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In order to effectively manage the overwhelming influx of data, it is crucialto ensure that data is findable, accessible, interoperable, and reusable(FAIR). While ontologies and knowledge graphs have been employed to enhanceFAIRness, challenges remain regarding semantic and cognitive interoperability.We explore how English facilitates reliable communication of terms andstatements, and transfer our findings to a framework of ontologies andknowledge graphs, while treating terms and statements as minimal informationunits. We categorize statement types based on their predicates, recognizing thelimitations of modeling non-binary predicates with multiple triples, whichnegatively impacts interoperability. Terms are associated with different framesof reference, and different operations require different schemata. Termmappings and schema crosswalks are therefore vital for semanticinteroperability. We propose a machine-actionable Rosetta Stone Framework for(meta)data, which uses reference terms and schemata as an interlingua tominimize mappings and crosswalks. Modeling statements rather than ahuman-independent reality ensures cognitive familiarity and thus betterinteroperability of data structures. We extend this Rosetta modeling paradigmto reference schemata, resulting in simple schemata with a consistent structureacross statement types, empowering domain experts to create their own schematausing the Rosetta Editor, without requiring knowledge of semantics. The Editoralso allows specifying textual and graphical display templates for each schema,delivering human-readable data representations alongside machine-actionabledata structures. The Rosetta Query Builder derives queries based on completedinput forms and the information from corresponding reference schemata. Thiswork sets the conceptual ground for the Rosetta Stone Framework that we plan todevelop in the future.

Towards a Rosetta Stone for (meta)data: Learning from natural language to improve semantic and cognitive interoperability