Rosetta Statements: simplifying FAIR knowledge graph construction with a user-centred approach

Abstract Knowledge graphs and ontologies are promising technologies for achieving FAIR (findable, accessible, interoperable, and reusable) data. We identify four challenges as high barriers for the effective use of knowledge graphs. Since the construction of knowledge graphs is a modelling task and every model serves a purpose against which it is optimized, we question the central paradigm of modelling a mind-independent reality. Instead, we propose the Rosetta Statement approach, which models English natural language statements and displays them as natural language sentences in its user interface. We suggest a Resource Description Framework (RDF)-native metamodel, from which semantic data schemata can be derived for any type of simple English statement. We provide a light and a full version, with the latter supporting versioning and a change-track. We implemented the full version in the Open Research Knowledge Graph (ORKG), an open, domain-agnostic, community-driven knowledge graph for documenting research findings from scholarly publications. The ORKG allows domain experts, with short training but without formal expertise in semantics, to define RDF schemata for new types of Rosetta Statements. We discuss how the Rosetta Statement approach contributes to addressing the four challenges and how its structural proximity to natural language supports the development of tools for data entry and summarization using Large Language Models. We further discuss how the Rosetta approach supports a three-step procedure for FAIR knowledge graph construction: (1) domain experts using Rosetta Statements and Wikidata terms to create a FAIR knowledge graph with basic search functionality; (2) the addition of semantic search capability by replacing Wikidata terms with ontology terms; and (3) the transformation of selected Rosetta Statement types into reasoning-capable graphs with support from ontology engineers. We argue that this three-step procedure is designed to substantially lower the entry barrier for knowledge graph construction while increasing their cognitive interoperability, consistent with the CLEAR Principle.