Realizing a Collaborative RDF Benchmark Suite in Practice

TL;DR

RiverBench is a pioneering open, collaborative RDF benchmark suite that enables community-driven updates, contributions, and benchmarking, fostering long-term sustainability and adaptability in RDF system evaluation.

Contribution

This paper introduces RiverBench, the first fully open, community-driven RDF benchmark suite utilizing RDF and Linked Data for collaboration and resource management.

Findings

RiverBench supports community submissions of datasets and benchmarks.

It enables reporting of benchmark results by users.

The suite is designed for long-term, sustainable community engagement.

Abstract

Collaborative mechanisms allow benchmarks to be updated continuously and adjust to the changing requirements and new use cases. This paradigm is employed for example in the field of machine learning, but up until now there were no examples of truly open and collaborative benchmarks for RDF systems. In this demo paper we present the collaboration functionalities of RiverBench, an open, multi-task RDF benchmark suite. Owing to its fully open and community-driven design, RiverBench allows any researcher or practitioner to submit a new dataset or benchmark task, report performed benchmark runs, and edit any resource in the suite. RiverBench's collaboration system is itself based on RDF and Linked Data mechanisms, and every resource in the suite has machine-readable RDF metadata. The showcased functionalities together make up a first-of-a-kind fully open and collaborative RDF benchmark suite. These features are meant to encourage other researchers to contribute to RiverBench, and make it a long-term project sustained by the community.