E 2 Storyline: Visualizing the Relationship with Triplet Entities and Event Discovery

Author:

Wang Yunchao1ORCID,Sun Guodao1ORCID,Zhu Zihao1ORCID,Li Tong1ORCID,Chen Ling1ORCID,Liang Ronghua1ORCID

Affiliation:

1. Zhejiang University of Technology, China

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 E 2 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 E 2 Storyline to real-world data, including movie synopses and live text commentaries. Through comprehensive case studies, we demonstrate that E 2 Storyline enables users to better extract information from stories and comprehend the relationships between entities.

Funder

National Key Research and Development Program of China

Zhejiang Provincial Natural Science Foundation of China

National Natural Science Foundation of China

Fundamental Research Funds for the Provincial Universities of Zhejiang

Publisher

Association for Computing Machinery (ACM)

Subject

Artificial Intelligence,Theoretical Computer Science

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