{"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.
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.