关键词: 智慧数据, 合成数据, 数据驱动, 图书馆, 知识服务

Abstract:

[Purpose/Significance] Data are playing an increasingly prominent role in driving library knowledge services, providing core support for enhancing service potential and improving accuracy. At present, the intelligent transformation of library knowledge services is hindered by issues such as insufficient resource value mining, limited service interactivity, and poor user privacy protection. These problems seriously restrict improvements to service efficiency and the transformation process. Focusing on library knowledge services empowered by the collaboration of smart data and synthetic data, this paper aims to solve the above transformation dilemmas, providing theoretical support and practical guidance for libraries to optimize knowledge service models, enhance resource utilization efficiency, and strengthen privacy protection, so as to further advance the in-depth development of intelligent transformation. [Method/Process] Based on literature research through keyword retrieval and citation tracing, we sorted out the expressions of smart data in four subfields of information resource management, namely digital humanities, library services, scientific and technical information, and archival studies. We then clarified the connotation of smart data-empowered library knowledge services from three dimensions: service concept, driving process, and value realization, and reviewed the definition and generation methods of synthetic data. On this basis, we discussed the value manifestations of smart data in empowering library knowledge services, and drew on the research and application of synthetic data in other fields to explore its potential value in empowering library knowledge services. Finally, we analyzed the advantages and disadvantages of the two types of data, and discussed the direction of their collaborative empowerment of library knowledge services. [Results/Conclusions] Smart data have advantages in terms of data sources, credibility, and value-added properties. Supported by high-quality data and algorithmic models, synthetic data can expand data scale and diversity, and alleviate challenges in privacy protection to a certain extent. The two complement each other, and their value in collaboratively empowering library knowledge services is mainly reflected in the knowledge resource layer, knowledge interaction layer, and data security layer. However, in the process of collaborative empowerment, they still face practical difficulties such as insufficient interpretability of synthetic data and disputes over data property rights. In the future, libraries should focus on strengthening the credibility of synthetic data in data generation and quality evaluation, clarify the boundaries of rights and responsibilities through contractual agreements and authorization protocols, and improve the transparency of data property rights tracing by integrating blockchain technology, so as to fully release the collaborative value of the two and build a more resilient library knowledge service system.

Key words: smart data, synthetic data, data-driven, library, knowledge service