RDF and property graph models have many similarities, such as using basic graph concepts like nodes and edges. However, such models differ in their modeling approach, expressivity, serialization, and the nature of applications. RDF is the de-facto standard model for knowledge graphs on the Semantic Web and supported by a rich ecosystem for inference and processing. The property graph model, in contrast, provides advantages in scalable graph analytical tasks, such as graph matching, path analysis, and graph traversal. RDF-star extends RDF and allows capturing metadata as a first-class citizen. To tap on the advantages of alternative models, the literature proposes different ways of transforming knowledge graphs between property graphs and RDF. However, most of these approaches cannot provide complete transformations for RDF-star graphs. Hence, this paper provides a step towards transforming RDF-star graphs into property graphs. In particular, we identify different cases to evaluate transformation approaches from RDF-star to property graphs. Specifically, we categorize two classes of transformation approaches and analyze them based on the test cases. The obtained insights will form the foundation for building complete transformation approaches in the future.
We analyzed existing approaches for mapping RDF-star to property graphs and identified several challenges that we highlighted with a number of test cases. These cases are used to evaluate the transformation approaches developed to transform RDF and RDF-star graphs into property graphs. For completeness, we also list a few simple cases we can iteratively build upon.
This table presents the transformation results from RDF-star graphs into property graphs. The input for each case is an RDF set of triples (Triples Column). The output corresponds to the transformation by the three evaluated approaches: Neosemantics, RDF-Star Tools, and RDF2PG.