Artificial intelligence can propose materials, interpret measurements and increasingly coordinate experiments. Yet reliable scientific autonomy requires more than capable models: research systems must preserve what is known, where evidence came from and which actions are justified. We argue that machine-actionable scientific knowledge is essential for trustworthy autonomous research, and that ontologygrounded knowledge graphs are a particularly powerful way of achieving it when scientific meaning must be shared across disciplines, systems and scales. Using The World Avatar (TWA) as a case study, we examine how ontologies, dynamic knowledge graphs and software agents connect models, literature, experiments and laboratory assets, and how ontology-derived tools can constrain large language model (LLM) actions. A reported design-to-synthesis study of a new metal–organic polyhedron demonstrates the integration of previously separate tasks, but not yet an environmental benefit or fully autonomous discovery. A material-passport example further shows how shared semantics can support industrial circularity assessment. We distinguish structural validation from scientific verification, and circularity indicators from demonstrated environmental benefits. The priority is not to create a universal platform but to build evidence-preserving research infrastructure whose scientific and sustainability benefits can be tested.