Knowledge Graphs

Author:

Hogan Aidan1ORCID,Blomqvist Eva2,Cochez Michael3,D’amato Claudia4,Melo Gerard De5,Gutierrez Claudio1ORCID,Kirrane Sabrina6,Gayo José Emilio Labra7,Navigli Roberto8,Neumaier Sebastian6,Ngomo Axel-Cyrille Ngonga9,Polleres Axel6,Rashid Sabbir M.10,Rula Anisa11,Schmelzeisen Lukas12,Sequeda Juan13,Staab Steffen14,Zimmermann Antoine15

Affiliation:

1. DCC, Universidad de Chile; IMFD, Chile

2. Linköping University, Sweden

3. Vrije Universiteit and Discovery Lab, Elsevier, The Netherlands

4. University of Bari, Italy

5. Rutgers University, USA

6. WU Vienna, Austria

7. Universidad de Oviedo, Spain

8. Sapienza University of Rome, Italy

9. DICE, Universität Paderborn, Germany

10. Tetherless World Constellation, Rensselaer Polytechnic Institute, USA

11. University of Milano–Bicocca, Italy and University of Bonn, Germany

12. Universität Stuttgart, Germany

13. data.world, USA

14. Universität Stuttgart, Germany and University of Southampton, UK

15. École des mines de Saint-Étienne, France

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.

Funder

Marie Skłodowska-Curie

ANID – Millennium Science Initiative Program

MOUSSE ERC

Fondecyt

German Research Foundation

European Union’s Horizon 2020 research and innovation programme

Spanish Ministry of Economy and Competitiveness

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

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