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., July, 2021. Place: New York, NY, USA Publisher: Association for Computing Machinery![Knowledge Graphs [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 12 downloads 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{hogan_knowledge_2021,
title = {Knowledge {Graphs}},
volume = {54},
issn = {0360-0300},
url = {https://doi-org.vu-nl.idm.oclc.org/10.1145/3447772},
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.},
number = {4},
journal = {ACM Comput. Surv.},
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, José 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},
month = jul,
year = {2021},
note = {Place: New York, NY, USA
Publisher: Association for Computing Machinery},
keywords = {Knowledge graphs, embeddings, graph algorithms, graph databases, graph neural networks, graph query languages, ontologies, rule mining, shapes},
}
Downloads: 12
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