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Updated: Aug 3, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
Combining data and theory for derivable scientific discovery with AI-Descartes
Cristina Cornelio1,2, Sanjeeb Dash3, Vernon Austel3
1IBM Research-Mathematics and Theoretical Computer Science, New York, NY, USA. c.cornelio@samsung.com.
This study introduces a new method combining logical reasoning and symbolic regression to discover scientific laws from data and prior knowledge. This approach enables accurate model derivation even with limited experimental data.
Area of Science:
- Physics
- Computer Science
- Scientific Discovery
Background:
- Mathematical models describe natural phenomena, derived manually or via machine learning.
- Incorporating functional constraints into models is studied, but incorporating general logical axioms remains an open problem.
Purpose of the Study:
- To develop a principled method for deriving scientific models from axiomatic knowledge and experimental data.
- To address the challenge of integrating general logical axioms into automated model discovery.
Main Methods:
- Combining logical reasoning with symbolic regression.
- Demonstrating the method on Kepler's third law, Einstein's time-dilation, and Langmuir's adsorption theory.
Main Results:
- Successfully derived governing laws from limited data points.
- Showcased the ability to distinguish between candidate formulae with similar data error using logical reasoning.
Conclusions:
- The developed method enables principled model derivations from axiomatic knowledge and data.
- Logical reasoning enhances the discovery of scientific laws, especially with sparse data.
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