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Knowledge Graphs

ACM Computing Surveys · 2021 · Vol. 54(4) · pp. 1–37

Aidan Hogan✉(University of Chile)Eva Blomqvist(Linköping University)Michael Cochez(RELX Group (Netherlands))Claudia d’Amato(University of Bari Aldo Moro)Gerard de Melo(Rutgers, The State University of New Jersey)Claudio Gutiérrez(University of Chile)Sabrina Kirrane(Vienna University of Economics and Business)José Emilio Labra Gayo(Universidad de Oviedo)Roberto Navigli(Sapienza University of Rome)Sebastian Neumaier(Vienna University of Economics and Business)Axel-Cyrille Ngonga Ngomo(Paderborn University)Axel Polleres(Vienna University of Economics and Business)Sabbir M. Rashid(Rensselaer Polytechnic Institute)Anisa Rula(University of Bonn)Lukas Schmelzeisen(University of Stuttgart)Juan SequedaSteffen Staab(University of Stuttgart)Antoine Zimmermann(Mines Saint-Étienne)

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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.

Advanced Graph Neural Networks Data Quality and Management Machine Learning and AlgorithmsComputer scienceKnowledge graphArtificial intelligence