In history research, cohort analysis seeks to identify social structures and figure mobilities by studying the group-based behavior of historical figures. Prior works mainly employ automatic data mining approaches, lacking effective visual explanation. In this paper, we present CohortVA, an interactive visual analytic approach that enables historians to incorporate expertise and insight into the iterative exploration process. The kernel of CohortVA is a novel identification model that generates candidate cohorts and constructs cohort features by means of pre-built knowledge graphs constructed from large-scale history databases. We propose a set of coordinated views to illustrate identified cohorts and features coupled with historical events and figure profiles. Two case studies and interviews with historians demonstrate that CohortVA can greatly enhance the capabilities of cohort identifications, figure authentications, and hypothesis generation.
翻译:在历史研究中,群集分析试图通过研究基于群体的历史数字行为来确定社会结构和图象动员; 先前的工作主要采用自动数据挖掘方法,缺乏有效的直观解释; 在本文中,我们介绍了CohortVA, 这是一种互动视觉分析方法,使历史学家能够将专门知识和洞察力纳入迭接探索过程; CohortVA的内核是一种新颖的识别模型,通过从大型历史数据库建立预先建立的知识图,生成候选群群和组群特征; 我们提出一套协调的观点,以说明已查明的群群和特征以及历史事件和图象特征; 两项案例研究和与历史学家的访谈表明,CohortVA可以大大提高群群识别、图象认证和假设生成的能力。