BenchEmbedd: A FAIR benchmarking tool for knowledge graph embeddings

Knowledge graph embedding models have been studied comprehensively recently. However, these studies lack an evaluation system that compares their efficiency in a reproducible manner that follows the FAIR principles. In this study, we extend the general HOBBIT benchmarking platform to evaluate the efficiency of embedding models with such criteria. The demo benchmark, source code of this study, and installation and usage guide are openly available in https://github.com/mlwinde/BenchEmbed. In this paper, we explain the structure of this Benchmarking tool and demonstrate the usage of the benchmarking system for the knowledge graph embedding models.