schema grounding

Current structured data modeling and cross-format schema mapping lack accessible, low-threshold tools—particularly hindering non-expert users. This paper proposes a hybrid approach synergizing large language models (LLMs) with deterministic rule-based processing: LLMs interpret natural-language requirements to generate or refine JSON Schema, while a verifiable rule engine performs high-precision, scalable schema mapping across multiple formats (JSON, CSV, XML, YAML). The method is implemented in the open-source tool MetaConfigurator, supporting visual schema modeling and automated code generation. Empirical evaluation in the chemistry domain demonstrates substantial reductions in modeling barriers, significant improvements in schema construction efficiency and mapping accuracy, and—critically—the first end-to-end data schema engineering solution that is natural-language-driven, flexible, and formally reliable.

Semantic drift in enterprise data pipelines—caused by multilingual transformations—decouples metadata from downstream data semantics, undermining reproducibility, governance, and performance of RAG and text-to-SQL applications. To address this, we propose a fine-grained schema lineage extraction method leveraging multilingual parsing, chain-of-thought prompting (optimized for 1.3B–32B models), and human-in-the-loop evaluation. We introduce SLiCE (Schema Lineage Composite Evaluation), the first benchmark framework tailored for multilingual script lineage, alongside a high-quality dataset of 1,700 real-world annotated samples. Experiments show that open-weight 32B models match GPT-4’s lineage accuracy under standard prompting, demonstrating cost-effective lineage extraction. Our core contributions are: (1) a systematic formalization of semantic-faithful lineage; (2) the first open, multilingual schema lineage benchmark with rigorous annotations; and (3) a lightweight, efficient extraction paradigm enabling scalable, accurate lineage inference.

Existing knowledge graph benchmark datasets commonly lack complete ontological schema information, limiting their utility for evaluating algorithms that rely on semantic constraints or neuro-symbolic reasoning. To address this gap, this work proposes a workflow that jointly extracts both schema and factual triples from knowledge graphs to construct consistency-aware datasets. By leveraging the OWL ontology language and description logic-based reasoning mechanisms, the approach resolves inconsistencies and infers implicit knowledge. The project delivers the first systematically constructed, high-expressivity dataset that integrates a complete ontological schema with factual assertions, while also enriching existing benchmarks with schema information. All released resources support both logical reasoning services and tensor-based loading in mainstream machine learning frameworks, substantially enhancing the fidelity and comprehensiveness of algorithm evaluation.

This work proposes a novel dataset discovery framework that leverages citation contexts from scientific papers to better capture the semantic intent behind research queries, addressing the limitations of existing dataset search engines that rely primarily on metadata and keyword matching and consequently suffer from low recall. By treating citation context as the core signal—combined with large-scale context extraction, large language model–guided pattern recognition, and provenance-preserving entity resolution—the approach significantly reduces dependence on incomplete or inconsistent metadata. Evaluated on eight computer science queries, the method achieves an average normalized recall of 47.47% (peaking at 81.82%), substantially outperforming Google Dataset Search and DataCite Commons. The framework’s novelty and practical utility have been affirmed by domain experts across multiple disciplines.

Scientific process descriptions are often embedded in unstructured text, hindering reproducibility, comparison, and automation. To address this challenge, this work presents the first cross-disciplinary, expert-driven repository of structured scientific process schemas, encompassing 16 expert-annotated patterns across five domains. Through a human-in-the-loop workflow, candidate schemas generated by large language models were iteratively refined via domain expert feedback, yielding reusable fields such as inputs, outputs, steps, and parameters. The resulting schemas are formalized in both JSON Schema and SHACL formats and accompanied by an integrated toolchain. The project also releases a comprehensive dataset—including schemas, intermediate artifacts, review records, and analysis scripts—to support knowledge graph construction, semantic publishing, and cross-study comparison.

This study addresses the challenge of low-quality metadata that hinders dataset discoverability and reuse, particularly in the context of large language model (LLM)-generated descriptions lacking empirical guidance on context selection and its impact on quality. Building a literature-based framework for description quality assessment, the authors conduct systematic ablation experiments across 252 real-world CSV datasets. They uncover a previously unreported “table-structure penalty” phenomenon: relying solely on table structure significantly degrades narrative quality. While representative data samples aid semantic grounding, they do not improve overall human-rated quality. The work further reveals that different LLMs exhibit consistent descriptive styles. Through LLM-as-a-judge evaluations, semantic attribute analysis, and large-scale experimentation, the study offers key recommendations for LLM-assisted data publishing: concise, relevant context yields better results than redundant input, and table structure should be used cautiously as a basis for generation.

This work addresses the challenge that domain experts face in translating natural language descriptions of data quality requirements into executable analyses, a process often hindered by reliance on data engineers, resulting in inefficiency and high technical barriers. To overcome this, the paper proposes a no-code, model-driven pipeline that leverages a QPM metamodel to define domain-specific quality analysis templates. Coupled with the Constrainify toolchain, it automatically transforms natural language requirements into executable and reusable analytical logic. By integrating model-driven engineering, metamodeling, and no-code web technologies, the approach significantly reduces dependency on technical expertise, enabling efficient, reproducible, and semantically aligned data quality assessments. This advancement enhances both the accessibility and automation of data quality analysis for non-technical domain practitioners.

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.