeisen | Juan Sequeda
Steffen Staab | Antoine Zimmermann
kg-tutorial [at] googlegroups [dot] comThe book is published by Springer in the series Synthesis Lectures on Data, Semantics, and Knowledge edited by Ying Ding and Paul Groth. The book and series was previously published by Morgan & Claypool. Please cite the book as:
Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d’Amato, Gerard de Melo, Claudio Gutierrez, Sabrina Kirrane, José Emilio Labra Gayo, Roberto Navigli, Sebastian Neumaier, Axel-Cyrille Ngonga Ngomo, Axel Polleres, Sabbir M. Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Sequeda, Steffen Staab, Antoine Zimmermann (2021) Knowledge Graphs, Synthesis Lectures on Data, Semantics, and Knowledge, No. 22, 1–237, DOI: 10.2200/S01125ED1V01Y202109DSK022, Springer.
BibTeX entry of this book:
@book{kg-book,
author = {Hogan, Aidan and Blomqvist, Eva and Cochez, Michael and
d'Amato, Claudia and de Melo, Gerard and Guti\'errez, Claudio and
Kirrane, Sabrina and Labra Gayo, Jos\'e Emilio and Navigli, Roberto and
Neumaier, Sebastian and Ngonga Ngomo, Axel-Cyrille and Polleres, Axel and
Rashid, Sabbir M. and Rula, Anisa and Schmelzeisen, Lukas and
Sequeda, Juan F. and Staab, Steffen and Zimmermann, Antoine},
doi = {10.2200/S01125ED1V01Y202109DSK022},
isbn = {9783031007903},
language = {English},
number = {22},
numpages = {237},
publisher = {Springer},
series = {Synthesis Lectures on Data, Semantics, and Knowledge},
title = {{K}nowledge {G}raphs},
url = {https://kgbook.org/},
year = {2021}
}Copyright © 2021 by Springer. All rights reserved.
kg-tutorial [at] googlegroups [dot] com. Example code and associated resources can be found on Github as well.SYNTHESIS LECTURES ON ON DATA, SEMANTICS, AND KNOWLEDGE #22
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.
knowledge graphs, graph databases, knowledge graph embeddings, graph neural networks, ontologies, knowledge graph refinement, knowledge graph quality, knowledge bases, artificial intelligence, semantic web, machine learning