Logic Reasoning in Machine Learning Through Data-Knowledge-Driven: Methods and Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 10 February 2026 | Viewed by 8
Special Issue Editor
Special Issue Information
Dear Colleagues,
Logic reasoning in machine learning through data-knowledge-driven approaches bridges data-driven models and knowledge-based reasoning, enhancing interpretability, accuracy, and context-awareness in decisions. This integration is vital for fields requiring logical consistency (e.g., healthcare, finance, autonomous systems), combining data analytics with symbolic knowledge for robust models.
This Special Issue invites original research and review articles on methods and applications of logic reasoning in machine learning via data-knowledge-driven frameworks.
Papers are solicited on topics including but not limited to the following areas:
- State-of-the-art methods for integrating data- and knowledge-driven logic reasoning.
- Techniques to enhance model interpretability through logical reasoning.
- Applications in healthcare, finance, or autonomous systems.
- Symbolic knowledge representation frameworks.
- Hybrid models merging neural networks with logical reasoning.
- Evaluation metrics for reasoning performance.
- Case studies on data-knowledge-driven reasoning effectiveness.
- Advances in automated theorem proving for machine learning.
- Knowledge graph integration for logical reasoning.
- Uncertainty management in reasoning systems.
- Natural language processing.
Dr. Dong Zhang
Guest Editor
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Keywords
- machine learning
- logic reasoning
- data-knowledge driven
- interpretability
- symbolic knowledge representation
- hybrid models
- knowledge graphs
- automated theorem proving
- uncertainty management
- applications in healthcare
- applications in finance
- autonomous systems
- language processing
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