Full Papers
The programme is still being finalized and is subject to ongoing updates as sessions are scheduled. Please check back regularly for the latest changes.
A BIBFRAME-Based Authority Structure for Interlinking Authority Data and Author Identifier Systems in a Linked Data Environment
Authors: Juhui Lee, Seungmin Lee
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.
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Juhui Lee
Librarian
Seoul National University Library
Juhui Lee has been working at Seoul National University Library from 2023. Including her previous experience, she has accumulated diverse experience in cataloging, library communications, and materials management. She holds a B.A. in Library and Information Science and a M.A in Record Management from Chung-Ang University. She is interested in connecting library data to the web environment.
A Workflow-Based Approach for Metadata Interoperability via Domain-Specific Schema Mapping to DataCite
Authors: Yan CONG, Masao TAKAKU, Yasuyuki MINAMIYAMA, Shigeki MATSUBARA
As research becomes increasingly data-driven and interdisciplinary, metadata interoperability has become essential for efficient data discovery and reuse.
However, many domain-specific metadata schemas lack clear specifications and standardized structures, making cross-domain integration difficult.
To address this issue, this study proposes a systematic three-phase workflow for cross-domain metadata mapping. The workflow consists of Phase (i) preliminary assessment of metadata schemas, Phase (ii) mapping relationship analysis, and Phase (iii) XSLT-based metadata transformation.
To evaluate the effectiveness of the proposed workflow, a case study is conducted using a set of 125 metadata schemas.
The results show that only 10 out of 125 schemas satisfy the requirements for mapping to the six mandatory DataCite properties, while the remaining schemas are limited by incomplete documentation or structural heterogeneity.
In particular, challenges in representing properties such as ``Identifier'' and ``ResourceType'' highlight persistent semantic and structural mismatches across metadata standards.
XSLT transformation files were developed and released, enabling practical implementation of the proposed mapping approach.
This study contributes a systematic workflow for metadata mapping and provides empirical evidence on the limitations of current metadata standardization practices, supporting future efforts toward improved cross-domain metadata interoperability.
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YAN CONG
Nagoya University
I obtained my Ph.D. in Library and Information Science, with a research focus on metadata standardization, Linked Open Data (LOD), and TEI markup. I also explored the application of AI in education, particularly methods for evaluating and validating its effectiveness in learning contexts. After graduation, I joined my current position, which focuses on metadata and interoperability. My work centers on metadata standardization and Persistent Identifiers (PIDs), with the aim of improving data consistency, system integration, and long-term accessibility across heterogeneous information systems.
AI-Guided Metadata Construction for Meaning-Driven Digital Knowledge Systems: A Framework for Automated Metadata Generation and Semantic Discovery
Authors: Wirapong Chansanam, Umawadee Detthamrong, Chunqiu Li, Abdul Rahman Ahmad, and Avshalom Elmalech
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.
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Avshalom Elmalech
Researcher
Bar-Ilan University
Avshalom Elmalech is a researcher at Bar-Ilan University with a PhD in Computer Science, working at the intersection of applied artificial intelligence and digital humanities. His research bridges information science and AI by examining how deep learning methods can be effectively applied to humanities data. He has contributed practical frameworks for guiding digital humanities scholars in choosing and adapting NLP and deep learning approaches under constraints such as limited training data and domain specificity. -
Wirapong Chansanam
Associate Professor
Khon Kaen University
Wirapong Chansanam is an Associate Professor of Information Science at Khon Kaen University, Thailand. He earned his Ph.D. in Information Science in 2014 and currently serves as Head of the Information Science Department and Chair of the Digital Humanities Research Group. His research focuses on information science, ontology, knowledge organization systems, linked open data, and data analytics. He actively contributes to advancing digital knowledge management and innovation.
An Exploratory Study on Genre Labeling of Online Comic Reading Platforms in Taiwan
Authors: Tzu-Yun Chien, Li-Min Huang
As online comic reading continues to grow in Taiwan, online platforms have become important sites for both comic consumption and discovery. This exploratory study examines genre-labeling practices on three major online comic platforms in Taiwan. We collected genre terms from the platforms’ Traditional Chinese and English interfaces and generated a list of 35 unique English genre terms. These terms were then mapped onto a facet framework drawn from previous literature. Our preliminary findings show that platform genre labels function as multidimensional access points rather than simple genre categories, representing aspects such as setting, mood, plot or narrative, and production context. Cross-platform differences in labeling granularity and cross-language differences in semantic scope were also observed. The findings may inform the development of more consistent and user-friendly genre labels for digital comic environments.
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Tzu-Yun Chien
National Taiwan University
Tzu-Yun Chien is a Master’s student in the Department of Library and Information Science at National Taiwan University. Her research interest focusing on human–computer interaction and information behavior. Her prior research on user behaviors in AI-assisted tasks has been published as a full conference paper. Her ongoing master's thesis focuses on the differences between existing genre categorization frameworks, platform labeling practices, and user interpretations within online comics.
Automated Classification of Chinese Books: A Large Language Model Approach to Knowledge Transfer and Domain Adaptation
Authors: Xin Yang, Junzhi Jia, and Ying-Hsang Liu
Automated subject indexing remains a critical challenge for digital libraries and knowledge organization systems. To address this issue, this study develops a knowledge-augmented domain adaptation framework that aligns general-purpose language models with the hierarchical logic of the Chinese Library Classification (CLC). A supervised fine-tuning (SFT) strategy is proposed to resolve domain knowledge drift and deep-category recognition bottlenecks in large language model (LLM)-based Chinese book indexing, using dual bibliographic and category data. Three evaluation experiments were conducted to assess the effectiveness of the proposed techniques for quantifying ontological contributions: hyperparameter sensitivity analysis for baseline establishment, backbone model comparison for architectural fitness, and knowledge injection ablation for. Results demonstrate that dual-data fine-tuning significantly enhances precision for long-tail and fine-grained categories. While ensuring high-quality output, the solution features low computational thresholds, robust local deployment, and high scalability, effectively internalizing knowledge organization systems within LLMs. By bridging the gap between classical theory and generative AI, this work provides a high-accuracy, institutionally autonomous solution for automated indexing, offering substantial theoretical and practical significance for the intelligent transformation of digital libraries.
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Yang Xin
none
Renmin University of China
Xin Yang is a Ph.D. candidate in Information Science at the School of Information Resource Management, Renmin University of China. He holds an M.S. in Library Science from Sun Yat-sen University and a B.S. in Archival Science from Sichuan University. His research focuses on knowledge organization, digital humanities, and automated bibliographic cataloging. His recent work involves leveraging large language models (LLMs) and multi-agent workflows to optimize automated library classification systems.
Beyond Mapping: A Semantic Transformation Approach from KORMARC and MODS to BIBFRAME
Authors: Seungmin Lee
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.
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Seungmin Lee
Professor
Chung-Ang University, South Korea
Seungmin Lee is a Professor in the Department of Library and Information Science at Chung-Ang University, Seoul, South Korea. He received his Ph.D. in Information Science from Indiana University Bloomington. His research interests include library classification, metadata, bibliographic ontologies, and knowledge organization. He previously served as Chair of the Cataloging Committee, Chair of the Planning and Policy Committee, and Chair of the Librarian Qualification Committee of the Korean Library Association. His recent research focuses on AI-driven metadata generation and AI literacy.
Beyond Metadata Completeness: A Multidimensional Interoperability Readiness Framework for National Web-Scale Discovery Services
Authors: Dwi Fajar Saputra, Taufik Asmiyanto, Nina Mayesti
National web-scale discovery services (WSDS) depend on the sustained interoperability of institutional repositories to deliver reliable access to scholarly content. Existing evaluations predominantly assess interoperability through metadata completeness at registration, overlooking the operational dimensions that determine long-term integration. This paper introduces the Multidimensional Interoperability Readiness (MIR) framework, which integrates three analytically distinct dimensions: metadata completeness, harvesting sustainability, and metadata capacity. The framework is validated empirically through analysis of the Indonesia One Search (IOS) registry and OAI-PMH harvesting dataset comprising 29 registration fields across four functional categories. Findings reveal a structural decoupling between metadata completeness and harvesting sustainability: most repositories register adequate descriptive metadata but fail to sustain active harvesting over time. Journal repositories demonstrate substantially higher interoperability readiness than dataset and ETD repositories. The MIR framework offers a principled basis for evaluating national discovery infrastructure, with concrete governance implications for repository onboarding, monitoring, and differentiated intervention strategies. This study contributes to the DCMI 2026 theme of Data Integrity and Reliability, arguing that trustworthy discovery infrastructure requires verified, sustained metadata flow—not merely administrative registration.
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Dwi Fajar Saputra
Doctoral Candidate, Information Studies
Faculty of Humanities, Universitas Indonesia
Dwi Fajar Saputra is a doctoral candidate in Information Studies at the Faculty of Humanities, Universitas Indonesia. His research focuses on digital library systems, repository interoperability, metadata quality, and web-scale discovery services. His doctoral research examines the sustainability of national aggregation infrastructure, with particular emphasis on metadata readiness and harvesting continuity in Indonesia One Search as a national web-scale discovery service.
Decolonizing Metadata: Lessons from Stolen Relations’ Controlled Vocabulary Development
Authors: Mairelys Lemus-Rojas, Patrick Rashleigh, Khanh Vo
Metadata should be understood as an interpretive practice and not just as a technical framework for describing digital objects. It holds power and facilitates community engagement. Within the digital humanities arena, metadata plays a pivotal role in narrating and recovering stories that have remained obscured or misrepresented in historical records. This raises a fundamental question: how can we more humanly describe Indigenous communities whose identities and relationships to kinship and culture have been misrepresented in colonial records? This paper examines the role of controlled vocabularies in shaping the representation of Indigenous histories in Stolen Relations’ digital humanities project, positioning metadata as a form of archival intervention. It demonstrates how iterative feedback informs the refinement, implementation, or creation of controlled vocabularies and positions metadata as a space where descriptive practices are examined and reshaped.
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Mairelys Lemus-Rojas
Head of Digital Scholarship
University of Central Florida
Mairelys Lemus-Rojas is the Head of Digital Scholarship at the University of Central Florida Libraries. She oversees Digital Initiatives, Open Scholarship, and the Digital Exploration Center, a digital scholarship hub to learn, engage, and collaborate on digital projects. Previously, she worked as the Head of Open Metadata Production and Initiatives at Brown University. As a strong advocate for open knowledge and an active contributor to Wikimedia projects, Mairelys is committed to democratizing access to information by amplifying the visibility of underrepresented communities.
Does AI-Encoded Meaning Align with Human Meaning?
Authors: Zhenhua Wang, Aixin Yao and Ming Ren
AIs are increasingly used to support metadata processing and investigation, which depends on whether AI-encoded meaning aligns with human meaning. However, AI encodes word meaning through distributional and contextual representations, and it remains unclear whether such representations preserve the meaning value of human system. We answer this question through Zipf’s meaning law, which links word frequency to number of word meanings. We compare multiple AI-induced meaning estimates with human-measured meaning. To quantify alignment, we propose Meaning-Zipf Deviation (MZD), which covers continuous meaning distributions and measures their divergence with reliability adjustment. Extensive experiments show that human words consistently follow Zipf’s meaning law. AI-encoded meanings also exhibit Zipfian regularities, inheriting part of the statistical structure of human language. However, AI meaning distributions remain flatter than human distributions, with lower scaling exponents and non-negligible MZD values. Larger models do not reduce this gap. AI tends to bind words to context-conditioned senses rather than preserve their broader polysemous potential.
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Ming Ren
Vice Dean
School of Information Resources Management, Renmin University of China
Ren Ming is a Professor, Doctoral Supervisor, and Vice Dean at the School of Information Resource Management, Renmin University of China. Her research focuses on big data analytics and applications, AI, and data element markets. She has led multiple national-level research projects, published in leading journals such as JASIST, JOI, TOIS, authored two academic monographs, and led the annual Data Element Marketization Promotion Index report. She serves as a committee member in national and professional societies related to information technology, knowledge organization.
From Multi-Notation Assignment to Faceted Classmark Synthesis in K-KOS: An Exploratory Application of the Integrative Levels Classification with a Classmark Builder
Authors: Ziyoung Park1, Claudio Gnoli, Daniele Morelli
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.
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Ziyoung Park
Professor
Hansung University, South Korea
Ziyoung Park is a professor of Library and Information Science at Hansung University, Seoul, Republic of Korea, where she also serves as Director of the University Library. Her research focuses on knowledge organization systems (KOSs), including the design of classification systems and metadata modeling. She serves as a member of ISKO Italy, an editor for BARTOC, and a program committee member of NKOS. She leads research projects on designing and building registries for Korean KOSs. Her additional work includes developing a bibliographic database of German literature translated into Korean.
Grounding AI Subject Cataloguing in Standards and Policy: An MCP Server for Live LC Authority Lookup and a DITA-Encoded SHM for RAG
Authors: May Chan
Large language models (LLMs) applied to subject cataloguing using Library of Congress Subject Headings (LCSH) tend to generate headings and strings that are syntactically plausible but policy-invalid, bypassing the controlled vocabularies and governing rules that subject collocation depends on. This paper describes a two-track experiment to address the lack of grounding in standards and policy. The first track is lc-vocabularies-mcp, a Model Context Protocol (MCP) server connecting an LLM to live Library of Congress (LC) linked data APIs, enabling real-time authority validation for LCSH and related controlled vocabularies. The second track is a conversion of the Subject Headings Manual (SHM) from PDF to structured DITA (Darwin Information Typing Architecture), designed as a machine-actionable retrieval-augmented generation (RAG) corpus. Together, the two tracks support a five-part subject cataloguing workflow in which non-parametric knowledge is supplied to Claude at each part where parametric knowledge alone is insufficient. The paper reports on the architecture of lc-vocabularies-mcp, the DITA conversion methodology, and an evaluation design that tests whether policy-grounded retrieval improves AI-assisted subject cataloguing.
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May Chan
Head, Metadata Services
University of Toronto
May Chan is Head, Metadata Services at the University of Toronto Libraries, with 17 years of prior experience in public libraries at Vancouver and Burnaby, British Columbia. A Carpentries Instructor Trainer, she is committed to building computational and technical literacy among library practitioners, and has been active in cataloguing training and professional development in a variety of roles throughout her career. May currently serves as co-chair of the PCC Standing Committee on Training and the SCT Linked Data Training Task Group.
Help or hype?: Standardizing date metadata with AI
Authors: Annamarie C. Klose, Scott Goldstein
There is a wide variety of date metadata practices employed by professional, paraprofessional, and volunteer catalogers and metadata creators. The lack of standardization in date metadata inhibits discovery. While normalization routines can be created to improve metadata, the sheer scale of date values and types of date formats can make that time consuming and onerous. As artificial intelligence (AI) is employed in metadata work, this paper addresses its potential usefulness in standardizing date metadata in more frequently used scenarios. In this exploratory study, the researchers employed several freely available AI chatbots: ChatGPT, Claude Sonnet, and Gemini.
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Scott Goldstein
Coordinator, Web Services & Library Technology
McGill University
Scott Goldstein is the Coordinator of Web Services and Library Technology at McGill University. His research interests include metadata quality, digital humanities, technology in libraries, and meta-research.
OpenDataGOV-AP: A Linked Data Application Profile for parliamentary activities
Authors: Tiago Ribeiro de Sá Cruz, João Miguel da Silva Lourenço, Mariana Curado Malta, João Carlos Viegas Martins Bispo
This paper presents OpenDataGOV-AP, a Linked Data Application Profile for modelling parliamentary initiatives and related actors in the Portuguese Parliament. Relevant public information exists but is fragmented across heterogeneous XML and PDF sources, limiting its discoverability, interoperability, and reuse by citizens, journalists, and policy-makers. We propose a data-driven profile structured around four modules — Core, Initiatives, Biographical-Profile, and MP-Activities — that reuses established RDF vocabularies and introduces domain-specific terms via the POLIS ontology where gaps exist. Development followed best practices, and the profile is accompanied by a SHACL file for validation. The resulting knowledge graph was validated through SPARQL queries demonstrating cross-party co-authorship analysis and electorate-normalised legislative output. This work provides a semantic foundation for Linked Open Data publishing, semantic search, and improved transparency of parliamentary information.
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Tiago Ribeiro de Sá Cruz
Student
Faculdade de Engenharia da Universidade do Porto
I am a final-year Master's student in Informatics and Computing Engineering at FEUP. I developed PoliTrack, a mobile app for tracking Portuguese parliamentary proposals and votes, promoting civic awareness. My thesis focuses on parliamentary data, where I converted data to RDF, co-developed a modular domain ontology, and built a hybrid search engine with dense retrieval and query expansion, enabling natural language querying over parliamentary initiatives for non-technical users.
Reconstructing Metadata Literacy in the AI Era: A Conceptual Framework and Educational Reflections for LIS Education
Authors: Ba Xi, Nurussobah Hussin and Hanis Diyana Kamarudin
This paper calls for a rethinking of metadata literacy in the age of AI. Previous discussions often defined metadata literacy as knowledge of descriptive structures or skills in creating and using metadata records. But this understanding is no longer enough – though still necessary – when AI systems create, transform, rank, summarise and recommend information at scale. In such environments, metadata is not simply about post hoc description of resources; it structures provenance, visibility, accountability, cultural representation, and the conditions under which machine outputs can be interpreted and trusted. Using metadata, metadata instruction, AI literacy, and information literacy scholarship, this conceptual paper presents a reconstructed model of metadata literacy for LIS education. The model is built on five dimensions: understanding of infrastructure, contextual description and representation, provenance and disclosure, evaluation of algorithmically mediated outputs and intervention in terms of ethics and governance. The paper also gives examples of learning tasks and assessment evidence to illustrate how the model can be applied. It argues for viewing metadata literacy not as a narrow technical specialisation but as a fundamental educational response to AI-mediated knowledge environments.
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Ba Xi
Faculty of Information Science, Universiti Teknologi MARA
Ba Xi is a PhD candidate in Information Management at the Faculty of Information Science, Universiti Teknologi MARA. She currently works at a university in China. Her research interests include AI literacy, community libraries, and infopreneurship. Her doctoral research focuses on the development of an AI literacy framework for Library Science students in China. Her work explores how emerging AI technologies are reshaping Library Science education, information practice, and the development of students’ professional competencies in academic and professional contexts.
Tapir: A Graphical Editor for Tabular Application Profiles
Authors: Nishad Thalhath, Mitsuharu Nagamori, and Tetsuo Sakaguchi
Application profiles record the local application of metadata vocabulary terms, how they combine, and the values they may take. There are two tabular formats for authoring application profiles. SimpleDSP, from the Metadata Information Infrastructure Construction Project in Japan, has served for over a decade as a tabular form of the Description Set Profile model. DCTAP, from a more recent DCMI working group, approaches the same task from a different starting point. Both are in active use, but practitioners moving between communities have had no single authoring interface that treats the two as peers. This paper compares and contrasts the two formats along the dimensions that shape application profile authoring in practice, and argues that neither is a reduction of the other. Based on this analysis, the authors present Tapir (https://yamaml.github.io/tapir/), a browser-based graphical editor that treats both formats as peers, preserves the bilingual character of SimpleDSP, and runs entirely in the user's browser, so that profile authoring stays within a privacy-oriented interface. Alongside the editor, an updated command-line toolkit and a type-safe authoring route in Apple's Pkl language extend the same profile model. The editor, the toolkit, and the Pkl package are released as open-source software.
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Nishad Thalhath
Technical Scientist
RIKEN
Nishad Thalhath is a researcher in information science with expertise in semantic interoperability, metadata standards, and knowledge graphs. He serves as a Technical Scientist at the RIKEN Center for Integrative Medical Sciences in Japan, where he develops and manages metadata and integration systems for omics data as part of the Laboratory for Large-Scale Biomedical Data Technology. He holds a Master’s degree in Library and Information Science and a PhD in Informatics. He also collaborates with the Metadata Laboratory at the University of Tsukuba’s School of Library, Information and Media Studies. With nearly two decades of experience in information technology, he has worked as a developer, engineer, and consultant, contributing to IT and ITES projects across diverse domains.
Together in Practice: Comparing LCC and DDC Assignment Across Library of Congress Bibliographic Records
Authors: Kai Li, Inkyung Choi, Jessica Yi-Yun Cheng, Zach Jenkins, and Brian Dobreski
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.
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Inkyung Choi
Assistant Professor
Sungkyunkwan University
Inkyung Choi is an Assistant Professor in the Department of Library and Information Science at Sungkyunkwan University. As a current FAIR Fellow, she specializes in metadata architecture, ontology engineering, and knowledge organization, with a focus on implementing FAIR principles to enhance data interoperability and reuse. Her current research focuses on developing a standard-based Knowledge Graph aiming to transform fragmented domain information into sustainable, machine-actionable knowledge infrastructures for AI-driven scientific discovery.
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