E2Storyline: Visualizing the Relationship with Triplet Entities and Event Discovery

IF 7.2 4区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yunchao Wang, Guodao Sun, Zihao Zhu, Tong Li, Ling Chen, Ronghua Liang
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引用次数: 0

Abstract

The narrative progression of events, evolving into a cohesive story, relies on the entity-entity relationships. Among the plethora of visualization techniques, storyline visualization has gained significant recognition for its effectiveness in offering an overview of story trends, revealing entity relationships, and facilitating visual communication. However, existing methods for storyline visualization often fall short in accurately depicting the specific relationships between entities. In this study, we present E2Storyline, a novel approach that emphasizes simplicity and aesthetics of layout while effectively conveying entity-entity relationships to users. To achieve this, we begin by extracting entity-entity relationships from textual data and representing them as subject-predicate-object (SPO) triplets, thereby obtaining structured data. By considering three types of design requirements, we establish new optimization objectives and model the layout problem using multi-objective optimization (MOO) techniques. The aforementioned SPO triplets, together with time and event information, are incorporated into the optimization model to ensure a straightforward and easily comprehensible storyline layout. Through a qualitative user study, we determine that a pixel-based view is the most suitable method for displaying the relationships between entities. Finally, we apply E2Storyline to real-world data, including movie synopses and live text commentaries. Through comprehensive case studies, we demonstrate that E2Storyline enables users to better extract information from stories and comprehend the relationships between entities.

e2故事线:可视化与三重实体和事件发现的关系
事件的叙事进程,演变成一个连贯的故事,依赖于实体与实体之间的关系。在众多的可视化技术中,故事情节可视化因其在提供故事趋势概述、揭示实体关系和促进视觉交流方面的有效性而获得了显著的认可。然而,现有的故事线可视化方法往往不能准确地描述实体之间的特定关系。在本研究中,我们提出了e2故事线,这是一种新颖的方法,强调布局的简单性和美学,同时有效地向用户传达实体与实体之间的关系。为了实现这一点,我们首先从文本数据中提取实体-实体关系,并将它们表示为主语-谓词-对象(SPO)三元组,从而获得结构化数据。在考虑三种设计需求的基础上,建立了新的优化目标,并利用多目标优化技术对布局问题进行建模。上述的SPO三元组,连同时间和事件信息,被纳入优化模型,以确保一个简单易懂的故事情节布局。通过定性用户研究,我们确定基于像素的视图是显示实体之间关系的最合适方法。最后,我们将e2故事线应用于现实世界的数据,包括电影大纲和现场文本评论。通过全面的案例研究,我们证明了e2故事线使用户能够更好地从故事中提取信息并理解实体之间的关系。
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来源期刊
ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.30
自引率
2.00%
发文量
131
期刊介绍: ACM Transactions on Intelligent Systems and Technology is a scholarly journal that publishes the highest quality papers on intelligent systems, applicable algorithms and technology with a multi-disciplinary perspective. An intelligent system is one that uses artificial intelligence (AI) techniques to offer important services (e.g., as a component of a larger system) to allow integrated systems to perceive, reason, learn, and act intelligently in the real world. ACM TIST is published quarterly (six issues a year). Each issue has 8-11 regular papers, with around 20 published journal pages or 10,000 words per paper. Additional references, proofs, graphs or detailed experiment results can be submitted as a separate appendix, while excessively lengthy papers will be rejected automatically. Authors can include online-only appendices for additional content of their published papers and are encouraged to share their code and/or data with other readers.
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