Quality management of requirements has seen a dramatic increase of the amount of applications, management platforms, data, etc. gaining momentum in the Systems Engineering area and more specifically in the deployment of the next wave of critical system. In this context, one of the next big things lies in the creation of quality functions that can automatically detect and make decisions according to natural‐language based requirements specifications and models. In this sense quality indicator of requirements seeks for providing an intelligent environment for detecting values of such as correctness, consistency and completeness based on domain knowledge in which both functional and nonfunctional properties of system components can be validated and verified easing the transition to a smart system environment. Thus the testing of critical systems based on requirements quality can be seen as a special kind of policy‐making strategy that must compile several key indicators to summarize data and information and obtain an objective quantitative measure. Nevertheless the quantitative analysis of several quality indicators is becoming a challenging task due to natural language ambiguities and a tangled/heterogeneous environment of data, providers, etc. Existing tools and techniques based on traditional processes of quality assessment are preventing a proper use of the new dynamic and data environment avoiding more timely, adaptable and flexible (on‐demand) quantitative index creation and, as a consequence, more accurate decisions. On the other hand, semantic‐based technologies emerge to provide the adequate building blocks to represent domain‐knowledge and process data in a flexible fashion using a common and shared data model. That is why the present paper introduces a Resource Description Framework (RDF) vocabulary to semantically represent and compute quantitative indexes as part of the implementation of the Open Services for Lifecycle Collaboration initiative (OSLC) Quality Management specification. Finally some discussion, conclusions and future work are also outlined.