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Property graphs and knowledge graphs are two dominant paradigms for graph-based data modeling, yet they remain structurally and semantically incompatible. Property graphs support richly attributed relationships but lack formal semantics and reasoning. Knowledge graphs, grounded in RDF, RDFS, and OWL, enable inference and interoperability but cannot natively represent edge metadata. This paper introduces a categorical framework that bridges this gap through a functorial transformation from typed, attributed property graphs to RDF-star knowledge graphs enriched with OWL/RDFS semantics. The transformation preserves edge attributes using quoted triples, consolidates repeated relationships, and ensures semantic fidelity through alignment functions. This formalization provides a foundation for integrating the structural expressiveness of property graphs with the logical rigor of knowledge graphs, paving the way for interoperable, ontology-aware graph systems that combine flexible data modeling with semantic reasoning.