Knowledge Graphs

Aidan Hogan; Eva Blomqvist; Michael Cochez; Gerard de Melo; Claudio Gutierrez; Sabrina Kirrane; José Emilio Labra Gayo; Roberto Navigli; Sebastian Neumaier; Axel-Cyrille Ngonga Ngomo; Axel Polleres; Sabbir M. Rashid; Anisa Rula; Lukas Schmelzeisen; Juan Sequeda; Steffen Staab; Antoine Zimmermann; Claudia d Amato

doi:10.1145/3447772

Knowledge Graphs

Aidan Hogan, Eva Blomqvist, Michael Cochez, Gerard de Melo, Claudio Gutierrez, Sabrina Kirrane, José Emilio Labra Gayo, Roberto Navigli, Sebastian Neumaier, Axel-Cyrille Ngonga Ngomo, Axel Polleres, Sabbir M. Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Sequeda, Steffen Staab, Antoine Zimmermann, Claudia d Amato

Publication: Scientific journal › Journal article › peer-review

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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.

Original language	English
Pages (from-to)	1 - 37
Journal	ACM Computing Surveys
Volume	54
DOIs	https://doi.org/10.1145/3447772
Publication status	Published - 2021

Austrian Classification of Fields of Science and Technology (ÖFOS)

102
102001 Artificial intelligence
102015 Information systems
502050 Business informatics
505002 Data protection

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@article{b7feecc23a204fdfab1182b6fc6289c1,

title = "Knowledge Graphs",

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.",

author = "Aidan Hogan and Eva Blomqvist and Michael Cochez and {de Melo}, Gerard and Claudio Gutierrez and Sabrina Kirrane and {Labra Gayo}, {Jos{\'e} Emilio} and Roberto Navigli and Sebastian Neumaier and {Ngonga Ngomo}, Axel-Cyrille and Axel Polleres and Rashid, {Sabbir M.} and Anisa Rula and Lukas Schmelzeisen and Juan Sequeda and Steffen Staab and Antoine Zimmermann and {d Amato}, Claudia",

year = "2021",

doi = "10.1145/3447772",

language = "English",

volume = "54",

pages = "1 -- 37",

journal = "ACM Computing Surveys",

issn = "0360-0300",

publisher = "Association for Computing Machinery (ACM)",

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TY - JOUR

T1 - Knowledge Graphs

AU - Hogan, Aidan

AU - Blomqvist, Eva

AU - Cochez, Michael

AU - de Melo, Gerard

AU - Gutierrez, Claudio

AU - Kirrane, Sabrina

AU - Labra Gayo, José Emilio

AU - Navigli, Roberto

AU - Neumaier, Sebastian

AU - Ngonga Ngomo, Axel-Cyrille

AU - Polleres, Axel

AU - Rashid, Sabbir M.

AU - Rula, Anisa

AU - Schmelzeisen, Lukas

AU - Sequeda, Juan

AU - Staab, Steffen

AU - Zimmermann, Antoine

AU - d Amato, Claudia

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

U2 - 10.1145/3447772

DO - 10.1145/3447772

M3 - Journal article

SN - 0360-0300

VL - 54

SP - 1

EP - 37

JO - ACM Computing Surveys

JF - ACM Computing Surveys

ER -