IEEE Trans Vis Comput Graph. 2016 Jan;22(1):190-9. doi: 10.1109/TVCG.2015.2467621.
The exploration and analysis of scientific literature collections is an important task for effective knowledge management. Past interest in such document sets has spurred the development of numerous visualization approaches for their interactive analysis. They either focus on the textual content of publications, or on document metadata including authors and citations. Previously presented approaches for citation analysis aim primarily at the visualization of the structure of citation networks and their exploration. We extend the state-of-the-art by presenting an approach for the interactive visual analysis of the contents of scientific documents, and combine it with a new and flexible technique to analyze their citations. This technique facilitates user-steered aggregation of citations which are linked to the content of the citing publications using a highly interactive visualization approach. Through enriching the approach with additional interactive views of other important aspects of the data, we support the exploration of the dataset over time and enable users to analyze citation patterns, spot trends, and track long-term developments. We demonstrate the strengths of our approach through a use case and discuss it based on expert user feedback.
科学文献集合的探索和分析是有效知识管理的一项重要任务。过去对这些文献集的兴趣促使人们开发了许多用于其交互分析的可视化方法。这些方法要么关注出版物的文本内容，要么关注包括作者和引文在内的文档元数据。之前提出的引文分析方法主要旨在可视化引文网络的结构及其探索。我们通过提出一种用于科学文献内容的交互式可视分析的方法来扩展现有技术，并将其与一种新的灵活技术相结合，以分析其引文。该技术使用高度交互的可视化方法，通过将引用出版物的内容链接起来，方便用户引导引文的聚合。通过使用其他重要数据方面的附加交互视图来丰富该方法，我们支持随时间探索数据集，并使用户能够分析引文模式、发现趋势和跟踪长期发展。我们通过一个用例展示了我们方法的优势，并根据专家用户的反馈进行了讨论。
