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October 20, 2025Open Access

Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability

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Authors

VSVeda C. StoreyJPJeffrey ParsonsACAgustín Castellanos

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Overview

Research reveals improved model performance and explainability in machine learning using domain knowledge and conceptual modeling principles.

Key Points

  • Using conceptual modeling improves machine learning performance and transparency, addressing common challenges.
  • The CMML method was applied to real-world problems, demonstrating its effectiveness in refining data preparation.
  • Data scientists assessed the impact of CMML, indicating its practical value in enhancing machine learning outcomes.
  • This approach highlights the importance of integrating domain knowledge in machine learning practices.

Cite This Study

Storey et al. (2025) studied this question.

synapsesocial.com/papers/68f5fcdc8d54a28a75cf235bhttps://doi.org/10.48550/arxiv.2507.02922
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