Articles liés à Knowledge Graphs

9783031007903: Knowledge Graphs
Couverture souple
ISBN 10 : 3031007905 ISBN 13 : 9783031007903
Editeur : Springer, 2021

Synopsis

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.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

À propos de l'auteur

Aidan Hogan is an Associate Professor at the Department of Computer Science, Universidad de Chile, and an Associate Researcher in the Millennium Institute for Foundational Research on Data (IMFD).

Eva Blomqvist is an Associate Professor at the Department of Computer and Information Science, Linköping University.

Michael Cochez is an Assistant Professor in the Knowledge Representation and Reasoning Group at the Computer Science department of the Vrije Universiteit, Amsterdam.

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.

Gerard de Melo 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.

Claudio Gutierrez is Full Professor at the Department of Computer Science, Universidad de Chile, and a Senior Researcher in the Millennium Institute for Foundational Research on Data (IMFD).

Sabrina Kirrane is an Assistant Professor at the Vienna University of Economics and Business Institute for Information Systems and New Media, where she is also a member of the Research Institute for Cryptoeconomics and the Sustainable Computing Lab.

Jose Emilio Labra Gayo is an Associate Professor at the University of Oviedo, Spain.

Roberto Navigli is a Full Professor of Computer Science at the Sapienza University of Rome, where he leads the Sapienza NLP Group.

Sebastian Neumaier is a researcher in the Data Intelligence group at the St. Poelten University of Applied Sciences, Austria.

Axel-Cyrille Ngonga Ngomo is a Full Professor for Data Science at Paderborn University.

Axel Polleres heads the Institute for Data, Process and Knowledge Management as a Full Professor of Vienna University of Economics and Business (WU Wien).

Sabbir M. Rashid is a Ph.D. candidate at Rensselaer Polytechnic Institute (RPI) working with Deborah L. McGuinness.

Anisa Rula is an Assistant Professor in Computer Science at the Department of Information Engineering, University of Brescia.

Lukas Schmelzeisen is a PhD candidate working with Steffen Staab in the Analytic Computing group at the University of Stuttgart, Germany.

Juan Sequeda is the Principal Scientist at data.world, which he joined through the acquisition of Capsenta: a company he founded as a spin-off from his research.

Steffen Staab holds a Cyber Valley endowed chair for Analytic Computing at the University of Stuttgart, Germany, and a chair for Web and Computer Science at the University of Southampton, UK.

Antoine Zimmermann is an Associate Professor at Mines Saint-Étienne in France.

À propos de la quatrième de couverture

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.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
Springer
Date d'édition
2021
Langue
anglais
ISBN 10 
3031007905
ISBN 13 
9783031007903
Reliure
Broché
Nombre de pages
237
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