Notable people's life trajectories have been a focus of study -- the
locations and times of various activities, such as birth, death, education,
marriage, competition, work, delivering a speech, making a scientific
discovery, finishing a masterpiece, and fighting a battle, and how these people
interact with others, carry important messages for the broad research related
to human dynamics. However, the scarcity of trajectory data in terms of volume,
density, and inter-person interactions, limits relevant studies from being
comprehensive and interactive. We mine millions of biography pages from
Wikipedia and tackle the generalization problem stemming from the variety and
heterogeneity of the trajectory descriptions. Our ensemble model COSMOS, which
combines the idea of semi-supervised learning and contrastive learning,
achieves an F1 score of 85.95%. For this task, we also create a hand-curated
dataset, WikiLifeTrajectory, consisting of 8,852 (person, time, location)
triplets as ground truth. Besides, we perform an empirical analysis on the
trajectories of 8,272 historians to demonstrate the validity of the extracted
results. To facilitate the research on trajectory extractions and help the
analytical studies to construct grand narratives, we make our code, the
million-level extracted trajectories, and the WikiLifeTrajectory dataset
publicly available.

通过挖掘 Wikipedia 上数百万的人物传记页面，结合 COSMOS 模型的半监督学习和对比学习思想，本研究成功提取了人物生命轨迹，并提供了公开可用的代码、大量提取的轨迹数据和 WikiLifeTrajectory 数据集，以促进轨迹提取研究并帮助构建宏大叙事的分析研究。