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.

中文翻译：

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选项：优化算法基准化本体

基准优化算法的许多平台为用户提供了共享实验数据的可能性，目的是促进可重复和可重复使用的研究。但是，不同的平台使用不同的数据模型和格式，这极大地抑制了相关数据集的标识，其解释以及它们的互操作性。因此，非常需要语义丰富，基于本体的机器可读数据模型。我们在本文中报告了这种本体的发展，我们将其命名为OPTION（标定本体的OPTImization算法）。我们的本体提供了对基准测试过程中涉及的核心实体（例如算法，问题和评估措施）进行语义注释所需的词汇。它还提供了自动数据集成的方法，改进的互操作性，强大的查询功能和推理功能，从而丰富了基准数据的价值。我们通过注释和查询BBOB研讨会数据中的基准性能数据集来演示OPTION的实用性-该用例可以轻松扩展为涵盖其他基准数据收集。