Science frequently works by people making connections between related (or even apparently unrelated) concepts or data. There are many ways of helping people make these connections – attending a conference or seminar, searching journals for published articles and nowadays also searching for data are just a few examples.
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.