Knowledge Graphs

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 contras...

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Published inACM computing surveys Vol. 54; no. 4; pp. 1 - 37
Main Authors Hogan, Aidan, Blomqvist, Eva, Cochez, Michael, D’amato, Claudia, Melo, Gerard De, Gutierrez, Claudio, Kirrane, Sabrina, Gayo, José Emilio Labra, Navigli, Roberto, Neumaier, Sebastian, Ngomo, Axel-Cyrille Ngonga, Polleres, Axel, Rashid, Sabbir M., Rula, Anisa, Schmelzeisen, Lukas, Sequeda, Juan, Staab, Steffen, Zimmermann, Antoine
Format Journal Article
LanguageEnglish
Published Baltimore Association for Computing Machinery 01.07.2021
Subjects
Computer Science
Graphical representations
Graphs
Knowledge representation
Modeling and Simulation
embeddings
rule mining
shapes
Knowledge graphs
graph query languages
graph neural networks
ontologies
graph databases
graph algorithms
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Summary: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.
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ISSN:0360-0300
1557-7341
1557-7341
DOI:10.1145/3447772