Over the last two decades, the amount of data which has been created, published and managed using Semantic Web standards and especially via Resource Description Framework (RDF) has been increasing.As a result, efficient processing of such big RDF datasets has become challenging.Indeed, these processes require, both efficient storage strategies and query-processing engines, to be able to scale in terms of data size.In this study, we propose a scalable approach to evaluate SPARQL queries over distributed RDF datasets using a semantic-based partition and is implemented inside the state-of-the-art RDF processing framework: SANSA.An evaluation of the performance of our approach in processing large-scale RDF datasets is also presented.The preliminary results of the conducted experiments show that our approach can scale horizontally and perform well as compared with the previous Hadoop-based system.It is also comparable with the in-memory SPARQL query evaluators when there is less shuffling involved.