Summary of Domain and Range Aware Synthetic Negatives Generation For Knowledge Graph Embedding Models, by Alberto Bernardi and Luca Costabello
Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models
by Alberto Bernardi, Luca Costabello
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 an innovative approach to training Knowledge Graph Embedding models by generating synthetic negative samples. The authors focus on improving the embeddings’ quality, which is crucial for tasks such as completing and exploring large knowledge graphs. They propose an updated strategy that generates corruptions respecting the domain and range of relations, and demonstrate its effectiveness with significant improvements (+10% MRR) on standard benchmark datasets and over +150% MRR on a larger ontology-backed dataset. The authors’ method utilizes relation-based corruption, which has not been explored before in this context. The results show that this approach can lead to better performance and improved robustness of the embeddings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to make computers understand and complete large collections of information called knowledge graphs. Right now, computers are very good at understanding these graphs when they have been partially filled out. But it’s hard for them to learn from incomplete graphs because there aren’t many examples of what the missing information should look like. So, scientists came up with a way to generate fake negative examples that can help computers learn. This approach is important because it could make computers better at understanding and filling in the blanks on large knowledge graphs. |
Keywords
» Artificial intelligence » Embedding » Knowledge graph
