Summary of A Unified Temporal Knowledge Graph Reasoning Model Towards Interpolation and Extrapolation, by Kai Chen et al.
A Unified Temporal Knowledge Graph Reasoning Model Towards Interpolation and Extrapolation
by Kai Chen, Ye Wang, Yitong Li, Aiping Li, Han Yu, Xin Song
First submitted to arxiv on: 28 May 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 paper introduces Temporal PAth-based Reasoning (TPAR), a novel model for temporal knowledge graph (TKG) reasoning, which addresses both interpolation and extrapolation settings. Unlike existing methods, TPAR performs neural-driven symbolic reasoning, making it robust to noisy data and interpretable. The proposed method outperforms state-of-the-art (SOTA) models on the link prediction task for both settings. A novel pipeline experimental setting is designed to evaluate SOTA combinations and TPAR. Comprehensive experiments demonstrate the effectiveness of TPAR in interpolation and extrapolation reasoning, highlighting its robustness and interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to reason with temporal knowledge graphs (TKG), which helps us understand how things are related over time. Currently, there are two main ways to do this: either by looking at facts that are close together or by considering events from the past. The problem is that most methods only work for one of these approaches, making it hard to apply them in real-life situations. To solve this issue, the authors propose a new model called TPAR, which can handle both types of reasoning. They tested TPAR and found that it outperforms other existing methods. This shows that TPAR is a reliable and understandable way to work with TKGs. |
Keywords
» Artificial intelligence » Knowledge graph
