🤖 AI Summary
This study addresses the challenges of maintaining consistency across heterogeneous schema languages—such as JSON Schema, XSD, and SHACL—during multilingual data model evolution, where fragmented converters, variable quality, and information loss impede reliable interoperability. The work proposes a novel approach that models schema languages and black-box converters as nodes and directed edges in a graph, enabling composable and evaluable conversion path orchestration. By integrating graph-based search, quality-aware ranking (combining agent-assisted and human evaluation), and failure backtracking, the method supports automated, reproducible cross-language schema transformation. The resulting open-source toolchain, Schema Conversion Orchestrator, integrated into the MetaConfigurator platform, successfully produced valid outputs for 43 out of 60 real-world tasks and precisely identified missing ecosystem components in the remaining 17, thereby delineating the current boundaries of schema conversion capabilities.
📝 Abstract
Modern software systems routinely need the same data model in several schema languages: a model may exist as JSON Schema for a web API, as XSD for data exchange, and as SHACL for a knowledge graph. Keeping these representations consistent as the model evolves is a recurring construction and maintenance burden, because converters between schema languages are hard to find, scattered across ecosystems, of uneven quality, and frequently lossy. We study, empirically, to what extent such imperfect, heterogeneous converters can be orchestrated into automated, reproducible, and quality-ranked conversions, and where the current converter landscape reaches its limits. Our approach models schema languages as nodes and converters, treated as black boxes, as directed edges, so that conversions become paths that are discovered, executed, ranked, and reported with full per-step provenance, with failures handled by trying alternatives. We realize it as the open-source Schema Conversion Orchestrator, integrate it into MetaConfigurator, and evaluate it on 60 conversion tasks built from real-world schemas across five schema languages, using agent-assisted, human-reviewed quality annotations. Orchestration surfaces a usable result for 43 of 60 tasks; the remaining failures localize concrete gaps in the converter landscape. We discuss implications for tool builders and for measuring conversion quality.