Since Google introduced the use of Knowledge Graphs to enhance search functionality and organize information internally, their adoption and application have grown significantly. Various technologies have been developed to implement Knowledge Graphs, with RDF-based triplestores being a cornerstone of the Semantic Web, while Property Graphs are also widely used in the context of graph databases. Wikidata, a well-known Knowledge Graph, provides RDF data through its SPARQL query service, but its data model closely resembles Property Graphs, incorporating features like qualifiers and references. The recent introduction of RDF 1.2 (formerly known as RDF-Star) aims to bridge the gap between RDF and Property Graphs by enabling statements about statements, offering greater flexibility. Data quality is a critical aspect of Knowledge Graphs, often ensured through validation against predefined data models or shapes. This tutorial will explore several approaches developed for describing and validating RDF, such as Shape Expressions (ShEx) and Shapes Constraint Language (SHACL). Notably, the Data Shapes Working Group has been tasked this year with developing SHACL 1.2, aligning it with RDF 1.2. We will briefly outline these approaches, highlighting their similarities, differences, and recent advancements. For Property Graphs, proposals like PGSchema, PShEx, and ProGS have emerged, with GQL recently offering a way to define typed graphs. Wikidata has adopted Entity Schemas, which are based on ShEx, alongside its own property constraint system, and a proposal called WShEx is also under consideration. This tutorial will delve into the various types of Knowledge Graphs and the methods used for their validation. Additionally, we will examine practical applications of these technologies, such as inferring shapes from existing data and generating compliant subsets of Knowledge Graphs.