Summary of Hip Network: Historical Information Passing Network For Extrapolation Reasoning on Temporal Knowledge Graph, by Yongquan He and Peng Zhang and Luchen Liu and Qi Liang and Wenyuan Zhang and Chuang Zhang
HIP Network: Historical Information Passing Network for Extrapolation Reasoning on Temporal Knowledge Graph
by Yongquan He, Peng Zhang, Luchen Liu, Qi Liang, Wenyuan Zhang, Chuang Zhang
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 proposed Historical Information Passing (HIP) network is a novel approach for predicting future events in temporal knowledge graphs. The method learns to pass historical information selectively, update representations appropriately, and predict events accurately by incorporating three perspectives: temporal evolution of events, interactions between events at the same time step, and known events. The HIP network uses scoring functions corresponding to these dimensions and updates relation representations to model the complex patterns behind temporal changes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The HIP network is a way for computers to better understand what’s going to happen in the future based on what has happened before. It looks at three different things: how events change over time, how events are connected to each other, and which events we already know about. This helps the computer make more accurate predictions about what will happen next. |
