GraphTide: Augmenting Knowledge-Intensive Text with Progressive Nested Graph

2026-04-14

AI Summary

This work addresses the challenge of comprehending knowledge-intensive texts, which often contain numerous entities and intricate nested relationships that hinder efficient semantic understanding. To tackle this issue, we propose GraphTide, a dynamic visualization approach that supports progressive exploration through on-demand decomposition of entity relationships, structure-aware force-directed layout optimization, incremental information disclosure, and smooth animated transitions. These mechanisms collectively produce clear, contextually coherent visualizations of nested relational structures. User studies demonstrate that GraphTide significantly improves both the efficiency and accuracy of users’ comprehension of complex textual content compared to conventional graph visualizations and static nested diagrams.

Technology Category

• Data Mining & Knowledge Management: Data Visualization & Summarization
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Web Mining and Content Analysis: Web data visualization

Abstract

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.

Problem

• knowledge-intensive text
• entity-relationship
• text comprehension
• nested graph
• complex relationships

Innovation

• progressive nested graph
• entity-relationship decomposition
• structure-aware layout
• animated visualization
• knowledge-intensive text comprehension