EntityWeaver：文档集合中命名实体关系的可视化探索与策管

中文摘要 AI 辅助

我们提出一个交互式可视化系统，用于探索文档集合中的命名实体及其关系，重点在于处理不确定性并支持远读与近读。系统围绕一个连接文档、实体提及和实体的图构建。从提及到实体链接的不确定性直接包含在此图中，使用户能够看到连接是强、弱还是模糊。受科学可视化方法启发的传递函数控制允许用户调整这种不确定性的显示方式，使得针对不同数据集和研究问题调整可视化变得容易。系统还在协调视图中提供对完整源文本的直接访问，从而实现快速上下文检查、解决模糊案例和纠正数字化错误。探索通过多阶段过滤器查询构建器、用于大型图的迷你地图导航以及用于下游分析的导出选项进一步支持。通过将不确定性感知的图可视化与源文本中的直接交互相结合，系统提供了一个统一的工作流程，支持大规模模式发现和基于命名实体的文档集合的策管。设计选择得到了领域专家的支持，他们也参与了初始系统评估。

英文摘要

We present an interactive visualization system for exploring named entities and their relationships across document collections, with a strong focus on handling uncertainty and supporting both distant and close reading. The system is built around a graph that links documents, entity mentions, and entities. Uncertainty from mention-to-entity linking is included directly in this graph, so users can see where connections are strong, weak, or ambiguous. A transfer-function control, inspired by approaches in scientific visualization, allows users to adjust how this uncertainty is displayed, making it easy to tune the visualization for different datasets and research questions. The system also provides direct access to the full source texts in a coordinated view, enabling quick context checks, resolving ambiguous cases, and correcting digitization errors. Exploration is further supported through a multi-stage filter query builder, mini-map navigation for large graphs, and export options for downstream analysis. By combining uncertainty-aware graph visualization with direct interaction in the source texts, the system provides a unified workflow that supports both large-scale pattern discovery and curation of named-entity-based document collections. The design choices were supported by the domain experts, who were also involved in the initial system evaluation.

发表机构

• University of Ljubljana, Faculty of Computer and Information Science（卢布尔雅那大学计算机与信息科学学院）

机构由 AI 辅助整理，请以论文原文为准。

补充信息

备注

Technical Report