Knowledge Graphs
Aidan Hogan
(1)
,
Eva Blomqvist
(2)
,
Michael Cochez
(3, 4)
,
Claudia D’amato
(5)
,
Gerard De Melo
(6)
,
Claudio Gutierrez
(1)
,
Sabrina Kirrane
(7)
,
José Emilio Labra Gayo
(8)
,
Roberto Navigli
(9)
,
Sebastian Neumaier
(7)
,
Axel-Cyrille Ngonga Ngomo
(10)
,
Axel Polleres
(7)
,
Sabbir Rashid
(11)
,
Anisa Rula
(12, 13)
,
Lukas Schmelzeisen
(14)
,
Juan Sequeda
(15)
,
Steffen Staab
(14)
,
Antoine Zimmermann
(16, 17, 18, 19)
1
IMFD -
Millennium Institute for Foundational Research on Data
2 LIU - Linköping University
3 VUB - Vrije Universiteit Brussel [Bruxelles]
4 Discovery Lab
5 Polytechnic University of Bari / Politecnico di Bari
6 Rutgers - Rutgers University System
7 WU Vienna
8 Universidad de Oviedo = University of Oviedo
9 UNIROMA - Università degli Studi di Roma "La Sapienza" = Sapienza University [Rome]
10 UPB - Universität Paderborn
11 Tetherless World Constellation
12 University of Milano
13 Universität Bonn = University of Bonn
14 Universität Stuttgart [Stuttgart]
15 data.world
16 Mines Saint-Étienne MSE - École des Mines de Saint-Étienne
17 LIMOS - Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes
18 FAYOL-ENSMSE - Institut Henri Fayol
19 FAYOL-ENSMSE - Département Informatique et systèmes intelligents
2 LIU - Linköping University
3 VUB - Vrije Universiteit Brussel [Bruxelles]
4 Discovery Lab
5 Polytechnic University of Bari / Politecnico di Bari
6 Rutgers - Rutgers University System
7 WU Vienna
8 Universidad de Oviedo = University of Oviedo
9 UNIROMA - Università degli Studi di Roma "La Sapienza" = Sapienza University [Rome]
10 UPB - Universität Paderborn
11 Tetherless World Constellation
12 University of Milano
13 Universität Bonn = University of Bonn
14 Universität Stuttgart [Stuttgart]
15 data.world
16 Mines Saint-Étienne MSE - École des Mines de Saint-Étienne
17 LIMOS - Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes
18 FAYOL-ENSMSE - Institut Henri Fayol
19 FAYOL-ENSMSE - Département Informatique et systèmes intelligents
Résumé
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.