2026-10-01T21:05:13Z https://dcpapers.dublincore.org/oai

oai:dcpapers.dublincore.org:952610716 2026-09-10 dcmi-2026 openaire

AI-Guided Metadata Construction for Meaning-Driven Digital Knowledge Systems: A Framework for Automated Metadata Generation and Semantic Discovery Chansanam, Wirapong Detthamrong, Umawadee Li, Chunqiu Ahmad, Abdul Rahman Elmalech, Avshalom The rapid expansion of digital repositories and scholarly resources has increased the demand for scalable and intelligent metadata management systems. Traditional metadata creation methods, which rely on manual cataloguing by information professionals, struggle to keep pace with the growing volume and heterogeneity of digital content. This study develops and evaluates an AI-guided framework, Metadata-Building-AI-Guidance V.1.0 that combines document ingestion, chunking, OpenAI text-embedding-3-small embeddings, and the gpt-4o-mini large language model into a single Streamlit application supporting both automated Dublin Core-aligned metadata extraction and embedding-based semantic retrieval. We position the system as a design-science artefact in which retrieval is not a side feature but a feedback loop: the same vector index that powers similarity search is also used to surface the contextual chunks from which structured metadata are extracted and to support retrieval-augmented question answering over uploaded collections. The system was evaluated on a purposively sampled corpus of 30 open-access academic documents and 20 expert queries, using (i) field-level F1 against librarian-curated ground truth with Cohen's κ for inter-annotator agreement and (ii) Precision@5 and Mean Reciprocal Rank with binary relevance judgements from two librarians. Field-level F1 ranged from 0.67 to 0.73 for dc:title, dc:creator, dc:subject, and dc:description, with overall κ = 0.83 (almost perfect agreement); the lowest F1 was 0.57 for dc:type, traced to a fixed generic prompt output rather than to a model-capability limitation. Semantic retrieval reached Precision@5 = 0.61 and MRR = 0.69 with κ = 0.66 (substantial agreement) across the 20 queries. We discuss the limits of LLM-only evaluation—including the absence of a head-to-head comparison with established non-LLM extractors such as GROBID—and identify controlled baseline comparison together with a refined dc:type prompt as the immediate next steps. Prompts, JSON schema, library versions, sampling log, and evaluation queries are released to support replication. The contribution is a reproducible reference implementation that aligns AI-assisted metadata extraction with Dublin Core Terms and the FAIR principles for digital libraries, archives, and cultural-heritage repositories. Dublin Core Metadata Initiative 2026-09-10 info:eu-repo/semantics/conferenceObject Text application/pdf text/html https://doi.org/10.23106/dcmi.952610716 https://dcpapers.dublincore.org/article/952610716 Dublin Core Metadata Initiative Conference Proceedings eng info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/