Papers: BIBFRAME & KOS

MARC- and MODS-based bibliographic data, due to their record-based structure, have limitations in supporting entity identification and semantic relationships in linked data environments. Existing approaches to converting these formats into BIBFRAME primarily rely on field-level mappings, which fail to capture bibliographic meaning and relationships adequately. To address this issue, this study proposes a semantic-based transformation framework that reinterprets bibliographic data at the level of semantic units. The framework includes a normalization model and a transformation module consisting of semantic analysis, entity extraction, relationship generation, and RDF transformation. This approach enables the reconstruction of bibliographic data into an entity–relationship structure, facilitating their conversion into BIBFRAME while preserving semantic integrity and enhancing interoperability and reusability.

Authority data plays a critical role in clearly identifying and linking library resources, enabling users to explore more accurate and enriched information. However, current authority data remains constrained by rigid record-based structures, resulting in limited interoperability with external data sources in the web information environment, This study focuses on author authority data and proposes a method for linking it with various author identifier systems used across different domains. Within the BIBFRAME framework, which is designed for linked data environments, the technical elements of both author authority data and author identifier systems are identified at the data level. Based on this, an authority structure is conceptualized to represent diverse relationships associated with authors. The proposed structure consists of four interconnected layers centered on the author authority entity. Each component within these layers is uniquely identified by URIs and published as linked data, thereby extending traditional authority data into a web-based, interoperable ecosystem. This approach enhances the semantic connectivity of author-related information and provides a foundation for integrating heterogeneous author identification systems within the linked data environment.

Library of Congress Classification (LCC) and Dewey Decimal Classification (DDC) are two of the most widely used knowledge organization systems in libraries, yet empirical understanding of how they align and diverge in cataloging practice at scale remains limited. This paper examines co-assignment patterns between LCC and DDC classes using 4,042,962 dual-classified bibliographic records drawn from the Library of Congress's book catalog. Through descriptive quantitative analysis and bipartite network analysis, we identify areas of strong and weak structural correspondence between the two systems. Results reveal that well-defined humanities disciplines — including law, fine arts, religion, literature, and history — exhibit high one-to-one alignment, while broader and more applied domains such as social sciences, technology, and computer and information science show markedly dispersed cross-system mappings. A structural asymmetry is also evident: LCC classes tend to map more sharply to single DDC counterparts than vice versa. Network analysis identifies seven disciplinary communities and highlights Social Sciences and Technology as key interdisciplinary hubs, while second-level classes reveal contrasting topologies — a hub-centric star structure for LCC:G and a fragmented, multi-polar constellation for DDC:6XX. These findings carry practical implications for library reclassification projects, cataloging workflows, and the reuse of bibliographic metadata in emerging technological environments.

KOS registry entries often cover multidimensional topics that resist representation by a single classmark, making multi-notation assignment a common but semantically limited approach. This study explores how selected K-KOS entries can be reclassified under the developing version of the Integrative Levels Classification (ILC) and synthesized into structured classmarks using the ILC Classmark Builder, with multi-notation assignment serving as the baseline representation. Based on three representative cases, candidate classmarks were manually constructed and examined through baseline assignment, free-facet combination, and facet synthesis, with support from the Builder for retrieval, combination, and syntactic validation, and followed by expert review of the resulting classmarks. The findings show that notation synthesis makes semantic relations more explicit and enhances the structural expressiveness of KOS representation, while also revealing that successful synthesis depends on human judgment in syntactic disambiguation, conceptual interpretation, and evaluation among alternative formulations. The study demonstrates both the feasibility and the practical challenges of this transition, and confirms the value of tool support — while underscoring that human judgment remains indispensable.

This study examines the limitations of relationship definitions in KCR5 and proposes an extension model based on BIBFRAME 3.0 to address these issues. Although KCR5 adopts the FRBR-based entity-relationship structure, its scope remains largely focused on manifestation-level description and is primarily limited to WEMI–Agent relationships. As a result, relationships involving contextual entities such as place, time, and subject are insufficiently represented. To overcome these limitations, this study utilizes BIBFRAME 3.0 classes and properties to propose an extended relationship framework centered on contextual entities. The proposed model enhances the representation of spatiotemporal and thematic relationships within bibliographic data, enabling richer semantic connections among resources. Furthermore, this study highlights the potential for improving interoperability in international data exchange and supports the development of a semantic, knowledge graph-based bibliographic environment grounded in BIBFRAME. The findings contribute to advancing the transition from record-based cataloging to entity-based, relationship-oriented bibliographic structures in the Korean cataloging context.

This study reinterprets classification notes in the Korean Decimal Classification (KDC) as key semantic elements for automated classification and proposes a method for structuring relationships among classification entries. Existing approaches have relied on keyword-based analysis or simple mapping, limiting their ability to reflect the intellectual structure of classification systems. To address this limitation, this study analyzes the types and functions of KDC notes and identifies their roles in expressing semantic relationships, such as conceptual definition, hierarchical and associative links, subdivision rules, and exceptions. These relationships are then categorized into internal and external relations and formalized as properties within a linked data framework. This approach enables the transformation of unstructured notes into machine-processable structures and supports the development of semantically enriched, knowledge graph–based classification systems.