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18178768. ONTOLOGY-BASED FRAMEWORK FOR INTERPRETABLE FEATURE ENGINEERING simplified abstract (SAP SE)

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ONTOLOGY-BASED FRAMEWORK FOR INTERPRETABLE FEATURE ENGINEERING

Organization Name

SAP SE

Inventor(s)

Mohamed Bouadi of La Garenne-Colombes (FR)

Arta Alavi of Andresy (FR)

Salima Benbernou of Colombes (FR)

Mourad Ouziri of Gennevilliers (FR)

ONTOLOGY-BASED FRAMEWORK FOR INTERPRETABLE FEATURE ENGINEERING - A simplified explanation of the abstract

This abstract first appeared for US patent application 18178768 titled 'ONTOLOGY-BASED FRAMEWORK FOR INTERPRETABLE FEATURE ENGINEERING

The patent application describes a system and method for generating features using a learning network, determining the interpretability of these features based on a domain ontology and symbolic rules, selecting interpretable features for model training, evaluating model performance, calculating a reward based on performance and interpretability, and generating new features based on the reward.

  • Features are generated using a learning network.
  • Interpretability of features is determined using a domain ontology and symbolic rules.
  • Interpretable features are selected for model training.
  • Model performance is evaluated.
  • A reward is calculated based on performance and interpretability.
  • New features are generated using the reward.

Potential Applications: This technology can be applied in various fields such as machine learning, artificial intelligence, data analysis, and predictive modeling.

Problems Solved: This technology addresses the challenge of selecting interpretable features for model training, which can improve the performance and explainability of machine learning models.

Benefits: The system and method described in the patent application can lead to more transparent and accurate machine learning models, enhancing decision-making processes in various industries.

Commercial Applications: This technology could be utilized in industries such as healthcare, finance, marketing, and cybersecurity to improve the accuracy and interpretability of predictive models.

Questions about the technology: 1. How does the system determine the interpretability of features based on a domain ontology and symbolic rules? 2. What are the potential implications of using interpretable features for model training in various industries?


Original Abstract Submitted

Systems and methods include generation of a first plurality of features using a learning network, determination of an interpretability of each of the first plurality of features based on a domain ontology and on symbolic rules associated with entities of the domain ontology, determination of a first set of the first plurality of features which were determined as interpretable, determination of a performance of a model trained using the first set of the plurality of features, determine a reward based on the performance and the interpretability, and generation of a second plurality of features using the learning network based on the reward.

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