Knowledge Graphs

被引:471
作者
Hogan, Aidan [1 ,2 ]
Blomqvist, Eva [3 ]
Cochez, Michael [4 ,5 ]
D'Amato, Claudia [6 ]
de Melo, Gerard [7 ,19 ]
Gutierrez, Claudio [1 ,2 ]
Kirrane, Sabrina [8 ,20 ]
Labra Gayo, Jose Emilio [9 ]
Navigli, Roberto [10 ]
Neumaier, Sebastian [8 ,21 ]
Ngomo, Axel-Cyrille Ngonga [11 ]
Polleres, Axel [8 ]
Rashid, Sabbir M. [12 ]
Rula, Anisa [13 ,14 ,22 ]
Schmelzeisen, Lukas [15 ]
Sequeda, Juan [16 ]
Staab, Steffen [15 ,17 ]
Zimmermann, Antoine [18 ]
机构
[1] Univ Chile, DCC, Beauchef 851, Santiago, Chile
[2] IMFD, Santiago, Chile
[3] Linkoping Univ, S-58183 Linkoping, Sweden
[4] Vrije Univ Amsterdam, De Boelelaan 1111, NL-1081 HV Amsterdam, Netherlands
[5] Elsevier, Discovery Lab, Amsterdam, Netherlands
[6] Univ Bari, Dipartimento Informat, Campus Univ,Via Orabona 4, I-70126 Bari, Italy
[7] Rutgers State Univ, New Brunswick, NJ USA
[8] WU Vienna, Vienna, Austria
[9] Univ Oviedo, Dept Comp Sci, Oviedo 33007, Spain
[10] Sapienza Univ Rome, Rome, Italy
[11] Paderborn Univ, DICE, Warburgerstr 100, D-33098 Paderborn, Germany
[12] Rensselaer Polytech Inst, Tetherless World Constellat, 164 James St, Worcester, MA 01603 USA
[13] Univ Milano Bicocca, Milan, Italy
[14] Univ Bonn, Bonn, Germany
[15] Univ Stuttgart, IPVS, Univ Str 32, D-70569 Stuttgart, Germany
[16] Dataworld, 7000 N Mopac Expy Suite 425, Austin, TX 78731 USA
[17] Univ Southampton, Southampton, Hants, England
[18] Ecole Mines St Etienne, St Etienne, France
[19] HPI, Prof Dr Helmert Str 2, D-14482 Potsdam, Germany
[20] Vienna Univ Econ & Business, Weldhandelspl 1, A-1020 Vienna, Austria
[21] FH St Polten, A-3100 St Polten, Austria
[22] Dept Informat Engn, Via Branze 38, I-25121 Brescia, Italy
关键词
Knowledge graphs; graph databases; graph query languages; shapes; ontologies; graph algorithms; embeddings; graph neural networks; rule mining; SCALE;
D O I
10.1145/3447772
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
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.
引用
收藏
页数:37
相关论文
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