Knowledge Graphs

Aidan Hogan; Eva Blomqvist; Michael Cochez; Claudia D’amato; 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

doi:10.1145/3447772

Knowledge Graphs

Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia D’amato, 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

Research output: Contribution to journal › Article › peer-review

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
Journal	ACM Computing Surveys
Volume	54
Issue number	4
DOIs	https://doi.org/10.1145/3447772
State	Published - Jul 1 2021
Externally published	Yes

Keywords

Knowledge graphs
embeddings
graph algorithms
graph databases
graph neural networks
graph query languages
ontologies
rule mining
shapes

Access to Document

10.1145/3447772

https://doi-org.vu-nl.idm.oclc.org/10.1145/3447772

DL: ICAI Discovery Lab
van Harmelen, F. (CoPI), De Rijke, M. (CoI), Siebert, M. (CoI), Hoekstra, R. (CoPI), Tsatsaronis, G. (CoPI), Groth, P. (CoPI), Cochez, M. (CoI), Pernisch, R. (CoI), Alivanistos, D. (CoI), Mansoury, M. (CoI), van Hoof, H. (CoI), Pal, V. (CoI), Pijnenburg, T. (CoI), Mitra, P. (CoI), Bey, T. (CoI) & de Waard, A. (CoPI)
10/1/19 → 03/31/25
Project: Research

Cite this

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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.",

keywords = "Knowledge graphs, embeddings, graph algorithms, graph databases, graph neural networks, graph query languages, ontologies, rule mining, shapes",

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

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doi = "10.1145/3447772",

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TY - JOUR

T1 - Knowledge Graphs

AU - Hogan, Aidan

AU - Blomqvist, Eva

AU - Cochez, Michael

AU - D’amato, Claudia

AU - Melo, Gerard De

AU - Gutierrez, Claudio

AU - Kirrane, Sabrina

AU - Gayo, José Emilio Labra

AU - Navigli, Roberto

AU - Neumaier, Sebastian

AU - Ngomo, Axel-Cyrille Ngonga

AU - Polleres, Axel

AU - Rashid, Sabbir M.

AU - Rula, Anisa

AU - Schmelzeisen, Lukas

AU - Sequeda, Juan

AU - Staab, Steffen

AU - Zimmermann, Antoine

N1 - Place: New York, NY, USA Publisher: Association for Computing Machinery

PY - 2021/7/1

Y1 - 2021/7/1

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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.

KW - Knowledge graphs

KW - embeddings

KW - graph algorithms

KW - graph databases

KW - graph neural networks

KW - graph query languages

KW - ontologies

KW - rule mining

KW - shapes

U2 - 10.1145/3447772

DO - 10.1145/3447772

M3 - Article

SN - 0360-0300

VL - 54

JO - ACM Computing Surveys

JF - ACM Computing Surveys

IS - 4

ER -

Knowledge Graphs

Abstract

Keywords

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DL: ICAI Discovery Lab

Cite this