Subjects: Management Science >> Science ology and Management submitted time 2023-08-23 Cooperative journals: 《中国科学院院刊》
Abstract： The generative large model GPT represented by ChatGPT is developing rapidly, which has aroused extensive discussion in academic circle and the industry and has an incalculable impact on foundational scientific research development. The study first sorts out the development of the GPT technological revolution, and discusses the new changes brought about by this technology in scientific research. Then, based on the three aspects of application status, core principles and innovation subjects, the impact of the GPT technological revolution on basic scientific research and its development suggestions for China are discussed. The study believes that GPT technology can certainly play a positive role in knowledge production, improve scientific research efficiency, and even promote scientific research paradigm changes, but it may also cause scientific research misconduct, weaken research credibility, and amplify the inherent bias of the Internet and other issues. Therefore, the study discusses how to develop Chinese foundational scientific research based on GPT technology. On the one hand, investing in the research and development of data and computing platforms that are independently controllable by the country and protected by intellectual property rights. On the other hand, emphasizing humancomputer collaboration and scientific research integrity supervision to create an open and transparent environment for Al development. In short, this study aims to provide policymakers and front-line researchers with an understanding perspective on the impact of GPT technology on fundamental science, promote the rational use of GPT technology, and provide a reference for the healthy development of the future academic ecology.
Subjects: Library Science，Information Science >> Information Science submitted time 2023-06-12
Abstract： Objective Systematically sort out and summarize the operation process of scientific research data in the scientific research process under the trend of Intelligent Scientific Research, mine the potential scientific research data demand, and provide thinking for the transformation and development of scientific research data services under the new trend.
Methods Under the guidance of the theory of scientific research data life cycle, taking the field of materials and chemistry as an example, this paper analyzes how scientific research data can be transformed into knowledge in the intelligent research of scientific research, and constructs six stages of scientific research data life cycle operation process, including data management plan, data generation and collection, data processing and analysis, data generation and publication, data storage and sharing, and data reuse, so as to explore the role and potential needs of scientific research data.
Results The research on intelligent scientific research demonstrates the exploration of multi-source heterogeneous data integration, fine-grained data structuring, human-machine interaction language representation, data association mining, and the enrichment of scientific research data types.
Conclusions It is recommended to strengthen the construction of high-quality and comprehensive data networks in the field of scientific research data services in the future, deepen embedded scientific research data services, enhance the knowledge and artificial intelligence literacy of librarians in the field, attach importance to the mining of experimental information in textual data, and pay attention to the exploration of human-machine interaction language.
Peer Review Status:
Awaiting Review
Subjects: Library Science，Information Science >> Information Science submitted time 2023-04-01 Cooperative journals: 《图书情报工作》
Abstract： [Purpose/significance] This paper aims to extract the tacit knowledge from the massive literatures by constructing a two-mode complex network model. [Method/process] Through the NetworkX complex network toolkit, a two-mode complex network model was constructed based on the co-occurrence relationship of any two nodes. The direct relationship between nodes and nodes was extracted by weighting the co-occurrence relationship of nodes in the network model, calculating the topology information of the network and AP clustering. The most appropriate prediction algorithm was selected by using AUC method to evaluate the four link prediction algorithms, such as AA, JC, wAA and wJC. The tacit knowledge was predicted by the most appropriate prediction algorithm from the complex networks. [Result/conclusion] The results showed that the wAA link prediction algorithm was the optimal link prediction algorithm. The two mode complex network model, indicators and method system were effective in drug target mining in the Chemical Abstracts Service database. The next step is to try in other databases or other research fields to further verify the generality and effectiveness of the model.
Subjects: Library Science，Information Science >> Information Science submitted time 2023-04-01 Cooperative journals: 《图书情报工作》
Abstract： [Purpose/significance] This paper try to propose a scientific, effective and reusable method to identify key technologies based on the perspective of intelligence research. It aims to provide information support for nation, regions, enterprises and innovative institutions to discover, deploy and promote the prospective R&D of key technologies.[Method/process] Based on the definition of key technology and its types, this paper used K-means++ algorithm to cluster scientific papers to identify hotspot technologies. Then it used the hotspot technologies as nodes to construct and visualize complex network through secondary clustering and Gephi. Structural holes theory was adopted to analysis the network and attributes of nodes, and thereby selected generic technologies. Link prediction algorithm was used to predict the missing edges in the network according to the structure, and we can identify the potential emerging technologies based on the phenomenon of cross-fusion of hot technologies to promote the formation of innovative technologies.[Result/conclusion] Taking the Intelligent Manufacturing as an example to carry out empirical research on the method, and validated the operability and effectiveness of the method through national authoritative documents and literature research.
Subjects: Library Science，Information Science >> Information Science submitted time 2023-04-01 Cooperative journals: 《图书情报工作》
Abstract： [Purpose/significance] In order to meet the needs of classifying massive patent automatically in current patent examination and patent information analysis work, this paper studies a series of patent automatic classification methods based on deep learning and compares the classification effects. This will promote the efficiency and effectiveness of patent classification. [Method/process] Aiming at the shortcoming of traditional machine learning methods, 7 deep learning models was designed, including Word2Vec+TextCNN, Word2Vec+GRU, Word2Vec+BiGRU, Word2Vec+ BiGRU+TextCNN and so on. These models based on the deep learning technology, such as Word2Vec, CNN, RNN, Attention mechanism and so on and considered the characteristics of patent text word order, context features and other key features in classification. Selecting the ‘Section’ of main International Patent Classification (IPC) was as the class labels, the study classified the Chinese patents by above 7 deep learning models and 3 traditional machine learning methods. And there was a comparison about the effect of classification in different models. [Result/conclusion] The empirical research indicated that it reached the better effect of Chinese patent classification by using deep learning methods which considered the characteristics of patent text word order, context features and other key features in classification.
Subjects: Medicine, Pharmacy >> Clinical Medicine submitted time 2022-07-01 Cooperative journals: 《中国全科医学》
WEI Xu
LIU Ning
ZHANG Yili
HAN Tao
SUN Kai
QI Baoyu
CHEN Xin
QIN Xiaokuan
GAO Jinghua
XIE Yanming
ZHU Liguo
Abstract：
The Patients with osteoporosis (OP) often suffer from a variety of comorbidities, including endocrine, circulatory, respiratory, urinary, immune, skeletal muscle, nerve and other multi system diseases. The incidence of OP comorbidity is high, and these comorbidities may aggravate osteoporosis and increase the risk of osteoporotic fracture, seriously affect the quality of patient's life, and make the clinical management more complex. So the situation brings a heavy burden to the families and society. At present, there is a lack of overall research on osteoporosis and its comorbidities. The existing research strategies are difficult to effectively guide clinicians to carry out comorbidity management in terms of common causes, common prevention and common treatment. In view of this, it is suggested to introduce the concept of multidisciplinary integrated treatment (MDT), and strengthen the understanding of osteoporosis related comorbidities and their pathogenesis. Patients who may be complicated with osteoporosis should be screened as early as possible. Once osteopenia and osteoporosis are found, active prevention and intervention should be carried out to reduce the risk of fracture. Early screening, early diagnosis and early treatment is necessary to realize the prevention and treatment of OP comorbidities.
Subjects: Library Science，Information Science >> Information Science submitted time 2017-11-08 Cooperative journals: 《数据分析与知识发现》
Abstract：【目的】结合链路预测与机器学习, 提出推荐未来科研合作的新方法, 以提高单独基于链路预测方法的推荐精确度。【方法】构建加权作者合作网, 以不同的链路预测指标作为特征输入, 运用极端随机树(Extremely Randomized Trees, ET)机器学习算法训练分类, 并利用遍历算法求取分类结果的最优权重组合, 选取TOP 准确度的预测作为合作推荐结果。【结果】选取纳米科技领域2008 年–2010 年SCI 论文数据进行实证。在城市合作推荐中, 改进的ET 方法优于已有方法, 有良好的推荐成功率; 预测方法受网络结构等因素影响较小, 适用范围更广泛。【局限】科研合作受合作动机、地域、语言等诸多因素影响, 加权作者合作网没有反映在一篇论文中同城市、同机构的多个作者, 也没有反映上述因素。【结论】改进算法能够比单个预测指标产生更准确的合作推荐建议, 也为推广到大学等机构、个人等更微观的应用层面提供参考。
Subjects: Library Science，Information Science >> Information Science submitted time 2017-11-08 Cooperative journals: 《数据分析与知识发现》
Abstract：【目的】降低文献–作者二分网络在投影为合著网络过程中的信息丢失影响, 形成适应特定二分网络的合著关系预测指标和方法, 提高预测准确率和结果可解释性。【方法】首先构建文献–作者二分网络及其投影合著网络; 接着抽取二分网络中的二阶路径和三阶路径表示作者间的关联关系; 最后利用逻辑回归方法学习不同路径对于合著关系预测的贡献, 由此形成文献–作者二分网络中基于路径组合的合著关系预测指标。【结果】在图书情报领域的实验证实, 文献–作者二分网络在投影为合著网络过程中存在较大的信息丢失, 并以合著关系预测准确率变化进行定量计算; 逻辑回归方法适合学习不同路径对于合著关系预测的贡献, 由此形成的路径组合指标准确率远远高出其他指标, 并且预测结果更易解释。【局限】其他的多阶路径尚未引入到该模型中, 方法通用性还需在其他领域进行验证。【结论】合著关系预测应直接在文献–作者二分网络上进行, 以降低投影过程中的信息丢失影响; 文献–作者二分网络上的路径组合指标是合著关系预测的最优指标; 该方法可扩展应用到其他类型的二分网络中, 如专利–发明人二分网络。
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