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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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Aidan Hogan
Eva Blomqvist
Michael Cochez
ACM Computing Surveys
Rutgers, The State University of New Jersey
University of Southampton
University of Bonn
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Hogan et al. (Fri,) studied this question.
www.synapsesocial.com/papers/69d8cc5fa5ecc596b5d18838 — DOI: https://doi.org/10.1145/3447772