Summary of Embedding Knowledge Graph in Function Spaces, by Louis Mozart Kamdem Teyou and Caglar Demir and Axel-cyrille Ngonga Ngomo
Embedding Knowledge Graph in Function Spaces
by Louis Mozart Kamdem Teyou, Caglar Demir, Axel-Cyrille Ngonga Ngomo
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 introduces a novel embedding method that diverges from conventional approaches by operating within function spaces of finite dimension rather than finite vector space. The authors initially employ polynomial functions to compute embeddings and then progress to more intricate representations using neural networks with varying layer complexities. This approach enables operations such as composition, derivatives, and primitive representation, thus enhancing expressiveness and allowing for more degrees of freedom. The paper provides a step-by-step construction of the approach and offers code for reproducibility, facilitating further exploration and application in the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to do something with data called “embedding”. It’s different from other methods because it uses functions instead of numbers. This lets it do more things, like combining information or understanding how things change over time. The authors use simple math formulas at first and then move on to more complicated neural networks. They explain everything in detail and provide the code so others can try it out. |
Keywords
» Artificial intelligence » Embedding » Vector space
