arXiv:2607. 05970v1 Announce Type: cross Abstract: Dataset search depends heavily on metadata, making LLM-generated metadata a consequential form of synthetic content in retrieval systems.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2605. 28787v2 Announce Type: replace-cross Abstract: In the era of autonomous agents, machine-actionable data is critical for data-driven workflows.
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:2607. 14494v1 Announce Type: new Abstract: Complex knowledge base question answering (KBQA) is commonly approached through either information retrieval over a question-specific subgraph or semantic parsing into an executable logical form.
arXiv:2508. 01815v2 Announce Type: replace-cross Abstract: Text-to-SPARQL maps natural-language questions to executable SPARQL queries over RDF knowledge graphs.
RENSA is a federated SPARQL query generation framework that extends SPARQL Builder Metadata to include class and authority information, enabling precise source selection and semantic constraint inference without runtime ASK queries. The generated metadata profiles occupy less than 1% of the original dataset triples, providing storage‑efficient insights. Evaluation on the LargeRDFBench benchmark shows that RENSA matches state‑of‑the‑art source selection performance while eliminating runtime communication overhead.
arXiv:2609.16498v1 Announce Type: cross Abstract: Research data repositories are essential infrastructure for scientific inquiry and for ensuring that datasets follow FAIR (Findable, Accessible, Inte...