The Property Graph (PG) [Ang18] model is widely used in modern data systems for its intuitive handling of nodes, relationships, and associated properties. However, PG lacks formal semantics and interoperability, particularly with web-native frameworks such as RDF and RDF-star [ABPS+12; KFH22; NYT+19]. While existing systems like Neosemantics [RCK22] and mapping languages such as RML and RML-star [BCML; Con+12b; DAI+21] allow PG-to-RDF transformation, they either rely on user-defined mappings or impose rigid ontology-based encodings [DVC+14; RCK22; TAT20]. To address these limitations, this thesis proposes the Graph Generating Transformer (GGT): a modular, rule-based transformation system that converts PG data into RDF-star using a fixed, reusable set of declarative transformation rules. The core innovation lies in applying Graph Generating Dependencies (GGDs) to define transformation semantics that preserve both the structure and metadata of PGs. Unlike prior approaches, GGT does not require users to define mappings per dataset and avoids schema reification. It supports RDF-star features such as quoted triples for representing edge properties and ensures datatype-annotated output for semantic querying. The system is implemented in Python with an extensible architecture comprising loaders, rule applicators, and RDF-star generators. Evaluation on WatDiv benchmark datasets and a custom evaluation set demonstrates that GGT scales to thousands of triples while maintaining clear semantic output. Runtime and memory consumption are analyzed across data sizes to assess practical viability. This thesis demonstrates that a GGD-based rule set can serve as a robust foundation for interoperable graph transformation to RDF-star, bridging the gap between PG and RDF-Star semantics [SYF24a].