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.

Knowledge graphs / 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.. - In: ACM COMPUTING SURVEYS. - ISSN 0360-0300. - 54:4(2021), pp. 1-37. [10.1145/3447772]

Knowledge graphs

D'Amato C.;Navigli R.
;
2021

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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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1589077
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