Models, and to a lesser extent datasets, embody sophisticated statements of environmental knowledge. Yet, the knowledge they incorporate is rarely self-contained enough for them to be understood and used - by humans or machines - without the modeller's mediation. This severely limits the options in reusing environmental models and connecting them to datasets or other models. The notion of ''declarative modelling'' has been suggested as a remedy to help design, communicate, share and integrate models. Yet, not all these objectives have been achieved by declarative modelling in its current implementations. Semantically aware environmental modelling is a way of designing, implementing and deploying environmental datasets and models based on the independent, standardized formalization of the underlying environmental science. It can be seen as the result of merging the rationale of declarative modelling with modern knowledge representation theory, through the mediation of the integrative vision of a Semantic Web. In this paper, we review the present and preview the future of semantic modelling in environmental science: from the mediation approach, where formal knowledge is the key to automatic integration of datasets, models and analytical pipelines, to the knowledge-driven approach, where the knowledge is the key not only to integration, but also to overcoming scale and paradigm differences and to novel potentials for model design and automated knowledge discovery.
['Ferdinando Villa', 'Ioannis N. Athanasiadis', 'Andrea Emilio Rizzoli']
Review: Modelling with knowledge: A review of emerging semantic approaches to environmental modelling