@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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M.","Rula, A.","Schmelzeisen, L.","Sequeda, J.","Staab, S.","Zimmermann, A."],"bibdata":{"bibtype":"article","type":"article","author":[{"propositions":[],"lastnames":["Hogan"],"firstnames":["Aidan"],"suffixes":[]},{"propositions":[],"lastnames":["Blomqvist"],"firstnames":["Eva"],"suffixes":[]},{"propositions":[],"lastnames":["Cochez"],"firstnames":["Michael"],"suffixes":[]},{"propositions":[],"lastnames":["D’amato"],"firstnames":["Claudia"],"suffixes":[]},{"propositions":[],"lastnames":["Melo"],"firstnames":["Gerard","De"],"suffixes":[]},{"propositions":[],"lastnames":["Gutierrez"],"firstnames":["Claudio"],"suffixes":[]},{"propositions":[],"lastnames":["Kirrane"],"firstnames":["Sabrina"],"suffixes":[]},{"propositions":[],"lastnames":["Gayo"],"firstnames":["José","Emilio","Labra"],"suffixes":[]},{"propositions":[],"lastnames":["Navigli"],"firstnames":["Roberto"],"suffixes":[]},{"propositions":[],"lastnames":["Neumaier"],"firstnames":["Sebastian"],"suffixes":[]},{"propositions":[],"lastnames":["Ngomo"],"firstnames":["Axel-Cyrille","Ngonga"],"suffixes":[]},{"propositions":[],"lastnames":["Polleres"],"firstnames":["Axel"],"suffixes":[]},{"propositions":[],"lastnames":["Rashid"],"firstnames":["Sabbir","M."],"suffixes":[]},{"propositions":[],"lastnames":["Rula"],"firstnames":["Anisa"],"suffixes":[]},{"propositions":[],"lastnames":["Schmelzeisen"],"firstnames":["Lukas"],"suffixes":[]},{"propositions":[],"lastnames":["Sequeda"],"firstnames":["Juan"],"suffixes":[]},{"propositions":[],"lastnames":["Staab"],"firstnames":["Steffen"],"suffixes":[]},{"propositions":[],"lastnames":["Zimmermann"],"firstnames":["Antoine"],"suffixes":[]}],"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. 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