Short Papers

Cataloging rules as content standards and encoding formats based on the LRM conceptual model should be closedly interrelated to ensure effective representation of resources. Numerous studies have examined the mapping among these standards, including mappings from conceptual models to cataloguing rules, from cataloging rules to encoding format, and from conceptual models to encoding formats. Examples include studies on mappings between LRM and RDA, RDA and BIBFRAME, as well as between LRM and BIBFRAME. Last year, the Korean Cataloging Committee developed KCR5 (previous KCR4) based on the original RDA. Accordingly, this study aims to evaluate the suitability of KCR5 for the exchange and sharing of bibliographic data within the BIBFRAME and RDA frameworks by mapping KCR 5 to both RDA and BIBFRAME.

Cataloging rules as content standards and encoding formats based on the LRM conceptual model should be closely interrelated to ensure effective representation of resources. Numerous studies have examined the mapping among these standards, including mappings from conceptual models to cataloguing rules, from cataloging rules to encoding format, and from conceptual models to encoding formats. Examples include studies on mappings between LRM and RDA, RDA and BIBFRAME, as well as between LRM and BIBFRAME. Last year, the Korean Cataloging Committee developed KCR5 (previous KCR4) based on the original RDA. Accordingly, this study aims to evaluate the suitability of KCR5 for the exchange and sharing of bibliographic data within the BIBFRAME and RDA frameworks by mapping KCR 5 to both RDA and BIBFRAME.

This paper presents a literature-based synthesis of motivations for participating in biomedical ontology communities, viewed as metadata infrastructures. As generative AI transforms ontology curation into human-in-the-loop workflows, human engagement becomes essential for ensuring metadata quality. Using Self-Determination Theory and Activity Theory, it identifies four themes—intrinsic motivation, extrinsic motivation, community aspects, and human AI collaboration—and analyzes their impact on autonomy, competence, and relatedness. Based on these themes, the study proposes practical implications for provenance-enhanced verification, quality-based incentives, and collaborative environments to sustain metadata quality and ongoing contributions.

Metadata systems traditionally prioritise technical efficiency over cultural authenticity. This paper examines how Singapore's National Library Board redesigned metadata practices for two major cultural heritage digital projects—the Encyclopedia of Singapore Tamils (EST) and Prominent Malays of Singapore (PMoS)—to align digital systems with community values. Breaking from locally established conventions, we implemented largely monolingual metadata records, creating separate collection identifiers, language-specific navigation paths, and culturally-aware controlled vocabularies. This approach required overcoming technical constraints in content management systems designed for English-language dominant workflows. Our methodology involved close collaboration with over 500 community contributors. Key innovations include collection name separation, multilingual controlled vocabulary integration, and community-driven category translation that reflects cultural mental models rather than literal translations. Implementation demonstrates that separate monolingual metadata records can preserve cultural authenticity while maintaining system functionality. These approaches create valuable training data for AI systems working with multilingual cultural heritage, offering a replicable model for institutions seeking to align digital systems with diverse community needs rather than technical convenience.