TY - BOOK ID - 124040590 TI - Knowledge graphs AU - Hogan, Aidan. AU - Blomqvist, Eva. AU - Cochez, Michael. AU - d’Amato, Claudia. AU - Melo, Gerard de. AU - Gutierrez, Claudio. AU - Kirrane, Sabrina. AU - Labra Gayo, Jose Emilio. AU - Navigli, Roberto. AU - Neumaier, Sebastian. AU - Ngonga Ngomo, Axel-Cyrille. AU - Polleres, Axel. AU - Rashid, Sabbir. AU - Rula, Anisa. AU - Schmelzeisen, Lukas. AU - Sequeda, Juan. AU - Staab, Steffen. AU - Zimmermann, Antoine. PY - 2022 SN - 3031019180 3031001133 3031007905 9783031019180 9783031007903 PB - Cham : Springer, DB - UniCat KW - Internet programming. KW - Ontology. KW - Web Development. KW - Graphic methods KW - Information visualization. KW - Graphic methods. KW - Semantic computing. KW - Conceptual structures (Information theory) KW - Computer programs. UR - https://www.unicat.be/uniCat?func=search&query=sysid:124040590 AB - 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. ER -