# Graph-Structured Trajectory Extraction from Travelogues

arXiv: https://arxiv.org/abs/2410.16633 · HTML: https://mlsanity.com/paper/2410.16633/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 44 references that are in the map.
- First posted on arXiv 22 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://mlsanity.com/paper/2010.01057/) (2020)
- [Continual Pre-Training for Cross-Lingual LLM Adaptation: Enhancing Japanese Language Capabilities](https://mlsanity.com/paper/2404.17790/) (2024)
- [GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding](https://mlsanity.com/paper/2310.14478/) (2023)
- [A Pragmatic Guide to Geoparsing Evaluation](https://mlsanity.com/paper/1810.12368/) (2018)
- [Building a Large Japanese Web Corpus for Large Language Models](https://mlsanity.com/paper/2404.17733/) (2024)
- [A Coherent Unsupervised Model for Toponym Resolution](https://mlsanity.com/paper/1805.01952/) (2018)
- [Arukikata Travelogue Dataset with Geographic Entity Mention, Coreference, and Link Annotation](https://mlsanity.com/paper/2305.13844/) (2023)

## Built on by (most cited first, 1 of 1)

- [VIR-Bench: Evaluating Geospatial and Temporal Understanding of MLLMs via Travel Video Itinerary Reconstruction](https://mlsanity.com/paper/2509.19002/) (2025)

## Abstract (arXiv)

Previous studies on sequence-based extraction of human movement trajectories have an issue of inadequate trajectory representation. Specifically, a pair of locations may not be lined up in a sequence especially when one location includes the other geographically. In this study, we propose a graph representation that retains information on the geographic hierarchy as well as the temporal order of visited locations, and have constructed a benchmark dataset for graph-structured trajectory extraction. The experiments with our baselines have demonstrated that it is possible to accurately predict visited locations and the order among them, but it remains a challenge to predict the hierarchical relations.