Summary of Leveraging Knowlegde Graphs For Interpretable Feature Generation, by Mohamed Bouadi and Arta Alavi and Salima Benbernou and Mourad Ouziri
Leveraging Knowlegde Graphs for Interpretable Feature Generation
by Mohamed Bouadi, Arta Alavi, Salima Benbernou, Mourad Ouziri
First submitted to arxiv on: 1 Jun 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 The proposed KRAFT framework combines neural generators and knowledge-based reasoners to develop interpretable Machine Learning (ML) models. By leveraging a knowledge graph, KRAFT automatically generates features through transformations and evaluates their interpretability using Description Logics (DL). This hybrid AI approach is trained using Deep Reinforcement Learning (DRL) to maximize prediction accuracy while ensuring feature interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary KRAFT is a new way to make machine learning models work better. It takes the raw data and turns it into something easier to understand, called interpretable features. This helps us know why the model is making certain predictions. KRAFT uses a special combination of computer programs to do this, and it’s trained to get really good at predicting things correctly while also being easy to understand. |
Keywords
» Artificial intelligence » Knowledge graph » Machine learning » Reinforcement learning
