For a long time, there were only two main types of graph databases — Resource Description Framework (RDF) and Labeled Property Graphs (LPGs). As these graphs excelled at different things, users had to choose one based on their main use case and hope that their requirements do not change too much. Today, with the emergence of a third graph type RDF* (or RDF star) that combines the best of these approaches; it is no longer necessary to choose between RDF and LPGs.
RDF* extends RDF’s benefits to include the level of detail offered by LPGs effortlessly. RDF* is a knowledge graph triple store that leverages properties. Consequently, adding properties to triples for any purpose in RDF* is considerably simpler than adding properties to RDFs. This practically unlimited expressivity is valuable for data provenance as it allows the tracking of the data sources and identifying the confidence levels in them for a more complete provenance.
The biggest data science obstacle for machine learning, training data requirements, can be overcome by utilizing RDF*’s inherent flexibility. Its malleability facilitates assembling of even disparate data sources in the RDF* standard to maximize training data for machine learning and artificial intelligence (AI). Further, adding properties to semantic statements quickly can enhance the usefulness of training datasets.
In brief, today, graph users, along with LPGs and RDF, have RDF*. The RDF*standard merges the best LPG qualities into an RDF environment thereby enabling the addition of properties to knowledge graphs to augment their intelligent inferences and standards-based settings with colorful expressivity. Among the numerous downstream advantages of using RDF* include revamped data lineage, data science and analytics capabilities. These capabilities make RDF* the ultimate playground for implementing data management staples and ensuring profound improvement in the ability to achieve organizational goals with data.
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