| 范炜,曾蕾.AI新时代面向文化遗产活化利用的智慧数据生成路径探析[J].中国图书馆学报,2024,50(2):4~29 |
| AI新时代面向文化遗产活化利用的智慧数据生成路径探析 |
| Exploring the Generation Path of Smart Data for the Activation and Utilization of Cultural Heritage in the New Era of AI |
| 投稿时间：2024-01-08 修订日期：2024-02-18 |
| DOI： |
| 中文关键词: 人工智能 文化遗产 活化利用 智慧数据 生成路径 |
| 英文关键词: Artificial intelligence Cultural heritage Activation and utilization Smart data Generation path |
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| 摘要点击次数: 169 |
| 全文下载次数: 113 |
| 中文摘要: |
生成式人工智能引爆AI新时代,新技术不断涌现并快速迭代更新,AI技术应用呈现出百花齐放、百家争鸣的繁荣发展局面。借助AI发展东风,智慧数据的生成进入了高效、深化、多模态集成的新阶段,提升了数据驱动的文化遗产活化利用创新手段和创新形式的丰富度与可行性。本文旨在探索面向文化遗产活化利用的智慧数据生成路径。首先,从AI技术视角,对文化遗产智慧数据的内涵与价值进行回顾并知新；其次,系统分析从多元异构数据资源中生成智慧数据的典型做法；再次,以羌年为例,探讨非遗活态文化的智慧数据生成思路。最后,总结归纳AI赋能文化遗产智慧数据生成路径的四点参考策略:①抓住AI赋能机遇,补齐数据基础设施短板,加强数据资源体系建设；②尽快开展馆藏数据资源的“大语言模型+知识库”结合工作,实现智能分析与计算增强；③鼓励更广泛的文化遗产数据开放与共享,支持活化利用的创新应用；④确保可信的智慧数据。图3。表1。参考文献68。
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| 英文摘要: |
With innovative technologies emerging and undergoing rapid iterative updates, the landscape of AI is constantly evolving, encompassing a multifaceted ecosystem that is beyond natural language based processing and data generating.
Based on the smart data construction scenarios of cultural memory institutions such as galleries, libraries, archives, and museums (GLAMs), this article explores the possibilities, challenges, and paths of the activation, utilization, and innovative inheritance of cultural heritage through generating smart data which has now entered a new phase characterized by high efficiency, in depth analysis, and multi modal integration in the new era of AI.
The paper first revisits the concept of smart data for cultural heritage. To consider the diverse features of cultural heritage data, the paper introduces the CARE principles (Collective benefit, Authority to control, Responsibility, and Ethics) which complement the existing FAIR principles (Findable, Accessible, Interoperable, and Reusable) by encouraging open, sustainable, and ethical data movements. Smart data can be efficiently processed using AI technology while also being used by AI to generate intelligent applications and produce high quality data. Such advancement makes applications for promoting cultural heritage more abundant and practical.
The diversity of cultural heritage presents complexities in data processing. This paper sorts five generative patterns for diverse and heterogeneous cultural heritage resources: 1) machine readable text data, including born digital or turned to digital, unstructured and semi structured; 2) non textual data, such as images, sound, scents, objects, etc; 3) hybrid data contributed by the community; 4) specialized knowledge integration and association; and 5) national level knowledge networks embracing all types of resources. The generation of smart data for GLAMs involves the processes with data structuring, ontology generating, and linked data driven semantic association and enrichment, culminating in AI enabled semantic computing.
The paper also emphasizes that human intelligence remains crucial in the data modeling and encoding of cultural heritage resources. Human oversight is essential to ensure the credibility, trustiness, and interpretability of the results.
Intangible cultural heritage reflects the passion and commitment of this paper. AI has the potential to enhance the efficiency and depth of salvage record keeping while assisting in the protection of digital archives, the creation of knowledge graphs, and the innovative dissemination and creative transformation of intangible cultural heritage.
Balancing technological advancements with cultural sensitivity will be the key to shaping a future where technology and heritage coexist harmoniously. To overcome the challenges and unlock the full potential of AI in cultural heritage preservation and dissemination, generate trustworthy smart data, and advance innovative AI powered applications, continuous investment in cross disciplinary research in digital humanities will be essential. 3 figs. 1 tab. 68 refs.
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