Knowledge Graphs

  1. Hogan, Aidan 1
  2. Blomqvist, Eva 11
  3. Cochez, Michael 45
  4. D’amato, Claudia 3
  5. Melo, Gerard De 9
  6. Gutierrez, Claudio 1
  7. Kirrane, Sabrina 2
  8. Gayo, José Emilio Labra 6
  9. Navigli, Roberto 10
  10. Neumaier, Sebastian 2
  11. Ngomo, Axel-Cyrille Ngonga 12
  12. Polleres, Axel 2
  13. Rashid, Sabbir M. 7
  14. Rula, Anisa
  15. Schmelzeisen, Lukas 14
  16. Sequeda, Juan 13
  17. Staab, Steffen
  18. Zimmermann, Antoine 8
  1. 1 Universidad de Chile
    info

    Universidad de Chile

    Santiago de Chile, Chile

    ROR https://ror.org/047gc3g35

  2. 2 Vienna University of Economics and Business
    info

    Vienna University of Economics and Business

    Viena, Austria

    ROR https://ror.org/03yn8s215

  3. 3 University of Bari Aldo Moro
    info

    University of Bari Aldo Moro

    Bari, Italia

    ROR https://ror.org/027ynra39

  4. 4 VU University Amsterdam
    info

    VU University Amsterdam

    Ámsterdam, Holanda

    ROR https://ror.org/008xxew50

  5. 5 Discovery Lab, Elsevier, The Netherlands
  6. 6 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

  7. 7 Rensselaer Polytechnic Institute
    info

    Rensselaer Polytechnic Institute

    Troy, Estados Unidos

    ROR https://ror.org/01rtyzb94

  8. 8 École des mines de Saint-Étienne, France
  9. 9 Rutgers University
    info

    Rutgers University

    Nuevo Brunswick, Estados Unidos

    ROR https://ror.org/05vt9qd57

  10. 10 Università de Roma La Sapienza
    info

    Università de Roma La Sapienza

    Roma, Italia

    ROR https://ror.org/02be6w209

  11. 11 Linköping University
    info

    Linköping University

    Linköping, Suecia

    ROR https://ror.org/05ynxx418

  12. 12 University of Paderborn
    info

    University of Paderborn

    Paderborn, Alemania

    ROR https://ror.org/058kzsd48

  13. 13 data.world, USA
  14. 14 University of Stuttgart
    info

    University of Stuttgart

    Stuttgart, Alemania

    ROR https://ror.org/04vnq7t77

  15. 15 University of Southampton
    info

    University of Southampton

    Southampton, Reino Unido

    ROR https://ror.org/01ryk1543

  16. 16 University of Bonn
    info

    University of Bonn

    Bonn, Alemania

    ROR https://ror.org/041nas322

  17. 17 University of Milano-Bicocca
    info

    University of Milano-Bicocca

    Milán, Italia

    ROR https://ror.org/01ynf4891

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Journal:
ACM Computing Surveys

ISSN: 0360-0300,  1557-7341

Year of publication: 2021

Volume: 54

Issue: 4

Pages: 1-37

Type: Article

DOI: 10.1145/3447772 GOOGLE SCHOLAR lock_openOpen access editor

More publications in: ACM Computing Surveys

Sustainable development goals

Abstract

In this article, we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models, as well as languages used to query and validate knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We conclude with high-level future research directions for knowledge graphs.

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Funding information

Hogan was supported by Fondecyt Grant No. 1181896. Hogan and Gutierrez were funded by ANID– Millennium Science Initiative Program– Code ICN17_002. Cochez did part of the work while employed at Fraunhofer FIT, Germany and was later partially funded byElsevier’s Discovery Lab.Kirrane,NgongaNgomo,PolleresandStaabreceivedfundingthroughthe project “KnowGraphs” from the European Union’s Horizon programme under the Marie Skłodowska-Curie grant agreement No. 860801. Kirrane and Polleres were supported by the European Union’s Horizon 2020 research and innovation programme under grant 731601. Labra was supported by the Spanish Ministry of Economy and Competitiveness (Society challenges: TIN2017-88877-R). Navigli was supported by the MOUSSE ERC Grant No. 726487 under the European Union’s Horizon 2020 research and innovation programme. Rashid was supported by IBM Research AI through the AI Horizons Network. Schmelzeisen was supported by the German Research Foundation (DFG) grant STA 572/18-1.

Funders

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