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 ACM Transactions on Intelligent Systems and Technology Pub Date : 2023-11-23 DOI:10.1145/3633519
Yunchao Wang, Guodao Sun, Zihao Zhu, Tong Li, Ling Chen, Ronghua Liang
{"title":"E2Storyline: Visualizing the Relationship with Triplet Entities and Event Discovery","authors":"Yunchao Wang, Guodao Sun, Zihao Zhu, Tong Li, Ling Chen, Ronghua Liang","doi":"10.1145/3633519","DOIUrl":null,"url":null,"abstract":"<p>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 <i>E</i><sup>2</sup>Storyline, 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 <i>E</i><sup>2</sup>Storyline to real-world data, including movie synopses and live text commentaries. Through comprehensive case studies, we demonstrate that <i>E</i><sup>2</sup>Storyline enables users to better extract information from stories and comprehend the relationships between entities.</p>","PeriodicalId":48967,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology","volume":"215 1","pages":""},"PeriodicalIF":7.2000,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Intelligent Systems and Technology","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3633519","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
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.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
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.
期刊最新文献
Aspect-enhanced Explainable Recommendation with Multi-modal Contrastive Learning The Social Cognition Ability Evaluation of LLMs: A Dynamic Gamified Assessment and Hierarchical Social Learning Measurement Approach Explaining Neural News Recommendation with Attributions onto Reading Histories Misinformation Resilient Search Rankings with Webgraph-based Interventions Privacy-Preserving and Diversity-Aware Trust-based Team Formation in Online Social Networks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1