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Knowledge Graphs

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Knowledge Graphs/ by 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.
Author:	Hogan, Aidan.
other author:	Blomqvist, Eva.
Description:	XIX, 237 p.online resource. :
Contained By:	Springer Nature eBook
Subject:	Internet programming. -
Online resource:	https://doi.org/10.1007/978-3-031-01918-0
ISBN:	9783031019180

Knowledge Graphs
Hogan, Aidan.

Knowledge Graphs[electronic resource] /by 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. - 1st ed. 2022. - XIX, 237 p.online resource. - Synthesis Lectures on Data, Semantics, and Knowledge,2691-2031. - Synthesis Lectures on Data, Semantics, and Knowledge,.

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.

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.

ISBN: 9783031019180

Standard No.: 10.1007/978-3-031-01918-0doiSubjects--Topical Terms:

565353
Internet programming.

LC Class. No.: QA76.625

Dewey Class. No.: 006.76

Knowledge Graphs
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