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Hogan, Aidan; Blomqvist, Eva; Cochez, Michael; D’amato, Claudia; Melo, Gerard De; Gutierrez, Claudio; Kirrane, Sabrina; Gayo, José Emilio Labra; Navigli, Roberto; Neumaier, Sebastian; Ngomo, Axel-Cyrille Ngonga; Polleres, Axel; Rashid, Sabbir M.; Rula, Anisa; Schmelzeisen, Lukas; Sequeda, Juan; Staab, Steffen; Zimmermann, Antoine
ACM computing surveys, 07/2021, Letnik: 54, Številka: 4Journal Article
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.
