Knowledge Graphs are increasingly being employed to improve data interoperability, search, and recommendation, alongside fostering the adoption of semantic web technologies. The quality of data within these graphs is pivotal, often validated against expected data models or shapes to enhance accuracy. Various technologies implement knowledge graphs; RDF-based triplestores are canonical in the Semantic Web, while in the graph databases context, Property Graphs are also considered for Knowledge Graphs. Wikidata, a popular Knowledge Graph, offers RDF through its SPARQL query service, but its data model aligns closely with Property Graphs using qualifiers and references, and the recent proposal of RDF-Star can bridge the gap between RDF and Property Graphs. Shape Expressions (ShEx) and Shapes Constraint Language (SHACL) were proposed for RDF validation while in the case of Property Graphs, PGSchema was proposed, as well as other proposals like PShEx or ProGS. Wikidata adopted Entity Schemas, which are based on ShEx as well as its own property constraint system, and there is a proposal called WShEx. This tutorial explores different types of Knowledge Graphs and approaches for their validation. We will also review practical applications like inferring shapes from existing data and creating conforming subsets of Knowledge Graphs.