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Related Experiment Video

Updated: Jun 3, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

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

Lars Vogt¹, Kheir Eddine Farfar², Pallavi Karanth²

¹Leibniz Institute for the Analysis of Biodiversity Change (LIB), Museum of Nature Hamburg, Martin-Luther-King Platz 3, 20146 Hamburg, Germany.

Database : the Journal of Biological Databases and Curation

|June 2, 2026

Summary

This summary is machine-generated.

The Rosetta Statement approach simplifies knowledge graph construction by modeling natural language, lowering barriers for domain experts and enhancing data interoperability for FAIR data principles.

Related Experiment Videos

Last Updated: Jun 3, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Computer Science
Information Science
Semantic Web Technologies

Background:

Knowledge graphs and ontologies are key for FAIR data, but face challenges in effective use.
Current modeling paradigms often assume mind-independent reality, posing difficulties for practical application.
Existing methods require significant semantic expertise, limiting broader adoption.

Purpose of the Study:

To propose the Rosetta Statement approach as a solution to challenges in knowledge graph construction.
To introduce an RDF-native metamodel for deriving semantic data schemata from simple English statements.
To demonstrate a practical implementation and a three-step procedure for FAIR knowledge graph creation.

Main Methods:

Developed the Rosetta Statement approach, modeling natural language statements directly.
Designed an RDF-native metamodel supporting versioning and change-tracking.
Implemented the full version in the Open Research Knowledge Graph (ORKG) platform.
Outlined a three-step FAIR knowledge graph construction procedure.

Main Results:

The Rosetta Statement approach facilitates knowledge graph construction by domain experts with minimal semantic training.
The ORKG implementation allows for community-driven definition of schemata for new statement types.
The proposed three-step procedure effectively lowers entry barriers and enhances cognitive interoperability for FAIR knowledge graphs.

Conclusions:

The Rosetta Statement approach significantly reduces the complexity of knowledge graph creation.
Its natural language proximity aids tool development for data entry and summarization, especially with Large Language Models.
This approach promotes FAIR data principles and cognitive interoperability, aligning with the CLEAR Principle.