  Graphs are a natural and fundamental representation of describing the
activities, relationships, and evolution of various complex systems. Many
domains such as communication, citation, procurement, biology, social media,
and transportation can be modeled as a set of entities and their relationships.
Resource Description Framework (RDF) and Labeled Property Graph (LPG) are two
of the most used data models to encode information in a graph. Both models are
similar in terms of using basic graph elements such as nodes and edges but
differ in terms of modeling approach, expressibility, serialization, and target
applications. RDF is a flexible data exchange model for expressing information
about entities but it tends to a have high memory footprint and inefficient
storage, which does not make it a natural choice to perform scalable graph
analytics. In contrast, LPG has gained traction as a reliable model in
performing scalable graph analytic tasks such as sub-graph matching, network
alignment, and real-time knowledge graph query. It provides efficient storage,
fast traversal, and flexibility to model various real-world domains. At the
same time, the LPGs lack the support of a formal knowledge representation such
as an ontology to provide automated knowledge inference. We propose Semantic
Property Graph (SPG) as a logical projection of reified RDF into LPG model. SPG
continues to use RDF ontology to define type hierarchy of the projected graph
and validate it against a given ontology. We present a framework to convert
reified RDF graphs into SPG using two different computing environments. We also
present cloud-based graph migration capabilities using Amazon Web Services.
