🤖 AI Summary
This work addresses the lack of semantic interoperability in structured data (e.g., JSON, YAML, CSV) within scientific workflows, which hinders consistent interpretation. The authors introduce an RDF authoring view in the MetaConfigurator editor that leverages AI-assisted generation of RML mappings to automatically transform structured data into RDF. The system supports triple editing, SPARQL querying, and knowledge graph visualization. Key innovations include the first integration of large language models for natural language-to-SPARQL translation, bidirectional synchronization between JSON-LD and RDF triples, and ontology-aware IRI auto-completion. Demonstrated on MOF synthesis experiments, the approach successfully converts JSON protocols into semantic knowledge graphs, enabling interactive exploration of relationships between experimental conditions and outcomes, thereby significantly lowering the barrier to adopting Semantic Web technologies.
📝 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, visualize knowledge graphs, and export RDF serializations within a single integrated web interface. This workflow is supported by ontology-aware IRI auto-completion, bidirectional synchronization between JSON-LD text views and RDF triple tables, and AI-assisted SPARQL query generation from natural language hints.
We demonstrate the workflow using laboratory data from metal-organic framework (MOF) synthesis experiments. Protocol data describing reagents, procedure steps, and quantities is converted from JSON to ontology-based JSON-LD via RML mappings. We then refine the semantic representation, query relationships between experimental conditions and outcomes, and explore the resulting knowledge graph interactively. This integrated environment bridges conventional structured data management with Semantic Web technologies while preserving experimental context and lowering technical barriers through AI assistance.