MetaConfigurator: AI-Assisted RDF Authoring from JSON Data

2025-12-22 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Expert, long

Summary

MetaConfigurator, an open-source JSON Schema editor, has been extended with an RDF Authoring View to bridge structured JSON data with Semantic Web technologies. This new view enables 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. The workflow features 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. The system was demonstrated using laboratory data from metal-organic framework (MOF) synthesis experiments, converting protocol data from JSON to ontology-based JSON-LD, refining semantic representations, querying relationships, and interactively exploring the resulting knowledge graph. This integration lowers technical barriers to Semantic Web adoption.

Key takeaway

For research scientists or data engineers managing complex structured data, MetaConfigurator offers a unified environment to transition from JSON to semantically rich RDF. You can use its AI-assisted RML mapping and SPARQL query generation to streamline data transformation and analysis, reducing the manual effort typically associated with Semantic Web technologies. Always review AI-generated outputs to ensure accuracy and alignment with your specific ontological requirements. This approach enhances data interoperability and reusability.

Key insights

MetaConfigurator integrates AI-assisted RDF authoring, RML mapping, SPARQL querying, and knowledge graph visualization for JSON data.

Principles

• AI assistance lowers Semantic Web entry barriers.
• Integrated tools streamline complex data transformations.
• Human review is crucial for AI-generated outputs.

Method

Convert JSON to JSON-LD via AI-assisted RML mappings, then inspect/edit triples, query with AI-generated SPARQL, and visualize the knowledge graph.

In practice

• Transform MOF synthesis JSON to RDF.
• Generate SPARQL queries from natural language.
• Visualize experimental data as a knowledge graph.

Topics

• MetaConfigurator
• RDF Authoring
• AI-Assisted Mappings
• Knowledge Graph Visualization
• SPARQL Querying
• Semantic Web Integration

Code references

• MetaConfigurator/meta-configurator

Best for: Research Scientist, AI Scientist, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.