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Structured, Complex, and Time-complete Temporal Event Forecasting: A Comprehensive Formulation and Benchmark


Core Concepts
This work proposes a novel formulation of Structured, Complex, and Time-complete Temporal Events (SCTc-TE) to comprehensively represent temporal events, and develops a fully automated pipeline to construct large-scale SCTc-TE datasets. It also introduces a novel forecasting method, LoGo, that leverages both local and global contexts for improved performance.
Abstract
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.
Stats
The MidEast-TE dataset contains 455,877 atomic events from 274,795 news articles, spanning 31.08 days on average per complex event. The GDELT-TE dataset contains 1,201,881 atomic events from 273,845 news articles, spanning 30.87 days on average per complex event.
Quotes
"To the best of our knowledge, we are the first to propose the SCTc-TE formulation that encompasses all the structured, complex, and time-complete properties of TEs." "We propose to unify the modeling of both local and global contexts for TE forecasting."

Key Insights Distilled From

by Yunshan Ma,C... at arxiv.org 04-04-2024

https://arxiv.org/pdf/2312.01052.pdf
SCTc-TE

Deeper Inquiries

What are the potential applications of the SCTc-TE formulation beyond event forecasting

The SCTc-TE formulation has potential applications beyond event forecasting. One application could be in the field of anomaly detection, where the structured, complex, and time-complete nature of the formulation can be leveraged to detect unusual patterns or behaviors in various systems. For example, in cybersecurity, SCTc-TE could be used to predict and identify potential cyber threats by analyzing historical events and patterns. Additionally, in financial markets, SCTc-TE could be applied to forecast market trends and predict potential financial risks based on past events and their temporal relationships.

How can the performance of the event extraction component be further improved using more advanced language models

To improve the performance of the event extraction component using more advanced language models, several strategies can be implemented. Firstly, fine-tuning the language models on domain-specific data related to the events of interest can enhance the model's understanding and extraction capabilities. Additionally, incorporating ensemble models that combine multiple language models can help capture a broader range of event types and nuances. Furthermore, utilizing pre-trained models with larger architectures and more parameters, such as GPT-4 or future iterations, can improve the model's ability to extract complex event information accurately.

How can the SCTc-TE formulation be extended to capture higher-order relationships and dependencies among complex events

To extend the SCTc-TE formulation to capture higher-order relationships and dependencies among complex events, a hierarchical modeling approach can be implemented. This approach involves organizing events into multiple levels of abstraction, where higher-order relationships are represented by aggregating lower-level events. By incorporating hierarchical structures and multi-level dependencies, the formulation can capture more nuanced and complex event interactions. Additionally, incorporating graph neural networks (GNNs) with attention mechanisms can help model the intricate relationships between events at different levels of granularity, enabling the formulation to capture more sophisticated event dependencies.
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