Knowledge Graphs can be encoded using different data models. They are especially abundant using RDF and recently also as property graphs. While knowledge graphs in RDF adhere to the subject-predicate-object structure, property graphs utilize multi-labeled nodes and edges, featuring properties as key/value pairs. Both models are employed in various contexts, thus applications often require transforming data from one model to another. To enhance the interoperability of the two models, we present a novel technique, S3PG, to convert RDF knowledge graphs into property graphs exploiting two popular standards to express schema constraints, i.e., SHACL for RDF and PG-Schema for property graphs. S3PG is the first approach capable of transforming large knowledge graphs to property graphs while fully preserving information and semantics. We have evaluated S3PG on real-world large-scale graphs, showing that, while existing methods exhibit lossy transformations (causing a loss of up to 70% of query answers), S3PG consistently achieves 100% accuracy. Moreover, when considering evolving graphs, S3PG exhibits fully monotonic behavior and requires only a fraction of the time to incorporate changes compared to existing methods.