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. N., Polleres, A., Rashid, S. M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., & Zimmermann, A. ACM Comput. Surv., Association for Computing Machinery, New York, NY, USA, July, 2021.
Knowledge Graphs [link]Paper  doi  abstract   bibtex   
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.
@article{hogan2021knowledge,
author = {Hogan, Aidan and Blomqvist, Eva and Cochez, Michael and D’amato, Claudia and Melo, Gerard De and Gutierrez, Claudio and Kirrane, Sabrina and Gayo, Jos\'{e} Emilio Labra and Navigli, Roberto and Neumaier, Sebastian and Ngomo, Axel-Cyrille Ngonga and Polleres, Axel and Rashid, Sabbir M. and Rula, Anisa and Schmelzeisen, Lukas and Sequeda, Juan and Staab, Steffen and Zimmermann, Antoine},
title = {Knowledge Graphs},
year = {2021},
issue_date = {July 2021},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {54},
number = {4},
issn = {0360-0300},
url = {https://arxiv.org/abs/2003.02320},
doi = {10.1145/3447772},
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.},
journal = {ACM Comput. Surv.},
month = jul,
articleno = {71},
numpages = {37},
keywords = {ontologies, shapes, graph query languages, rule mining, graph algorithms, embeddings, graph neural networks, graph databases, Knowledge graphs}
}

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