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Knowledge Graphs

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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·2022·ACM Computing Surveys

DOI 10.1145/3447772

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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.

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A Survey on Knowledge Graphs: Representation, Acquisition, and Applications
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Authors: Hogan, A., Blomqvist, E., Cochez, M., D’amato, C., Melo, G. D., Gutierrez, C., Kirrane, S., Gayo, J. E. L., Navigli, R., Neumaier, S., Ngomo, A.-C. N., Polleres, A., Rashid, S. M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., & Zimmermann, A.
Venue: ACM Computing Surveys
Publisher: Association for Computing Machinery (ACM)
Published: 2021-07-02
Volume: 54
Issue: 4
Pages: 1-37
ISSN: 0360-0300 (ISSN-L)
1557-7341
DOI: 10.1145/3447772
OpenAlex: W3010336026
Cited by: 1,904
References: 22
FWCI: 71.81
Type: journal article
Language: en
Open access: bronze
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
Funders: Marie Skłodowska-Curie (860801)
ANID – Millennium Science Initiative Program (ICN17_002)
MOUSSE ERC (726487)
Fondecyt (1181896)
German Research Foundation (STA 572/18-1)
European Union’s Horizon 2020 research and innovation programme (731601)
Spanish Ministry of Economy and Competitiveness (TIN2017-88877-R)

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.

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