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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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GrooveSquid.com Paper Summaries

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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