Developing Knowledge-Centric Frameworks for Enhancing Web Search Diversity through Semantic Artificial Intelligence
Keywords:
Semantic Web Technologies, Machine Learning, Explainability, XAI.Abstract
Machine Learning methods, particularly Artificial Neural Networks, have garnered significant interest in both research and practical applications because to their substantial promise in prediction tasks. Nevertheless, these models often fail to provide explainable results, which is an essential criterion in several high-stakes fields such as healthcare or transportation.
Concerning explainability, Semantic Web Technologies provide semantically interpretable tools that facilitate reasoning on knowledge bases. Consequently, the inquiry emerges about how Semantic Web Technologies and associated notions might enhance explanations inside Machine Learning systems. This discussion presents contemporary methodologies for integrating Machine Learning with Semantic Web Technologies, focusing on model explainability, derived from a rigorous literature review. In this process, we also emphasize the areas and applications propelling the study field and examine the methods by which explanations are provided to the user. Based on these observations, we propose avenues for further study on the integration of Semantic Web Technologies with Machine Learning.
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