Summary of Enhancing Heterogeneous Knowledge Graph Completion with a Novel Gat-based Approach, by Wanxu Wei et al.
Enhancing Heterogeneous Knowledge Graph Completion with a Novel GAT-based Approach
by Wanxu Wei, Yitong Song, Bin Yao
First submitted to arxiv on: 5 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper proposes GATH, a novel method for completing knowledge graphs (KGs) that tackles two primary issues: overfitting in heterogeneous KGs and poor performance in predicting tail entities. Existing GAT-based methods suffer from these limitations due to imbalanced sample sizes. To address this, GATH incorporates two attention network modules that work synergistically to predict missing entities. The model also introduces novel encoding and feature transformation approaches to improve robustness in scenarios with imbalanced samples. Comprehensive experiments are conducted on the FB15K-237 and WN18RR datasets, showing significant improvements over state-of-the-art models on Hits@10 and MRR metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GATH is a new way to make big lists of information (called knowledge graphs) more accurate and complete. Right now, these lists can be kind of messy and missing important details. GAT-based methods are really good at fixing this problem, but they have some limitations when dealing with very large or mixed-type lists. To solve this, the researchers created a new method that combines two special attention networks to find the right information. They also came up with new ways to prepare data for better results in situations where there’s an imbalance of examples. The team tested their approach on two big datasets and showed it performed much better than existing methods. |
Keywords
» Artificial intelligence » Attention » Overfitting
