Short Summary: Researchers and business have and are meticulously transferring structured data about specific domains into domain specific semantic graphs using domain specific ontologies. The standard is RDF. With this standard we are capable to extract more meaningful relations on such graphs. But structural and in-graph analysis (ML/AI) remains tedious and resource intensive. Property graphs enable us to do such analysis. But we cannot simply load domain specific data structures into it. Instead we have to model the graph schema appropriately. Our work is focused on bridging this gap and using the advantages of both graph tools by integrating existing RDF stores from any domain to build the right subgraphs for analysis in a property graph. This kind of scientific graph analysis allows us to understand the dynamics of complex systems such as customer relations, traffic conditions, or brain activity. The RDF store can serve with its unified data integration model and has the capability of semantic reasoning. TigerGraph as scalable graph database and analytics platform provides the capability for large scale graph processing on multi-layer networks. Our approach enables researchers and businesses to integrate data from various sources into a scalable managed property graph.

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