OntoXAI: a semantic web rule language approach for explainable artificial intelligence

Abstract

Machine learning revolutionizes accuracy in diverse fields such as disease diagnosis, speech understanding, and sentiment analysis. However, its intricate architecture often obscures the decision-making process, creating a “black box” that hinders trust and limits its potential. This lack of transparency poses significant challenges, particularly in critical fields like the healthcare system. We present OntoXAI, a Semantic Web Rule Language (SWRL) based Explainable Artificial Intelligence (XAI) approach to address these challenges. OntoXAI leverages semantic technology and machine learning (ML) to enhance prediction accuracy and generate user-comprehensible natural language explanations in the context of dengue disease classification. OntoXAI can be summarized into three key aspects. (1) Creates a knowledge base that incorporates domain-specific knowledge related to the disease. This allows for the integration of expert knowledge into the classification process. (2) OntoXAI presents a diagnostic classification system that utilizes patient symptoms as input to classify the disease accurately. By leveraging ML algorithms, it achieves high accuracy in disease classification. (3) OntoXAI introduces SWRL and ontology to integrate explainable AI techniques with Open AI API, enabling a better understanding of the classification process. By combining the power of machine learning algorithms with the ability to provide transparent, human-understandable explanations through Open AI API, this approach offers several advantages in enhancing prediction accuracy, achieving levels of up to 96%. Overall, OntoXAI represents a significant advancement in the field of explainable AI, addressing the challenges of transparency and trust in machine learning systems, particularly in critical domains like healthcare.