RESEARCH PAPERS

The Content Transformation Path of Library Metadata from the Perspective of Smart Metadata

  • Guo Limin ,
  • Liu Yueru ,
  • Fu Yaming
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  • 1 Shanghai Library/Institute of Scientific & Technical Information of Shanghai, Shanghai 200031;
    2 Tongji University Library, Shanghai 200092;
    3 School of Cultural Heritage and Information Management, Shanghai University, Shanghai 200444
Guo Limin, Associate Research Librarian, Master’s Degree; Liu Yueru, Associate Research Librarian, Master’s Degree, Corresponding Author, E-mail: monikalyr@tongji.edu.cn; Fu Yaming, Assistant Research Fellow, PhD.

Received date: 2025-07-01

  Revised date: 2025-12-29

  Online published: 2026-05-07

Supported by

This work is supported by Shanghai Magnolia Talent Program Pujiang Project titled “Research on Cultural Heritage Knowledge Service Based on Generative Artificial Intelligence” (Grant No. 24PJC036).

Abstract

[Purpose/Significance] Although traditional metadata approaches have mainly focused on the organization, classification, and reuse of resources, the library metadata system is now undergoing a profound transformation from static description to dynamic semantic organization under the pervasive integration of artificial intelligence technologies. Focusing on smart metadata as the core concept, this paper aims to define its capability boundaries and technical connotations, and to explore reconstruction paths and supporting frameworks for library metadata in a generative AI environment. [Method/Process] On the basis of reviewing practical applications of large language models in resource generation, semantic organization, content adaptation, computational friendliness, and service integration, the paper proposes a five-dimensional capability model of smart metadata and constructs a three-layer technical architecture comprising a data layer, semantic layer, and application layer. Furthermore, the book Aesthetic Education: Starting from Teaching Children to Paint is selected as a case, and a chapter-level semantic modeling experiment is conducted to examine the feasibility of smart metadata in semantic extraction, structured generation, and support for generative tasks. [Result/Conclusion] The findings indicate that smart metadata can, with minimal human intervention, achieve automatic extraction and structured representation of chapter-level semantic elements, showing clear application potential in key library functions such as cataloging optimization, semantic retrieval, and knowledge services. At the same time, due to the lack of a unified standards system and systematic evaluation framework, there is an urgent need to strengthen the normative construction of semantic infrastructure through interdisciplinary collaboration, so as to better support the intelligent evolution of libraries in the era of generative artificial intelligence.

Cite this article

Guo Limin , Liu Yueru , Fu Yaming . The Content Transformation Path of Library Metadata from the Perspective of Smart Metadata[J]. Library and Information Service, 2026 , 70(10) : 95 -107 . DOI: 10.13266/j.issn.0252-3116.2026.10.008

References

[1] GREENBERG J. Big metadata, smart metadata, and metadata capital: toward greater synergy between data science and metadata[J]. Journal of data and information science, 2017, 2(3): 19-36.
[2] NISO. Understanding metadata[EB/OL]. [2025-12-05]. https://groups.niso.org/higherlogic/ws/public/download/17446/Understanding%20Metadata.pdf.
[3] ULRICH H, KOCK-SCHOPPENHAUER A K, DEPPENWIESE N, et al. Understanding the nature of metadata: systematic review[J]. Journal of medical internet research, 2022, 24(1): e25440.
[4] LEIPZIG J, NÜST D, HOYT C T, et al. The role of metadata in reproducible computational research[J]. Patterns, 2021, 2(9): 1-28.
[5] GREENBERG J, WU M, LIU W, et al. Metadata as data intelligence[J]. Data intelligence, 2023, 5(1): 1-5.
[6] OYIGHAN D, UKUBEYINJE E S, DAVID-WEST B T, et al. The role of AI in transforming metadata management: insights on challenges, opportunities, and emerging trends[J]. Asian journal of information science and technology, 2024, 14(2): 20-26.
[7] 吴跃. AACR2与RDA的联系及在图书著录部分的区别[J]. 大学图书馆学报, 2010, 28(4): 77-83. (WU Y. From AACR2 to RDA— The difference between AACR2 and RDA in monograph description[J]. Journal of academic libraries, 2010, 28(4): 77-83.)
[8] 胡小菁. BIBFRAME核心类演变分析[J]. 中国图书馆学报, 2016, 42(3): 20-26. (HU X J. Evolution of BIBFRAME core classes[J]. Journal of library science in China, 2016, 42(3): 20-26.)
[9] YANG W, FU R, AMIN M B, et al. Impact and influence of modern AI in metadata management[J]. arXiv preprint arXiv:2501.16605, 2025.
[10] ZAVALIN V, ZAVALINA O L. Are we there yet? Evaluation of AI-generated metadata for online information resources[J]. Information research an international electronic journal, 2025, 30(iConf): 732-740.
[11] 郭利敏, 付雅明, 刘悦如. 检索增强思维在图书馆知识挖掘服务中的应用研究[J]. 图书情报工作, 2025, 69(21): 108-119. (GUO L M, FU Y M, LIU Y R. Research on the application of retrieval augmented thoughts in library knowledge mining services[J]. Library and information service, 2025, 69(21): 108-119.)
[12] 刘炜, 刘倩倩, 付雅明, 等. 人工智能时代的元数据方法论[J]. 图书馆理论与实践, 2023(4): 16-29. (LIU W, LIU Q Q, FU Y M, et al. Metadata methodology in artificial intelligence era[J]. Library theory and practice, 2023(4): 16-29.)
[13] SINGH M, KUMAR A, DONAPARTHI S, et al. Leveraging retrieval augmented generative LLMs for automated metadata description generation to enhance data catalogs[J]. arXiv preprint arXiv:2503.09003, 2025.
[14] XU Y, LIU Z, CHEN J, et al. Multi-modal knowledge graph generation with semantics-enriched prompts[J]. arXiv preprint arXiv:2504.13631, 2025.
[15] LEE J, WANG Y, LI J, et al. Multimodal reasoning with multimodal knowledge graph[J]. arXiv preprint arXiv:2406.02030, 2024.
[16] PENG J, HU X, HUANG W, et al. What is a multi-modal knowledge graph: a survey[J]. Big data research, 2023, 32: 100380.
[17] LU Y, ZHAO W, SUN N, et al. Enhancing multimodal knowledge graph representation learning through triple contrastive learning[C]//Proceedings of the thirty-third international joint conference on artificial intelligence. Jeju: International joint conferences on artificial intelligence, 2024: 5963-5971.
[18] LI C, DENG Y, HU H, et al. Incorporating external knowledge and goal guidance for LLM-based conversational recommender systems[J]. arXiv preprint arXiv:2405.01868, 2024.
[19] 罗鹏程, 王继民, 聂磊. 基于生成式大语言模型的文献资源自动分类研究[J]. 情报理论与实践, 2024, 47(12): 174-182. (LUO P C, WANG J M, NIE L. Research on automatic classification of literature resources based on generative large language model[J]. Information studies: theory & application, 2024, 47(12): 174-182.)
[20] 方卿, 陈瑞. ChatGPT下科技期刊语义出版的变革[J]. 图书情报工作, 2024, 68(18): 4-12. (FANG Q, CHEN R. The changes in semantic publishing of scientific journals under ChatGPT[J]. Library and information service, 2024, 68(18): 4-12.)
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