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Knowledge graphs

Authors :
Hogan, A
Blomqvist, E
Cochez, M
D'Amato, C
Melo, G
Gutierrez, C
Kirrane, S
Gayo, J
Navigli, R
Neumaier, S
Ngomo, A
Polleres, A
Rashid, S
Rula, A
Schmelzeisen, L
Sequeda, J
Staab, S
Zimmermann, A
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.
Hogan, A
Blomqvist, E
Cochez, M
D'Amato, C
Melo, G
Gutierrez, C
Kirrane, S
Gayo, J
Navigli, R
Neumaier, S
Ngomo, A
Polleres, A
Rashid, S
Rula, A
Schmelzeisen, L
Sequeda, J
Staab, S
Zimmermann, A
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.
Publication Year :
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.

Details

Database :
OAIster
Notes :
STAMPA, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1354534653
Document Type :
Electronic Resource