Subjects: Library Science，Information Science >> Information Science submitted time 2023-04-01 Cooperative journals: 《图书情报工作》
Abstract： [Purpose/significance] The fragmented and unstructured information of the epidemic brings challenges to emergency decision-making. To support the digitization of emergency decision-making and to promote intelligent emergency management, combining natural language processing and event ontology to realize the automation of epidemic information management and knowledge representation. [Method/process] An automatic construction strategy of domain ontology knowledge base based on web crawler, natural language processing, and event ontology was proposed. First, web crawlers and natural language processing were used for information collection and automatic extraction of event elements, and an epidemic event ontology model was built on this basis. Then, the algorithms for the ontology construction and update were designed, and the automatic construction and expansion for the event ontology was completed by them. [Result/conclusion] The results show that the proposed strategy has the feasibility of dynamic management and automatic update of epidemic information, and event ontology can describe events effectively and create conditions for knowledge expansion. This study also provides a reference for the research and practice of emergency decision-making.
Subjects: Library Science，Information Science >> Information Science submitted time 2018-10-19 Cooperative journals: 《智库理论与实践》
Abstract： [Purpose/significance] Think tanks research in the big data environment aims at collecting, processing, analyzing, and digging out the logical rules contained in the massive information effectively, and representing large amounts of irregular data by visualization methods, which is a hot topic of research on think tanks. [Method/process] This article started with analyzing the current research status of think tanks and the big data visualization at home and abroad, and related theories of think tanks research methods based on big data visualization were sorted out. The application and innovation of big data visualization methods in supporting think tanks research were analyzed, and CiteSpaceⅡ software was used to analyze the research status of think tanks research in China. [Result/conclusion] The results show that the research methods of think tanks in China were relatively simple at present, and big data visualization research has two features: multi-disciplinary and multi-industry. Think tanks research methods based on big data visualization have developed rapidly, including techniques of the massive data process, the visualization algorithm, the graphic visualization, the human-computer interaction, the distributed processing and so on, which can support the big data visualization analysis in think tanks research. The application of big data visualization analysis in think tanks research can help think tanks process data efficiently, make decisions scientifically and accurately, visualize complex graphics, simplify complex problems, enhance users’ interaction well, agglomerate and share resources, and enhance their role and influence in the application research of think tanks.
Subjects: Library Science，Information Science >> Information Science submitted time 2017-10-27 Cooperative journals: 《智库理论与实践》
Abstract：摘要：[目的/意义]大数据环境下的智库研究主要针对海量的信息进行有效采集、处理、分析，挖掘信息中蕴含的逻辑规律，并且将大量的无规则数据可视化表示，这是当今智库研究的热点。[方法/过程]文章通过分析国内外智库和大数据可视化研究现状，整理了目前大数据可视化的智库研究方法的相关理论。分析大数据可视化方法在智库研究中的应用与创新，并运用CiteSpaceⅡ软件分析我国智库研究现状。[结果/结论]结果表明：我国目前对智库研究的方法还比较单一，大数据可视化研究涉及多学科和行业应用。大数据可视化的智库研究方法发展迅速，包括支持智库大数据可视化分析的海量数据处理、可视化算法、图形可视化、人机交互、分布式处理等技术。大数据可视化分析在智库研究中的应用有助于智库数据高效处理、决策科学准确化、复杂图形可视化、复杂问题简化、用户交互良好、资源集聚共享、提升智库作用和影响力。
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