Summary of Generalizing Knowledge Graph Embedding with Universal Orthogonal Parameterization, by Rui Li et al.
Generalizing Knowledge Graph Embedding with Universal Orthogonal Parameterization
by Rui Li, Chaozhuo Li, Yanming Shen, Zeyu Zhang, Xu Chen
First submitted to arxiv on: 14 May 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 presents a novel approach to knowledge graph embedding (KGE) called GoldE, which addresses the limitations of existing methods by introducing a universal orthogonal parameterization based on Householder reflection. The framework can naturally achieve dimensional extension and geometric unification with theoretical guarantees, enabling it to capture both logical patterns and topological heterogeneity of knowledge graphs. Empirically, GoldE achieves state-of-the-art performance on three standard benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GoldE is a new way to represent complex relationships in large networks. Right now, most methods can only capture simple patterns and don’t account for the many different types of connections between things. This paper shows how to overcome these limitations by using a special type of transformation that allows for more flexibility and captures both logical patterns and topological heterogeneity. |
Keywords
» Artificial intelligence » Embedding » Knowledge graph
