Summary of Embedding Knowledge Graphs in Degenerate Clifford Algebras, by Louis Mozart Kamdem Teyou et al.
Embedding Knowledge Graphs in Degenerate Clifford Algebras
by Louis Mozart Kamdem Teyou, Caglar Demir, Axel-Cyrille Ngonga Ngomo
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 explores a new direction in knowledge graph embeddings by generalizing Clifford algebras, specifically considering nilpotent base vectors with a nilpotency index of two. The authors design two novel models for discovering the parameters p, q, and r, one using a greedy search and the other relying on neural networks to predict these values based on an input knowledge graph embedding. The paper evaluates their approach on seven benchmark datasets, showing that nilpotent vectors can improve capture embeddings. The results also suggest that the proposed method generalizes better than state-of-the-art approaches, achieving higher MRRs on validation data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a new way to understand complex relationships between things. This paper introduces a fresh idea in computer science that uses special mathematical structures called Clifford algebras. The authors create two new methods to find the right combination of these structures to capture important patterns in big datasets. They test their approach on several benchmark tests and show that it’s better than existing methods at capturing important relationships between things. |
Keywords
* Artificial intelligence * Embedding * Knowledge graph
