Orchestrating Black-Box Schema Converters: An Empirical Study of Automated, Quality-Ranked Conversion Across Heterogeneous Schema Languages

2026-06-26 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

The Schema Conversion Orchestrator addresses the challenge of maintaining consistent data models across various schema languages like JSON Schema, XSD, and SHACL, where existing converters are often scattered, inconsistent, and lossy. This open-source tool models schema languages as nodes and converters as black-box directed edges, enabling automated discovery, execution, and quality-ranked conversion paths. Implemented as a Python service and integrated into MetaConfigurator, the orchestrator was empirically evaluated on 60 conversion tasks using real-world schemas across five languages. It successfully surfaced a usable result for 43 of these tasks (31 good, 12 lacking), demonstrating its effectiveness in automating cross-language schema conversion and identifying specific gaps in the current converter landscape. An accuracy benchmark for SHACL ↔ JSON Schema conversions showed the shacl-bridge converter achieving a mean F1 of 0.93.

Key takeaway

For AI Architects or Software Engineers managing data models across diverse schema languages, this orchestrator offers a robust solution. You can automate complex schema conversions, reducing manual effort and ensuring consistency. Utilize its graph-based approach to discover optimal conversion paths and identify gaps in your current converter landscape. This system provides ranked, reproducible results with full provenance, streamlining your interoperability challenges and informing future tool development.

Key insights

Orchestrating black-box schema converters via a graph model enables automated, quality-ranked data model consistency across heterogeneous languages.

Principles

• Schema languages are graph nodes, converters are directed edges.
• Empirical quality ranking guides path selection.
• Provenance metadata ensures traceability and reproducibility.

Method

The Schema Conversion Orchestrator discovers cycle-free paths, prunes dominated ones, ranks the top 10 candidates by benchmark accuracy, empirical edge quality, then output size, executes them, and returns results with full provenance.

In practice

• Integrate existing converters as black-box edges.
• Use structural F1 metrics for accuracy benchmarks.
• Employ agent-assisted human review for quality annotation.

Topics

• Schema Conversion
• Data Model Interoperability
• JSON Schema
• XML Schema Definition
• SHACL
• Graph-Based Orchestration
• MetaConfigurator

Code references

• MetaConfigurator/shacl-bridge
• citiususc/jsonschema2shaclPython
• comake/shacl-to-json-schemaSoftware
• ethlo/jsons2xsdJava
• gbd-ufsc/JS2SHACLWeb

Best for: AI Scientist, Research Scientist, Software Engineer, AI Architect

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