Metadata Evolution: Adapting with Linked Data, BIBFRAME, and AI
Title
Metadata Evolution: Adapting with Linked Data, BIBFRAME, and AI
Subject
Description
Libraries today face rapid technological and landscape changes, necessitating a proactive approach to metadata management. This poster explores practical strategies for adapting to these shifts through the implementation of linked data and artificial intelligence (AI) in metadata workflows. The objective is to illustrate practical applications of these technologies in enhancing access and discovery of library collections.
The poster highlights ongoing efforts to integrate linked data into diverse collections, including the digital humanities project People, Religion, Information Networks, and Travel (PRINT), as well as theses and dissertations and other collections, using both Dublin Core and MARC-based records. It will discuss the library’s experiments with the linked data editor Sinopia, its integration with the Alma sandbox, and the resulting display in Primo. Furthermore, it investigates AI-driven approaches, such as generating Faceted Application of Subject Terminology and keywords and automating metadata creation.
These initiatives underscore the importance of collaboration within the library community, continuous professional development, and a proactive approach to emerging technologies. Key takeaways include practical applications of applying linked data for both digital and traditional collections, insights into Sinopia and its integration with Alma, the benefits of collaborative metadata work, the potential of AI in metadata work, and the necessity of ongoing learning and exploration to stay relevant in a rapidly evolving library environment.
The poster highlights ongoing efforts to integrate linked data into diverse collections, including the digital humanities project People, Religion, Information Networks, and Travel (PRINT), as well as theses and dissertations and other collections, using both Dublin Core and MARC-based records. It will discuss the library’s experiments with the linked data editor Sinopia, its integration with the Alma sandbox, and the resulting display in Primo. Furthermore, it investigates AI-driven approaches, such as generating Faceted Application of Subject Terminology and keywords and automating metadata creation.
These initiatives underscore the importance of collaboration within the library community, continuous professional development, and a proactive approach to emerging technologies. Key takeaways include practical applications of applying linked data for both digital and traditional collections, insights into Sinopia and its integration with Alma, the benefits of collaborative metadata work, the potential of AI in metadata work, and the necessity of ongoing learning and exploration to stay relevant in a rapidly evolving library environment.
Creator
Deng, Sai
Piascik, Jeanne
Publisher
Chinese American Librarians Association
Date
2025-06-29
Format
application/pdf
application/x-mspublisher
Language
eng
Type
Text; Poster
Extent
36 × 24 in.
Medium
Paper; Digital
Bibliographic Citation
Deng, S., Piascik, J. (2025). Metadata evolution: adapting with linked data, BIBFRAME, and AI. The Chinese American Librarians Association (CALA) Annual Conference 2025 poster session. June 29, 2025.
Position: 8 (794 views)
Files
Collection
Citation
Deng, Sai and Piascik, Jeanne, “Metadata Evolution: Adapting with Linked Data, BIBFRAME, and AI,” CALASYS - CALA Academic Resources & Repository System, accessed February 17, 2026, https://ir.cala-web.org/items/show/1483.