Knowledge Graphs

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.Funding Agencies|FondecytComision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)CONICYT FONDECYT [1181896]; ANID -Millennium Science Initiative Program [ICN17_002]; Elseviers Discovery Lab; European UnionEuropean Commission [860801, 731601]; Spanish Ministry of Economy and Competitiveness [TIN2017-88877-R]; MOUSSE ERC [726487]; IBM Research AI through the AI Horizons NetworkInternational Business Machines (IBM); German Research Foundation (DFG)German Research Foundation (DFG) [STA 572/18-1]