Knowledge Graphs

Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia D’Amato, Gerard de Melo, Claudio Gutierrez, Sabrina Kirrane, Jose Emilio Labra Gayo, Roberto Navigli, Sebastian Neumaier, Axel Polleres, Sabbir Rashid, Anisa Rula, Antoine Zimmermann, Lukas Schmelzeisen, Axel-Cyrille Ngonga Ngomo, Juan Sequeda, Steffen Staab (Autoren)

Buch | Softcover

XIX, 237 Seiten

2021
Springer International Publishing (Verlag)
978-3-031-00790-3 (ISBN)

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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.

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, Linkoeping University. She received a Ph.D. from Linkoeping 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 Linkoeping 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 Jyvaskyla, 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.

Preface.- Acknowledgments.- Introduction.- Data Graphs.- Schema, Identity, and Context.- Deductive Knowledge.- Inductive Knowledge.- Creation and Enrichment.- Quality Assessment.- Refinement.- Publication.- Knowledge Graphs in Practice.- Conclusions.- Bibliography.- Authors' Biographies.

Erscheinungsdatum	06.06.2022
Reihe/Serie	Synthesis Lectures on Data, Semantics, and Knowledge
Zusatzinfo	XIX, 237 p. 2 illus.
Verlagsort	Cham
Sprache	englisch
Maße	191 x 235 mm
Gewicht	496 g
Themenwelt	Geisteswissenschaften ► Philosophie ► Metaphysik / Ontologie
Themenwelt	Mathematik / Informatik ► Informatik ► Web / Internet
ISBN-10	3-031-00790-5 / 3031007905
ISBN-13	978-3-031-00790-3 / 9783031007903
Zustand	Neuware