Summary of History Repeats Itself: a Baseline For Temporal Knowledge Graph Forecasting, by Julia Gastinger et al.
History repeats Itself: A Baseline for Temporal Knowledge Graph Forecasting
by Julia Gastinger, Christian Meilicke, Federico Errica, Timo Sztyler, Anett Schuelke, Heiner Stuckenschmidt
First submitted to arxiv on: 25 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a simple yet effective baseline for Temporal Knowledge Graph (TKG) Forecasting, which predicts links in knowledge graphs at future timesteps based on historical data. The proposed baseline, known as Recurring Facts Baseline, requires minimal hyperparameter tuning and no iterative training, making it an accessible starting point for researchers. The authors demonstrate the effectiveness of this approach by comparing it to 11 existing TKG models on five datasets, showing that it ranks first or third in three of them. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about predicting what happens next in a special kind of database called a knowledge graph. Right now, scientists don’t have a good way to test which methods are working best for this task. The authors came up with a simple idea: instead of trying to predict everything that will happen, just try to predict the things that always happen (like “the sun will rise tomorrow”). They tested this idea against 11 other methods and found that it did surprisingly well on some datasets. |
Keywords
» Artificial intelligence » Hyperparameter » Knowledge graph
