  Machines need data and metadata to be machine-actionable and FAIR (findable,
accessible, interoperable, reusable) to manage increasing data volumes.
Knowledge graphs and ontologies are key to this, but their use is hampered by
high access barriers due to required prior knowledge in semantics and data
modelling. The Rosetta Statement approach proposes modeling English natural
language statements instead of a mind-independent reality. We propose a
metamodel for creating semantic schema patterns for simple statement types. The
approach supports versioning of statements and provides a detailed editing
history. Each Rosetta Statement pattern has a dynamic label for displaying
statements as natural language sentences. Implemented in the Open Research
Knowledge Graph (ORKG) as a use case, this approach allows domain experts to
define data schema patterns without needing semantic knowledge. Future plans
include combining Rosetta Statements with semantic units to organize ORKG into
meaningful subgraphs, improving usability. A search interface for querying
statements without needing SPARQL or Cypher knowledge is also planned, along
with tools for data entry and display using Large Language Models and NLP. The
Rosetta Statement metamodel supports a two-step knowledge graph construction
procedure. Domain experts can model semantic content without support from
ontology engineers, lowering entry barriers and increasing cognitive
interoperability. The second level involves developing semantic graph patterns
for reasoning, requiring collaboration with ontology engineers.
