javascript object notation

To address the error-prone and inefficient manual rewriting of data transformation logic upon JSON Schema evolution, this paper proposes a type-directed, top-down program synthesis approach for automatically generating semantics-preserving JSON Schema converters. Our method integrates type inference, semantic constraint modeling, a rewrite system, and intermediate representation (IR)-driven code generation to guarantee lossless data transformation and formal verifiability. It natively supports complex nested schemas and synthesizes correct, efficient, and human-readable Python and JavaScript conversion code. We evaluate our approach on real-world API configuration schemas and healthcare data integration scenarios, demonstrating its safety—via formal guarantees and empirical validation—its practical utility in industrial settings, and its generalizability across diverse schema evolution patterns. Experimental results confirm high accuracy, robustness to structural changes (e.g., field additions, type refinements, nested object restructuring), and scalability to large, deeply nested schemas.

Modern JSON Schema (starting from the 2019-09 draft) introduces annotation dependencies and dynamic references, leading to semantic collapse, undecidability of satisfiability checking, and exponential schema bloat. Method: We propose the first formally verified method for eliminating annotation dependencies via semantic restructuring, static keyword analysis, and equivalence-preserving transformations—rewriting modern schemas into semantically equivalent classical schemas. Contribution/Results: We rigorously prove that exponential blowup in the worst case is unavoidable during dependency elimination, and we design a practically efficient algorithm achieving sub-exponential runtime in practice. Experimental evaluation demonstrates that our algorithm significantly outperforms the theoretical lower bound, enabling reliable schema validation and seamless integration with existing toolchains. This overcomes a critical barrier hindering the migration of classical satisfiability-checking techniques to modern JSON Schema.

Traditional industries rely on unstructured long-form documents to store high-value information, yet they lack high-quality, scalable training data for text-to-JSON structuring. This work proposes STAGE, a method that achieves the first end-to-end synthetic data generation pipeline grounded in spreadsheet source data: leveraging large language models to produce semantically coherent narrative reports alongside their corresponding JSON representations, and employing value-level verification against the original source data to ensure factual fidelity. The STAGE-Eval dataset constructed via this approach substantially enhances model performance—on Qwen3-4B, exact match accuracy improves from 31.37% to 74.27%, and value-level accuracy rises from 45.46% to 90.69%.

Existing object serialization formats (e.g., Protobuf, JSON, XML) exhibit poor readability and limited auditability in source-embedded contexts such as test cases. This paper introduces ProDJ—the first pure-code serialization technique for Java—that converts runtime objects directly into syntactically valid, executable, and highly readable Java source code expressions. Its core innovation lies in leveraging the target language’s native syntax for serialization, integrating reflective introspection, abstract syntax tree (AST) generation, and cycle-aware object graph traversal to simultaneously ensure readability, executability, and maintainability. Evaluation demonstrates that ProDJ successfully serializes over 174,000 real-world objects with negligible runtime overhead. A user study confirms that developers significantly prefer ProDJ-generated Java code over JSON or XML—particularly for development tasks requiring human involvement, such as test generation.

To address poor data interoperability among computer algebra systems (CAS) and the difficulty of reproducing and reusing symbolic computation results, this paper designs and implements the first general-purpose JSON format conforming to the FAIR principles—Findable, Accessible, Interoperable, and Reusable. Rigorously defined via JSON Schema, the format supports lossless serialization of core algebraic objects—including polynomials, matrices, and groups—and is deeply integrated into the OSCAR system. It enables bidirectional data exchange with other algebraic environments such as Singular, GAP, and Polymake. The key contribution lies in the systematic application of FAIR principles to symbolic computation data persistence—a novel approach that establishes a standardized infrastructure for cross-platform reproducible mathematical computation. Empirical evaluation demonstrates the format’s high extensibility and practical deployability in real-world CAS workflows.

Standard JSON exhibits significant token redundancy when representing tabular data due to repeated key names, which undermines the contextual efficiency of large language models. This work proposes JTON, a JSON-compatible superset format that introduces the novel Zen Grid mechanism to separate column headers from values—encoding values as delimiter-separated sequences—thereby achieving substantial token compression while preserving JSON’s full type system. A high-performance parser implemented in Rust with PyO3 integrates SIMD acceleration and demonstrates, across seven real-world datasets, an average token reduction of 28.5% (up to 60%), improves comprehension accuracy by 0.3 percentage points across ten large language models, achieves 100% syntactically valid output generation for twelve models, and attains a parsing speed 1.4× faster than Python’s native json module.

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.

This study addresses the lack of efficient, automated methods for structuring heterogeneous real estate questionnaire documents. To this end, the authors propose an end-to-end information extraction framework that first categorizes documents into structural types using K-Means clustering and text classification. Subsequently, it leverages the DeepSeek-R1 large language model enhanced with prompt engineering to accurately extract 35 predefined attributes from complex document formats—including checkboxes and scanned images—and outputs them as structured JSON. Evaluated on a dataset of 2,781 documents, the method produced 2,766 unique property records. Downstream validation demonstrated a Jaccard similarity of 0.82, marking the first high-precision, scalable solution for structured information extraction from such challenging real estate documentation.

Existing approaches to JSON Schema inclusion checking struggle to balance efficiency and completeness: rule-based methods are efficient but incomplete, while instance-generation techniques are complete yet computationally expensive. This work proposes a refutation normalization technique that synergistically combines the efficiency of rule-based reasoning with the completeness of instance generation, enabling fast and reliable inclusion checking through optimized logical inference paths. Evaluated on both real-world and synthetic datasets, the proposed method significantly outperforms state-of-the-art tools, achieving theoretical completeness while substantially improving verification efficiency. The approach effectively supports complex practical applications and advances the practical applicability boundary of JSON Schema validation technologies.

This study addresses the prevalent issue in computer vision datasets wherein image provenance—such as acquisition parameters and preprocessing steps—is often missing or stored separately, leading to compromised traceability, regulatory compliance, and data reusability. To overcome this limitation, the work proposes a novel approach that leverages JSON-LD (JavaScript Object Notation for Linked Data) to define a structured provenance schema and embeds it directly within image files. This integration ensures an inseparable binding between metadata and the image payload, preserving provenance integrity and persistence across workflows. The proposed method maintains compatibility with existing standards while significantly enhancing data maintainability, system interoperability, and the trustworthiness of downstream models trained on such annotated datasets.