During the past two centuries, the world has undergone deep societal, political, and economical changes that heavily affected human life. The above changes contributed to an increased awareness about the deep impact that policy decisions have at the local and the global level. Therefore, there is a strong need that policy-making and decision-making processes for a sustainable development be based on the best available knowledge about Earth system and environment. The recent advance of information technologies enables running complex models that use the large amount of Earth Observation datasets available. However, data and model interoperability are still limited to the syntactic level allowing to access and process datasets independently of their structural characteristics (data format, co- ordinate reference systems, service interface, ...) but with no clear reference to their content (the semantic level) and context of use (the pragmatic level). This poses heavy limitations to the reusability of scientific processes and related workflows. The paper presents a general framework to address this issue through the design of a Knowledge Base sup- porting data and model semantic (and pragmatic) interoperability. In this framework, a general ontology rep- resents the knowledge generation process for policy relevant decision-making, while multiple vocabularies formalize the semantics of data and models, identifying different types of observables, process variables, and indicators/indices. To evaluate the proposed approach to semantic interoperability of data and models, the Knowledge Base has been integrated with an advanced model-sharing framework, and a proof-of-concept has been developed for the assessment of one of the indicators of the Sustainable Development Goals defined by the United Nations.