Summary of Geometric Relational Embeddings, by Bo Xiong
Geometric Relational Embeddings
by Bo Xiong
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI)
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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 dissertation introduces a novel approach to relational representation learning, focusing on capturing complex and symbolic structures in relational data. The existing vector-based representations are inadequate for this task, as they fail to respect the underlying symbolic nature of the data. To address this limitation, the authors propose geometric relational embeddings, which aim to capture structured patterns, logical structures, and high-order relationships between entities and relations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Geometric relational embeddings provide a new way to represent relational data in a continuous and low-dimensional space. The approach is designed to capture complex structures like hierarchies and cycles in networks and knowledge graphs, as well as logical constraints applicable for machine learning models. By using geometric relational embeddings, the authors demonstrate improved performance on benchmark and real-world datasets. |
Keywords
» Artificial intelligence » Machine learning » Representation learning
