This first chapter introduces the concepts of Knowledge Graphs and Linked Data, and the technical specifications of RDF, and SPARQL as paradigmatic implementations of these concepts. These technologies have gained a lot of traction in recent years in both academia and industry, as shown by large Knowledge Graphs like Wikidata and DBpedia, and those fueling the services of Google, Microsoft, Apple, Facebook, Amazon, Yahoo, and LinkedIn. Writing an exhaustive chapter–which would easily turn into a book, see, e.g., [Hogan et al., 2021]–about all the technologies involved in the construction and access of these Knowledge Graphs is out of the scope of this book. Instead, we focus on a specific view of what Knowledge Graphs are: a data representation paradigm that uses graphs of linked data to represent knowledge through a set of W3C Web standards.1 This is, however, just a pragmatic decision: Knowledge Graphs are not one single, unanimously recognized technology stack. In fact, there are many interesting, alternative ways of implementing Knowledge Graphs outside the space of Linked Data, for example, through property graphs which are becoming increasingly popular in various domains. In order to give a flavor of these alternatives, we also provide a brief introduction to GraphQL at the end of the chapter.
Knowledge graphs of linked data
Book chapter N°1, in "Web Data APIs for Knowledge Graphs", Synthesis Lectures on Data, Semantics, and Knowledge, Springer, 2021, ISBN: 978-3-031-00789-7
© Springer. Personal use of this material is permitted. The definitive version of this paper was published in Book chapter N°1, in "Web Data APIs for Knowledge Graphs", Synthesis Lectures on Data, Semantics, and Knowledge, Springer, 2021, ISBN: 978-3-031-00789-7 and is available at : http://dx.doi.org/10.1007/978-3-031-01917-3_1
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