OPTION: OPTImization Algorithm Benchmarking ONtology

Abstract

Many optimization algorithm benchmarking platforms allow users to share their experimental data to promote reproducible and reusable research. However, different platforms use different data models and formats, which drastically complicates the identification of relevant datasets, their interpretation, and their interoperability. Therefore, a semantically rich, ontology-based, machine-readable data model that can be used by different platforms is highly desirable. In this paper, we report on the development of such an ontology, which we call 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 automatic data integration, improved interoperability, and powerful querying capabilities, thereby increasing the value of the benchmarking data. We demonstrate the utility of OPTION, by annotating and querying a corpus of benchmark performance data from the BBOB collection of the COCO framework and from the Yet Another Black-Box Optimization Benchmark (YABBOB) family of the Nevergrad environment. In addition, we integrate features of the BBOB functional performance landscape into the OPTION knowledge base using publicly available datasets with exploratory landscape analysis. Finally, we integrate the OPTION knowledge base into the IOHprofiler environment and provide users with the ability to perform meta-analysis of performance data.

Using Knowledge Graphs for Performance Prediction of Modular Optimization Algorithms

“…Finally, each ELA feature is discretized into 10 bins using the uniform binning strategy. These data are available through the OPTION ontology [9] knowledge base and we used their API to extract them. The data described below were generated as part of this study and matched with data extracted from OPTION to create the KGs.…”

“…For example, searching for algorithms that can solve problems from a particular class, and searching for algorithms that have been applied to a specific engineering problem. The OPTimization Algorithm Benchmarking ONtology (OPTION) [9] formalizes knowledge about benchmarking optimization algorithms, focusing on the formal representation of data from the performance and problem landscape space, but currently lacks descriptors for optimization algorithms.…”

”cityjson2rdf” a Converter for Producing 3d City Knowledge Graphs

“…RDFs depend on the creation of triples in the form of (subject, predicate, object), where subjects and objects denote entities, and predicates establish relationships between entities (Zhang et al, 2021;Kostovska et al, 2022). RDF Schema (RDFS) extends the RDF data model and provides essential elements for describing ontologies, including classes and properties, thereby facilitating the organization and representation of knowledge in a structured manner.…”