arXiv:2606. 05415v1 Announce Type: cross Abstract: Real-world data spans tables, documents, and semi-structured files with implicit semantics.
arXiv:2608.21375v1 Announce Type: new Abstract: Heterogeneous agentic retrieval-augmented generation (RAG) systems increasingly orchestrate external APIs, internal databases, vector stores, and graph...
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:2508. 01815v2 Announce Type: replace-cross Abstract: Text-to-SPARQL maps natural-language questions to executable SPARQL queries over RDF knowledge graphs.
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.
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.
The paper introduces EXYGEN, a framework that enables conversational access to large knowledge graphs by combining VoID descriptions, ShEx schemas, retrieved triples, and example question‑query pairs in a retrieval‑augmented generation pipeline. On the SciQA benchmark, this approach achieves an exact‑match score of 0.419 without fine‑tuning any large language model, and shows that larger general‑purpose LLMs can outperform smaller code‑specialized ones when provided sufficient context. To scale metadata generation for very large KGs, the authors propose a predicate‑coverage‑aware parallel graph sampling strategy that preserves structural diversity, reduces runtime by over 80× on OpenCitations Meta and GESIS, and is the only tractable method for obtaining complete metadata on ORKG.
The paper introduces a novel LLM‑driven multi‑agent pipeline that converts relational databases into graph databases by standardizing table and column names and iteratively refining the graph schema through ETL, Analyzer, and Graph agents. The resulting graph database meets accuracy, groundedness, and faithfulness criteria and shows significant performance gains, achieving 85.6% Q&A accuracy—12.12% higher than an SQL agent on PostgreSQL—and reducing latency by roughly threefold on a BFSI dataset. This demonstrates an efficient, automated method for transforming tabular data into a more intuitive and faster‑executing graph format.
arXiv:2607. 03447v1 Announce Type: cross Abstract: Knowledge graphs (KGs) that underpin Graph-based Retrieval-Augmented Generation (Graph-RAG) are increasingly built automatically by LLM-driven extraction rather than curated by experts.
arXiv:2607. 29677v1 Announce Type: new Abstract: Enterprise workflows increasingly rely on agents for \emph{schema-guided extraction}: given a document and a user-defined schema, the agent faithfully follows the schema to produce the correct output with source evidence as grounding metadata.
The paper introduces a modular agentic-AI platform that transforms heterogeneous CMC process-development documents into a dual-layer knowledge graph. The base layer creates a lexical Document‑Section‑Chunk hierarchy, while the intelligence layer extracts ontology‑aligned entities and links cross‑document concepts, all anchored by provenance. LLM agents navigate these layers to answer queries, and a novel three‑tier evaluation protocol demonstrates high retrieval‑augmented generation performance on proprietary data from a Sanofi program.
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:2607. 12310v1 Announce Type: cross Abstract: While modern question answering (QA) systems excel on clean, schema-aligned corpora, real-world knowledge is rarely so neatly packaged.