Summary of Seg:seeds-enhanced Iterative Refinement Graph Neural Network For Entity Alignment, by Wei Ai et al.
SEG:Seeds-Enhanced Iterative Refinement Graph Neural Network for Entity Alignment
by Wei Ai, Yinghui Gao, Jianbin Li, Jiayi Du, Tao Meng, Yuntao Shou, Keqin Li
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 The proposed soft label propagation framework integrates multi-source data and iterative seed enhancement to address scalability challenges in handling extensive datasets, which excel at scale computing. The framework uses seeds for anchoring and selects optimal relationship pairs to create soft labels rich in neighborhood features and semantic relationship data. A bidirectional weighted joint loss function is implemented, which reduces the distance between positive samples and differentially processes negative samples, taking into account non-isomorphic neighborhood structures. This approach outperforms existing semi-supervised methods, demonstrating superior results on multiple datasets and improving the quality of entity alignment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Entity alignment is important for combining knowledge from different sources. Researchers have been using a method that matches entities with similar meanings based on their embeddings, but this can be tricky when dealing with large amounts of data from different places. This paper proposes a new way to match these entities by looking at the relationships between them and adjusting how we process negative samples. The results show that this approach is better than previous methods and can improve the quality of entity alignment. |
Keywords
» Artificial intelligence » Alignment » Loss function » Semi supervised
