Knowledge-intensive text usually contains fruitful entities and complex relationships, such as academic articles and scientific exposition. Reading and comprehending such texts often demands considerable time and mental effort to track the relationships between entities. To reduce the burden, we present GraphTide, a visualization technique that progressively constructs nested entity-relationship graphs with animation to support the understanding of complex text. Our method features an on-demand entity-relationship decomposition pipeline that constructs nested graphs to represent intra- and inter-sentence relationships. Moreover, we propose a structure-aware force-directed layout optimization algorithm to enhance structural clarity. Sentences and their associated entities are incrementally revealed through animated transitions, helping users maintain context as the narrative unfolds. A user study shows that GraphTide significantly improves users' comprehension of knowledge-intensive texts compared to traditional graph-based techniques and static nested graph representations.

翻译：摘要：知识密集型文本通常包含丰富的实体与复杂关系，例如学术论文与科学论述。阅读和理解此类文本往往需要耗费大量时间与心智努力来追踪实体间的关联。为减轻这一负担，我们提出GraphTide——一种通过动画渐进构建嵌套式实体关系图的可视化技术，以支持对复杂文本的理解。该方法的核心是构建按需分解实体关系的流水线，通过生成嵌套图来表征句内与句间关系。此外，我们提出一种结构感知的力导向布局优化算法以增强图的结构清晰度。伴随文本叙述推进，句子及其关联实体通过动画过渡逐步呈现，帮助用户维持语境感知。用户研究表明，与传统的基于图的技术及静态嵌套图表示相比，GraphTide显著提升了用户对知识密集型文本的理解水平。