arXiv AI

MetaConfigurator: AI-Assisted RDF Authoring from JSON Data

arXiv:2606. 07094v1 Announce Type: cross Abstract: Scientific workflows increasingly generate structured JSON data that is easy to exchange but difficult to interpret consistently across systems due to lacking semantic interoperability.

arXiv AI

Jul 28

Retrieval-Augmented Generation of Ontologies from Relational Databases

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.

By Nadeen Fathallah, Mojtaba Nayyeri, Athish A Yogi, Ratan Bahadur Thapa, Hans-Michael Tautenhahn, Anton Schnurpel, Steffen Staab

llms rag

arXiv Machine Learning

2d ago

AutoSchema: Live Schema Grounding for Agentic Text-to-Sparql over Heterogeneous Knowledge Graphs

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.

By Yiming Zhang, Koji Tsuda

llms agents

arXiv AI

Jul 21

Natural Language Access to Domain-Specific Metadata: A Reusable Framework for LLM Query Generation

arXiv:2607. 18029v1 Announce Type: cross Abstract: Researchers need to answer ad-hoc questions about the contents of domain-specific archives but often lack the expertise to write structured queries on the metadata.

By Blake G. Fitch, Cato Elia Kurtz

llms agents fine-tuning benchmarks safety data-engineering

arXiv AI

2d ago

From Field Data to Global Food Systems Intelligence: A Semantic Graph Framework for Sustainable Wheat Production

arXiv:2502. 19507v2 Announce Type: replace Abstract: In response to the growing need for structured, interoperable agricultural data, this paper presents the Sustainable Wheat Production Datahub, a modular, graph-based framework that brings diverse wheat production datasets together into a single, queryable store.

By Nirmal Gelal, Aastha Gautam, Soheil Abadifard, Nico Giordano, Moumita Sen Sarma, Sanaz Saki Norouzi, Claudio Dias da Silva Jr, Jean Ribert Francois, Kathleen M. Jagodnik, Katherine Nelson, Terry Griffin, Xiaomao Lin, Stacy Hutchinson, Stephen M. Welch, Kelsey Andersen Onofre, Romulo Lollato, Pascal Hitzler, Hande K\"u\c{c}\"uk McGinty

arXiv Machine Learning

Jun 5

Executable Schema Contracts: From Automatic Ingestion to Multi-Source Retrieval

arXiv:2606. 05415v1 Announce Type: cross Abstract: Real-world data spans tables, documents, and semi-structured files with implicit semantics.

By Padmaja Jonnalagedda, Yuguang Yao, Xiang Gao, Hilaf Hasson, Kamalika Das

llms rag agents benchmarks

arXiv AI

5d ago

SchemaLink: An Intelligent Web Editor for LinkML Schema Curation

arXiv:2608. 12529v1 Announce Type: cross Abstract: Motivation: LinkML is a suitable language for the representation of the structural and content constraints of different kinds of biomedical data.

By Emanuele Cavalleri, Paolo Perlasca, J. Harry Caufield, Justin Reese, Christopher J. Mungall, Marco Mesiti

rag

arXiv AI

Aug 11

H2: A Dual Hybrid Semantic Data Lake Architecture for Medical Data Harmonization with Human-In-the-Loop verified, LLM Driven Metadata Annotation System

arXiv:2608. 08056v1 Announce Type: new Abstract: Medical data, by its nature, exhibit a high degree of heterogeneity on multiple levels ranging from (a) different modalities like images, text and time series, (b) diverse tabular schemata introduced by institutions and (c) completely unstructured textual information data provided by healthcare professionals.

By Ioannis N. Tzortzis, Georgia Kapetadimitri, Agapi Davradou, Nefeli Kousta, Nikolaos Bakalos, Ioannis Rallis, Dimitrios Kalogeras, Nikolaos Doulamis, Anastasios Doulamis

llms data-engineering

arXiv AI

Jun 2

From Graph Retrieval to Schema Realization: Counterfactual Validation for Text-to-SPARQL over Heterogeneous Knowledge Graphs

arXiv:2508. 01815v2 Announce Type: replace-cross Abstract: Text-to-SPARQL maps natural-language questions to executable SPARQL queries over RDF knowledge graphs.

By Yang Zhao, Chengxiao Dai, Yue Xiu, Dusit Niyato

agents nlp benchmarks safety

arXiv AI

Jul 28

SetGo: Metadata Readiness for Scientific AI Datasets

arXiv:2607. 22677v1 Announce Type: cross Abstract: Scientific datasets intended for AI use require both computational readiness for model training and metadata readiness for discovery, sharing, and reuse.

By Sean R. Wilkinson, Polina Shpilker, Wesley Brewer

llms agents

arXiv AI

Jul 17

SAGA: Schema-Aware Grounding for Agentic Text-to-SPARQL Generation

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.

By Yiming Zhang, Koji Tsuda

llms rag agents nlp benchmarks

arXiv AI

Jul 2

Making Failure Safe: A Constrained, Verifiable Agent Framework for Open-Web Data Collection

arXiv:2607. 00035v1 Announce Type: new Abstract: LLMs and agents can generate web scrapers from natural-language requirements, but direct generation remains unreliable because of dependency errors, broken selectors, schema mismatches, and heterogeneous page structures.

By Bo Chen

llms agents data-engineering

arXiv AI

Jul 15

FAIR GraphRAG: A Retrieval-Augmented Generation Approach for Semantic Data Analysis

arXiv:2607. 11464v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) addresses the limitations of Large Language Models (LLMs) when providing responses to domain-specific questions.

By Marlena Fl\"uh, Soo-Yon Kim, Carolin Victoria Schneider, Sandra Geisler

llms rag nlp

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