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.
@article{arxiv.2410.12965,
title = {Realizing a Collaborative RDF Benchmark Suite in Practice},
author = {Piotr Sowinski and Maria Ganzha},
journal= {arXiv preprint arXiv:2410.12965},
year = {2024}
}
Comments
Accepted at 24th International Conference on Knowledge Engineering and Knowledge Management (EKAW 2024) as a demo paper