Rosetta Statements: Simplifying FAIR Knowledge Graph Construction with a User-Centered Approach

TL;DR

The paper introduces Rosetta Statements, a user-centered approach for simplifying FAIR knowledge graph construction by enabling domain experts to model semantic content without deep semantic expertise, using natural language statements and a flexible metamodel.

Contribution

It presents a novel metamodel for creating semantic schema patterns that support versioning, natural language display, and user-friendly knowledge graph construction without requiring prior semantic knowledge.

Findings

Implemented in the Open Research Knowledge Graph (ORKG) as a use case.

Enables domain experts to define data schemas without semantic expertise.

Supports a three-step knowledge graph construction process.

Abstract

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. The Rosetta Statement metamodel supports a three-step knowledge graph construction procedure. Domain experts can model semantic content without support from ontology engineers by using Wikidata, lowering entry barriers and increasing cognitive interoperability. The second level involves mapping Wikidata terms to established ontologies, and the third step developing semantic graph patterns for reasoning, requiring collaboration with ontology engineers.