danyvarghese/PyGol
A novel Inductive Logic Programming(ILP) system based on Meta Inverse Entailment in Python.
This is a tool for researchers and data scientists working with complex, relational datasets to discover underlying logical rules and relationships. You provide examples of known relationships (positive and negative) and existing background knowledge, and it outputs a set of human-readable logical rules explaining those relationships. It's particularly useful for those needing explainable AI or for understanding intricate systems.
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Use this if you need to find understandable, logical rules from structured data, especially when dealing with relational data where standard machine learning models might struggle to provide clear explanations.
Not ideal if your primary goal is high predictive accuracy with unstructured or purely numerical data, or if you don't require human-interpretable logical explanations.
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Language
C
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Last pushed
Mar 21, 2025
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