The authors introduce a new formulation called Structured, Complex, and Time-complete Temporal Events (SCTc-TE) to represent temporal events. This formulation encompasses three key properties: 1) Structured representation using Temporal Knowledge Graphs, 2) Complex events composed of multiple atomic events, and 3) Time-complete with absolute timestamps for each atomic event.
To implement this formulation, the authors develop a fully automated pipeline that utilizes large language models and time-aware clustering to extract SCTc-TE from news articles. They construct two large-scale datasets, MidEast-TE and GDELT-TE, based on this pipeline.
The authors then propose a novel forecasting method called LoGo that leverages both local and global contexts for improved performance. The local context captures the evolution of a specific complex event, while the global context provides auxiliary environmental information. LoGo fuses the representations from these two contexts and uses a convolutional decoder for final prediction.
Extensive experiments on the MidEast-TE and GDELT-TE datasets demonstrate the effectiveness of the proposed SCTc-TE formulation and the LoGo forecasting method, outperforming state-of-the-art baselines by a large margin.
To Another Language
from source content
arxiv.org
Key Insights Distilled From
by Yunshan Ma,C... at arxiv.org 04-04-2024
https://arxiv.org/pdf/2312.01052.pdfDeeper Inquiries