Three methods for turning rdf 1.2 graphs into property graphs
Translating RDF 1.2 Graphs to Property Graphs
Databases
Summary
RDF 1.2 is a way to describe information using triples, but new features make it tricky to convert this data into another format called property graphs, which label connections with properties. The authors offer and compare three methods to do this conversion, each handling these new features differently. One method keeps everything with extra nodes, another recovers special edges, and the last one combines details into fewer edges but loses some information. They tested these methods to see how well they work and what trade-offs happen in terms of speed and detail.
What this means in practice
- •For graph database engineers: Choose the best rdf 1.2 to property graph translation method suited for specific data complexity and query needs in scalable graph databases.
- •For data integration teams: Convert rdf 1.2 datasets with statement-level annotations into property graphs to unify diverse semantic data sources for analysis.
Authors
Maxime Jakubowski, Dominik Tomaszuk, Daniel Fernández-Álvarez, Ruben Taelman, Ruben Dedecker, Jose-Emilio Labra-Gayo, Katja Hose
Abstract
RDF 1.2 introduces statement-level constructs, such as triple terms, reifiers, and descriptions, that pose new challenges for transforming RDF data into labeled property graphs (LPGs). This work presents and compares three algorithms for translating RDF 1.2 data into LPGs, each interpreting a different amount of the RDF 1.2 reification vocabulary. Algorithm Structural interprets no vocabulary and losslessly supports arbitrary RDF 1.2 graphs by representing triple terms explicitly as nodes; Direct interprets rdf:reifies to recover annotated edges while preserving reifiers as graph objects; and Fold additionally folds reifier descriptions into edge properties, resulting in a more concise, albeit lossy, LPG representation. We define the three translations, analyze which class of RDF 1.2 graphs each preserves, and experimentally evaluate their implementations. The experiments confirm the predicted trade-offs: all variants scale linearly on the tested workloads, the generality of Structural has no runtime penalty, and folding descriptions into edge records can significantly reduce the number of edges on description-heavy data.