  Feature selection in Knowledge Graphs (KGs) are increasingly utilized in
diverse domains, including biomedical research, Natural Language Processing
(NLP), and personalized recommendation systems. This paper delves into the
methodologies for feature selection within KGs, emphasizing their roles in
enhancing machine learning (ML) model efficacy, hypothesis generation, and
interpretability. Through this comprehensive review, we aim to catalyze further
innovation in feature selection for KGs, paving the way for more insightful,
efficient, and interpretable analytical models across various domains. Our
exploration reveals the critical importance of scalability, accuracy, and
interpretability in feature selection techniques, advocating for the
integration of domain knowledge to refine the selection process. We highlight
the burgeoning potential of multi-objective optimization and interdisciplinary
collaboration in advancing KG feature selection, underscoring the
transformative impact of such methodologies on precision medicine, among other
fields. The paper concludes by charting future directions, including the
development of scalable, dynamic feature selection algorithms and the
integration of explainable AI principles to foster transparency and trust in
KG-driven models.
