From past decade, the advancement in the field of RDF data management poses many challenges to researchers. Processing large volumes of RDF data is very difficult task in the cloud. The RDF data actually contains complex graphs along with large number of schemas. Distributing the RDF data with traditional approaches or partitioning them with conventional mechanism leads to faulty distribution as well as generated large number of join operations. To address the above issues, this paper developed architecture for distributed query processing using the adaptive hash partitioning approach along with hash join operation. This paper also developed an algorithm for executing the query by minimizing the joins. This paper presented an evaluation of the proposed model with other standard model. The experimental results proved that the proposed method had faster response time compared to the other standard models. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.