Summary of Mitigating Heterogeneity Among Factor Tensors Via Lie Group Manifolds For Tensor Decomposition Based Temporal Knowledge Graph Embedding, by Jiang Li and Xiangdong Su and Guanglai Gao
Mitigating Heterogeneity among Factor Tensors via Lie Group Manifolds for Tensor Decomposition Based Temporal Knowledge Graph Embedding
by Jiang Li, Xiangdong Su, Guanglai Gao
First submitted to arxiv on: 14 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 In this paper, researchers address a limitation in Temporal Knowledge Graphs Embedding (TKGE) tasks by proposing a novel method to overcome the inherent heterogeneity among factor tensors in tensor decomposition. This heterogeneity hinders the tensor fusion process and limits the performance of link prediction. The proposed method maps factor tensors onto a unified smooth Lie group manifold, making the distribution of factor tensors more homogeneous. This approach enables better performance and can be integrated into existing TKGE methods without introducing extra parameters. Theoretical proofs support this motivation, showing that homogeneous tensors are more effective than heterogeneous ones in tensor fusion and approximation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, scientists worked to fix a problem with how we learn from knowledge graphs over time. They found that when we break down these graphs into smaller pieces called “factor tensors,” the different types of information in each piece cause trouble when trying to combine them. To solve this issue, they developed a new way to make all these pieces look similar by mapping them onto a special kind of geometric shape. This helps the process work better and can be added to existing methods without needing extra information. The results show that this approach is more effective and can help us make better predictions about relationships in knowledge graphs. |
Keywords
» Artificial intelligence » Embedding
