Summary of Towards Continual Knowledge Graph Embedding Via Incremental Distillation, by Jiajun Liu et al.
Towards Continual Knowledge Graph Embedding via Incremental Distillation
by Jiajun Liu, Wenjun Ke, Peng Wang, Ziyu Shang, Jinhua Gao, Guozheng Li, Ke Ji, Yanhe Liu
First submitted to arxiv on: 7 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 proposed method for continual knowledge graph embedding (CKGE) addresses the issue of preserving old knowledge while learning new triples efficiently. Existing methods ignore the explicit graph structure, which is critical for the task. To optimize the learning order, a hierarchical strategy ranks new triples layer-by-layer based on graph structure features. The incremental distillation mechanism promotes old knowledge preservation by transferring entity representations from previous layers to next ones. A two-stage training paradigm avoids over-corruption of old knowledge influenced by under-trained new knowledge. Experimental results demonstrate the superiority of the proposed method over state-of-the-art baselines, with improvements in the mean reciprocal rank (MRR) score ranging from 0.2% to 6.5%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to learn about new things while keeping what we already know. This is called continual knowledge graph embedding. Most methods don’t take into account how the new information relates to what we already know, which can cause problems. The proposed method uses a special strategy to learn about new things in a way that helps keep what we already know. It also has a mechanism to help transfer what we already know to the new information. This approach is better than other methods and can improve how well it does its job. |
Keywords
» Artificial intelligence » Distillation » Embedding » Knowledge graph
