An AI-guided framework is developed that aligns AI-assisted metadata extraction with Dublin Core Terms and the FAIR principles for digital libraries, archives, and cultural-heritage repositories and is evaluated as a design-science artefact in which retrieval is not a side feature but a feedback loop.
The findings suggest that AI-based structured extraction may redefine how organisations formalise expertise, shifting from document-centric storage toward schema-driven knowledge architectures.
Dilyan Georgiev, E. Gourova· European Conference on Knowl...· 0 citations
Large Language Models (LLMs) offer strong capabilities for Natural Language Processing, yet their inherent uncertainty often produces hallucinations, confident but incorrect statements, which is critical in domains requiring precise knowledge representation. Retrieval-Augmented Generation (RAG) reduces this risk throug...
Alexandra V. Jove-Ticona, Luis J. Duarte-Coaquera, Israel N. Chaparro-Cruz et al.· International Journal of Adv...· 0 citations
An AI-assisted form for KG metadata curation that enables a more efficient curation workflow, leads to more complete metadata, and is preferred by participants over both baselines is presented.
M. Mohammadi, Anas Elghafari, Chang Sun et al.· 0 citations
An AI-driven pipeline for transforming the interviews from “Digitalne Ikone 20+” book from unstructured transcripts into a structured, semantically enriched, and queryable knowledge resource that allows researchers, students, and the public to explore cultural heritage interviews through intelligent querying, automated...
R. Stanković, Tamara Vučenović, Milica Ikonic-Nesic et al.· Computer Science and Informa...· 0 citations
The results demonstrate that, once a domain metamodel and expert-authored examples are available, few-shot code generation can extract legal metadata relationships without training a task-specific supervised model and can be adapted to unseen legislation.
Anmol Singhal, Travis D. Breaux· Requirements Engineering· 0 citations
A practical, quality-assured method for publishing KOS-related RFPs as context-bearing registry records is contributed by integrating a two-track pipeline—scripted page generation for structured fields and generative summarization for narrative RFP text—with HITL governance for expert verification and correction.
Ziyoung Park· Knowledge organization· 0 citations
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