- Keywords: KG, RML, R2RML, GTFS-Madrid-Bench
- Abstract: Knowledge graphs have proven to be a powerful technology to integrate and structure the myriad of data available nowadays. The semantic web community has actively worked on data integration systems, providing an important set of engines and mapping rule specifications to facilitate the construction of knowledge graphs. Despite these important efforts, there is a lack of objective evaluations of the capabilities of these engines in terms of performance, scalability and conformance with mapping specifications. In this work, we conduct such evaluation considering several R2RML and RML processors with the purpose of identifying their strengths and weaknesses. We (i) perform a qualitative analysis of the distinctive features of each engine, (ii) examine their conformance with the mapping language specification they support, and (iii) assess their performance and scalability using the GTFS-Madrid-Benchmark. The results extracted from this evaluation will help developers to identify facets of the engines susceptible to enhancement, and will allow practitioners to select the most suitable engine for their use cases.