Abstract The objective of this work is to establish a natural language environment that would encourage the deployment of synthesis and optimization models by non-PSE experts, conceptualizing problem knowledge and associating the concepts to data, computational models, and management structures to expand the knowledge domain further. For this purpose, ontologies are deployed to integrate data from different resources, the high-throughput use of mathematical formulations. The approach is illustrated with the synthesis of biochemical paths and the design of biorefineries, a field particularly multi-disciplinary, with data and models of different granularities, texture and origin. The ontologies are applied to automate the selection of synthesis blocks, their integration into superstructures and the translation of data into a knowledge basis. The illustration addresses real-life problems challenged by a maze of options set up in a background of 82 different chemistries, 80 intermediate and an even higher number of end-products.