arXiv:2606.07094v1 Announce Type: cross Abstract: Scientific workflows increasingly generate structured JSON data that is easy to exchange but difficult to interpret consistently across systems due to lacking semantic interoperability. While JSON Schema ensures structural validation, it provides no native support for Linked Data semantics. This paper presents an RDF Authoring View extending the open-source JSON Schema editor MetaConfigurator, enabling researchers to transform existing JSON, YAML, or CSV data into RDF using AI-assisted RML mappings, refine triples, execute SPARQL queries, visua
The proliferation of structured JSON data in scientific workflows coupled with the need for better semantic interoperability is driving demand for solutions that can bridge the gap between data format and meaning.
This development addresses a critical challenge in data integration and knowledge representation, enabling more consistent and interpretable data across systems, which is crucial for advanced AI applications and automated reasoning.
Researchers and developers can now more easily transform existing semi-structured data into semantically rich RDF, improving data usability and facilitating the creation of Linked Data-based systems with AI assistance.
- · AI/ML researchers and developers
- · Linked Data practitioners
- · Data scientists
- · Enterprise data integration teams
- · Organizations with siloed, unintelligible data
- · Manual data mapping consultancies that don't adapt
- · Legacy data integration vendors without semantic capabilities
Increased adoption of Linked Data principles and RDF graphs for knowledge representation across various domains.
Improved accuracy and efficiency of knowledge graphs and AI systems relying on integrated, semantically rich data.
Acceleration of automated scientific discovery and 'digital twin' initiatives due to enhanced data interoperability.
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Read at arXiv cs.AI