  In order to effectively manage the overwhelming influx of data, it is crucial
to ensure that data is findable, accessible, interoperable, and reusable
(FAIR). While ontologies and knowledge graphs have been employed to enhance
FAIRness, challenges remain regarding semantic and cognitive interoperability.
We explore how English facilitates reliable communication of terms and
statements, and transfer our findings to a framework of ontologies and
knowledge graphs, while treating terms and statements as minimal information
units. We categorize statement types based on their predicates, recognizing the
limitations of modeling non-binary predicates with multiple triples, which
negatively impacts interoperability. Terms are associated with different frames
of reference, and different operations require different schemata. Term
mappings and schema crosswalks are therefore vital for semantic
interoperability. We propose a machine-actionable Rosetta Stone Framework for
(meta)data, which uses reference terms and schemata as an interlingua to
minimize mappings and crosswalks. Modeling statements rather than a
human-independent reality ensures cognitive familiarity and thus better
interoperability of data structures. We extend this Rosetta modeling paradigm
to reference schemata, resulting in simple schemata with a consistent structure
across statement types, empowering domain experts to create their own schemata
using the Rosetta Editor, without requiring knowledge of semantics. The Editor
also allows specifying textual and graphical display templates for each schema,
delivering human-readable data representations alongside machine-actionable
data structures. The Rosetta Query Builder derives queries based on completed
input forms and the information from corresponding reference schemata. This
work sets the conceptual ground for the Rosetta Stone Framework that we plan to
develop in the future.
