# Paths of A Million People: Extracting Life Trajectories from Wikipedia

arXiv: https://arxiv.org/abs/2406.00032 · HTML: https://mlsanity.com/paper/2406.00032/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 79 references that are in the map.
- First posted on arXiv 25 May 2024.

## Builds on (most cited first, 11 of 11)

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- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning](https://mlsanity.com/paper/2011.01403/) (2020)
- [Exploring the Feasibility of ChatGPT for Event Extraction](https://mlsanity.com/paper/2303.03836/) (2023)
- [Career on the Move: Geography, Stratification, and Scientific Impact](https://mlsanity.com/paper/1404.6247/) (2014)
- [The POLUSA Dataset: 0.9M Political News Articles Balanced by Time and Outlet Popularity](https://mlsanity.com/paper/2005.14024/) (2020)
- [Where Did the President Visit Last Week? Detecting Celebrity Trips from News Articles](https://mlsanity.com/paper/2307.08721/) (2023)

## Abstract (arXiv)

The life trajectories of notable people have been studied to pinpoint the times and places of significant events such as birth, death, education, marriage, competition, work, speeches, scientific discoveries, artistic achievements, and battles. Understanding how these individuals interact with others provides valuable insights for broader research into 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.