SINT-Flow is a schema integration framework that uses five large language model (LLM) operators to automatically produce a unified schema from multiple input tables, even when those tables are denormalized and contain attributes for several entity types. The framework decomposes such tables into entity‑specific relations during integration. Evaluation on the newly introduced SINT‑Bench benchmark, using GPT‑5.2 and Qwen‑3.6‑27B, shows high performance with at least 97% completeness in entity type detection, 81% attribute detection, and an 84% F1‑score for schema mapping, while ablation studies confirm the benefits of self‑consistency and a review loop.
arXiv:2608. 06331v1 Announce Type: cross Abstract: From natural-language query interfaces to automated report generation, data analysis tools need a description of the data: the real-world entities it contains, which columns function as measures or identifiers, and how tables connect into units of analysis.
arXiv:2608. 14228v1 Announce Type: new Abstract: Life science knowledge graphs make large collections of structured data available through SPARQL, but each resource uses its own schema, identifiers, and links.
arXiv:2606. 28601v1 Announce Type: cross Abstract: Recent progress in Text-to-SQL has been driven by stronger language models and prompting strategies, yet performance on real enterprise benchmarks such as Spider 2.
The paper examines the role of schema linking in Text-to-SQL systems and finds that recent large language models can effectively use relevant schema elements even when many irrelevant ones are present. Consequently, the authors eliminate schema linking when the entire schema fits within the model’s context window, instead employing augmentation, selection, and correction techniques to enhance accuracy. Their approach achieves first place on the BIRD benchmark with a 71.83% accuracy.
arXiv:2607. 21610v1 Announce Type: cross Abstract: Schema graphs are an upstream bottleneck of schema-grounded information extraction and knowledge graph construction, yet most extraction systems assume the schema is already available.
The paper introduces an explainable, header‑centric framework for semantic table interpretation and data quality assessment that operates solely on column headers. It maps headers to 39 interpretable types using curated lexical resources, applies validation rules to detect data quality issues, and aggregates these into a lightweight quality metric called HeadersIQ. The framework was evaluated on diverse benchmarks, demonstrating broad coverage of noisy real‑world metadata and providing a workflow for metadata‑driven semantic annotation, quality monitoring, and benchmark diagnosis.
Practical AI systems increasingly need to turn long, heterogeneous documents into queryable relational databases, not isolated spreadsheets. In domains such as finance, healthcare, education, transportation, and enterprise operations, downstream workflows rely on normalized schemas, entity identities, keys, cross-table relationships, and integrity constraints for analytics, compliance, auditing, and SQL-backed decision making.
arXiv:2608. 08459v1 Announce Type: cross Abstract: Practical AI systems increasingly need to turn long, heterogeneous documents into queryable relational databases, not isolated spreadsheets.
arXiv:2607. 24707v1 Announce Type: new Abstract: Entity-Relationship Diagrams (ERDs) are central to conceptual database design, yet they are typically available only as rendered images rather than machine-readable schemas, limiting AI-assisted database engineering.
arXiv:2607. 16201v1 Announce Type: new Abstract: Ontology engineering remains a critical bottleneck in knowledge-intensive AI systems.
arXiv:2506. 01232v2 Announce Type: replace-cross Abstract: Deriving OWL ontologies from relational database schemas supports semantic interoperability and downstream tasks such as knowledge graph population, ontology-based data access, graph-based learning, and automated reasoning.
arXiv:2508. 01815v2 Announce Type: replace-cross Abstract: Text-to-SPARQL maps natural-language questions to executable SPARQL queries over RDF knowledge graphs.