  Many platforms for benchmarking optimization algorithms offer users the
possibility of sharing their experimental data with the purpose of promoting
reproducible and reusable research. However, different platforms use different
data models and formats, which drastically inhibits identification of relevant
data sets, their interpretation, and their interoperability. Consequently, a
semantically rich, ontology-based, machine-readable data model is highly
desired.
  We report in this paper on the development of such an ontology, which we name
OPTION (OPTImization algorithm benchmarking ONtology). Our ontology provides
the vocabulary needed for semantic annotation of the core entities involved in
the benchmarking process, such as algorithms, problems, and evaluation
measures. It also provides means for automated data integration, improved
interoperability, powerful querying capabilities and reasoning, thereby
enriching the value of the benchmark data. We demonstrate the utility of OPTION
by annotating and querying a corpus of benchmark performance data from the BBOB
workshop data - a use case which can be easily extended to cover other
benchmarking data collections.
