AI-Powered Bibliographic Control: Automating Cataloging, Standardization, and Data Capture

dc.audienceAudience::Bibliography Section
dc.audienceAudience::Information Technology Section
dc.audienceAudience::Artificial Intelligence Special Interest Group
dc.contributor.authorDiaz, Claudio Daniel Henriquez
dc.contributor.authorVergara, María Paz Rioseco
dc.date.accessioned2025-10-19T14:27:50Z
dc.date.available2025-10-19T14:27:50Z
dc.date.issued2025-10
dc.description.abstractArtificial Intelligence (AI) optimizes bibliographic control by automating cataloging workflows, capturing data via mobile OCR, and normalizing records to established standards. Universal Bibliographic Control demands solutions that address the exponential growth of documentary output and the requirement for immediate access. AI accelerates record creation and review while maintaining consistency, even in resource-constrained environments. A human-in-the-loop system was implemented: librarians validate and correct AI-generated entries, reinforcing accountability and accuracy. Algorithmic transparency is ensured by documenting the AI’s criteria and decisions, creating an audit trail that facilitates suggestion evaluation and bias detection. Three use cases demonstrate the impact: 1. Automated Cataloging of Theses and Monographs. AI models segment PDF documents, extract essential fields, and generate MARC 21/RDA records. Average accuracy exceeding 93 % Cataloging time reduced from 30 minutes to under 5 minutes per item 2. Mobile Application for Image-Assisted Cataloging. Android devices capture cover pages; AI employs OCR and computer vision to propose normalized metadata. Provisional records are integrated into the bibliographic system via REST API. 3. Correction and Normalization of Historical Records. Models trained on RDA rules and authority lists detect inconsistencies and propose automatic or semi-automatic corrections. Over 40 000 records updated, 58 % improvement in access-point consistency, Reduction in authority collisions. Results include precision metrics, labor-hour savings, and seamless REST API integration with library management systems. This combination of advanced technology, governance, and measurable outcomes charts a roadmap for libraries to navigate new horizons of automation and governance effectively and securely. (presented on 15 August 2025, at "Metadata's New Frontiers: AI-Driven Systems and Standards" session)
dc.identifier.urihttps://www.ifla.org/events/artificial-intelligence-bibliographic-control-and-legal-matters-navigating-new-horizons/
dc.identifier.urihttps://2025.ifla.org/bibliography-section-with-the-information-technology-section-and-the-ifla-artificial-intelligence-special-interest-group/
dc.identifier.urihttps://wlic2025.astanait.edu.kz/
dc.identifier.urihttps://repository.ifla.org/handle/20.500.14598/6853
dc.language.isoeng
dc.publisherInternational Federation of Library Associations and Institutions (IFLA)
dc.relation.ispartofseries89th IFLA World Library and Information Congress (WLIC), 2025 Astana
dc.relation.ispartofseriesWLIC 2025, Astana, Satellite Meeting: Artificial Intelligence, Bibliographic Control and Legal Matters: Navigating New Horizons
dc.rights.holderInternational of Library Associations and Institutions (IFLA)
dc.rights.licenseCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.subjectArtificial intelligence
dc.subjectBibliographic control
dc.subjectAutomation
dc.subjectCataloguing
dc.subjectMetadata
dc.subjectBibliographic data
dc.subjectAuthority data
dc.titleAI-Powered Bibliographic Control: Automating Cataloging, Standardization, and Data Capture
dc.title.alternativeControl bibliográfico impulsado por IA: automatización de la catalogación, la estandarización y la captura de datos
dc.typeEvents Material
ifla.UnitSection::Bibliography Section
ifla.UnitSection::Information Technology Section
ifla.UnitSpecial Interest Group::Artificial Intelligence Special Interest Group
ifla.oPubId0

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