Knowledge Graphs

  1. Hogan, Aidan 9
  2. Gutierrez, Claudio 9
  3. Cochcz, Michael 78
  4. Melo, Gerard de 45
  5. Kirranc, Sabrina 6
  6. Pollcrcs, Axel 6
  7. Navigli, Roberto 15
  8. Ngomo, Axcl-Cyrille Ngonga 11
  9. Rashid, Sabbir M. 12
  10. Schmclzciscn, Lukas 3
  11. Staab, Steffen 313
  12. Blomqvist, Eva 16
  13. d’Amato, Claudia 18
  14. Gayo, José Emilio Labra 2
  15. Ncumaicr, Sebastian 10
  16. Rula, Anisa 17
  17. Scqucda, Juan 1
  18. Zimmermann, Antoine 14
  1. 1 data.world, USA
  2. 2 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

  3. 3 University of Stuttgart
    info

    University of Stuttgart

    Stuttgart, Alemania

    ROR https://ror.org/04vnq7t77

  4. 4 University of Potsdam
    info

    University of Potsdam

    Potsdam, Alemania

    ROR https://ror.org/03bnmw459

  5. 5 Rutgers University
    info

    Rutgers University

    Nuevo Brunswick, Estados Unidos

    ROR https://ror.org/05vt9qd57

  6. 6 Vienna University of Economics and Business
    info

    Vienna University of Economics and Business

    Viena, Austria

    ROR https://ror.org/03yn8s215

  7. 7 VU University Amsterdam
    info

    VU University Amsterdam

    Ámsterdam, Holanda

    ROR https://ror.org/008xxew50

  8. 8 Discovery Lab, Elsevier, Netherlands
  9. 9 Universidad de Chile
    info

    Universidad de Chile

    Santiago de Chile, Chile

    ROR https://ror.org/047gc3g35

  10. 10 St. Pölten University of Applied Sciences
    info

    St. Pölten University of Applied Sciences

    Sankt Pölten, Austria

    ROR https://ror.org/039a2re55

  11. 11 University of Paderborn
    info

    University of Paderborn

    Paderborn, Alemania

    ROR https://ror.org/058kzsd48

  12. 12 Rensselaer Polytechnic Institute
    info

    Rensselaer Polytechnic Institute

    Troy, Estados Unidos

    ROR https://ror.org/01rtyzb94

  13. 13 University of Southampton
    info

    University of Southampton

    Southampton, Reino Unido

    ROR https://ror.org/01ryk1543

  14. 14 École des mines de Saint-Étienne, France
  15. 15 Università de Roma La Sapienza
    info

    Università de Roma La Sapienza

    Roma, Italia

    ROR https://ror.org/02be6w209

  16. 16 Linköping University
    info

    Linköping University

    Linköping, Suecia

    ROR https://ror.org/05ynxx418

  17. 17 Università degli Studi di Brescia
    info

    Università degli Studi di Brescia

    Brescia, Italia

    ROR https://ror.org/02q2d2610

  18. 18 University of Bari Aldo Moro
    info

    University of Bari Aldo Moro

    Bari, Italia

    ROR https://ror.org/027ynra39

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Editorial: Springer

ISSN: 2691-2023,  2691-2031

ISBN: 9783031007903,  9783031019180

Año de publicación: 2022

Tipo: Libro

DOI: 10.1007/978-3-031-01918-0 GOOGLE SCHOLAR

Objetivos de desarrollo sostenible

Resumen

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.