Knowledge Graphs
It serves as a tutorial for researchers and practitioners needing to exploit diverse, dynamic, large-scale data, but is incremental as it summarizes existing knowledge without novel contributions.
This paper provides a comprehensive introduction to knowledge graphs, covering their data models, query languages, representation techniques, creation methods, and applications, without presenting new experimental results or specific problems.
In this paper 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 and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs.