AccScience Publishing / IJOCTA / Online First / DOI: 10.36922/IJOCTA026070024
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Enhancing biomedical text interpretation through neuro-symbolic knowledge integration

Zahraa Tarek1 ,  Esraa Hassan2*
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1 Department of Computer Engineering and Information, College of Engineering, Wadi Ad Dwaser, Prince Sattam Bin Abdulaziz University, Al-Kharj, Riyadh, Saudi Arabia
2 Department of Machine learning and Information Retrieval, Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, Egypt
IJOCTA, 026070024 https://doi.org/10.36922/IJOCTA026070024
Received: 9 February 2026 | Revised: 21 March 2026 | Accepted: 23 March 2026 | Published online: 5 May 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

The integration of statistical learning with symbolic reasoning provides a promising pathway toward artificial intelligence systems that are not only highly accurate but also transparent and trustworthy. In this study, we propose a neuro-symbolic framework that integrates neural models for robust pattern recognition with symbolic reasoning mechanisms designed to enforce domain-informed logical constraints, thereby improving the interpretation of biomedical text. The proposed framework is evaluated through a comprehensive experimental protocol that assesses classification performance, statistical robustness, and prediction reliability. Experimental results on a multi-class biomedical text classification task demonstrate that the proposed approach consistently outperformed conventional machine learning baselines, including term frequency--inverse document frequency-based logistic regression. Specifically, the neuro-symbolic model achieved a macro-averaged F1-score of 0.863, compared to 0.724 for the baseline model, representing a 19.2\% relative improvement. Furthermore, the framework exhibited strong predictive stability across multiple biomedical categories, with classification accuracies ranging from 0.938 to 0.950 across topics. The highest performance was observed for sclerenchyma-related texts (accuracy = 0.950), while all other categories maintained accuracies above 0.94, indicating consistent and reliable classification performance. In addition, the proposed framework substantially improved model calibration, achieving a 63\% reduction in expected calibration error and yielding more reliable probability estimates for decision-support applications.

Graphical abstract
Keywords
Neuro-symbolic framework
Knowledge fusion model
Biomedical text understanding
Statistical learning
Text classification
Funding
None.
Conflict of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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An International Journal of Optimization and Control: Theories & Applications, Electronic ISSN: 2146-5703 Print ISSN: 2146-0957, Published by AccScience Publishing
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