Exploring Knowledge Graphs for Machine Learning Enhancement

In recent years, machine learning (ML) methods have seen widespread adoption across various domains due to their ability to perform complex tasks with high accuracy. However, their applicability remains limited in certain critical fields, where the demand for reliable decision-making is crucial. Furthermore, the efficacy of ML models is often limited by data availability and quality, posing significant challenges
especially in data-sensitive areas. Addressing these limitations, this thesis explores the integration of semantic knowledge through knowledge graphs to enhance the performance of ML models. Knowledge graphs, with their structured representation of domain-specific information, offer a way to augment ML models with domain insights, improving their performance and potentially overcoming the issues
of data scarcity. This research contributes to the field of Neurosymbolic AI and Semantic Web by not only demonstrating the feasibility and benefits of combining knowledge graphs with ML but also by offering guidance on the effective construction and utilization of knowledge graphs for the purpose of ML enhancement.