Engineered nanoparticles (ENPs) have gained huge commercial interest because of their unique and size-related physicochemical properties. The diversity and complexity of ENPs is increasing with the introduction of next generation nanoparticles. The current approaches are not able to assess the safety of all types and applications of ENPs. Therefore, we are developing a decision support system (DSS) that helps to identify those ENPs and applications that should get priority in the risk assessment. This DSS smartly uses existing knowledge in publicly available databases. With the aid of vocabularies, knowledge rules and logic reasoning new knowledge will be derived. In this paper, the procedure for a DSS is described. Since this system is open by design, others can re-use and extend the DSS content, and newly developed DSS tools can be easily accommodated, which will make the DSS more effective over the years. Data of newly emerging studies will be used for the validation of the DSS. The results will benefit regulating authorities and scientists focussing on the development of inherently safe ENPs.