A design space for RDF data representations

Abstract

RDF triplestores’ ability to store and query knowledge bases augmented with semantic annotations has attracted the attention of both research and industry. A multitude of systems offer varying data representation and indexing schemes. However, as recently shown for designing data structures, many design choices are biased by outdated considerations and may not result in the most efficient data representation for a given query workload. To overcome this limitation, we identify a novel three-dimensional design space. Within this design space, we map the trade-offs between different RDF data representations employed as part of an RDF triplestore and identify unexplored solutions. We complement the review with an empirical evaluation of ten standard SPARQL benchmarks to examine the prevalence of these access patterns in synthetic and real query workloads. We find some access patterns, to be both prevalent in the workloads and under-supported by existing triplestores. This shows the capabilities of our model to be used by RDF store designers to reason about different design choices and allow a (possibly artificially intelligent) designer to evaluate the fit between a given system design and a query workload.

Transforming RDF-star to Property Graphs: A Preliminary Analysis of Transformation Approaches -- extended version

“…In our future work, we plan to expand our experiments to comprehensive datasets and combining RPT and PGT into a single hybrid transformation approach. Furthermore, based on our previous work, we plan to investigate how processing advanced queries over provenance-enhanced datasets can be improved [12][13][14]24], how querying heterogeneous provenance-enhanced graph data can be embedded in scalable ecosystems [29], how evolving knowledge graphs can be supported [20], and how interoperability between RDF-star and property graphs can be achieved [28].…”