A review of feature selection strategies utilizing graph data structures and knowledge graphs
It addresses the need for more efficient and interpretable feature selection in knowledge graphs for applications such as precision medicine, but it is incremental as it synthesizes existing methodologies rather than introducing new ones.
This paper reviews feature selection strategies using graph data structures and knowledge graphs to enhance machine learning model efficacy, interpretability, and scalability across domains like biomedical research and NLP, highlighting the importance of multi-objective optimization and domain knowledge integration.
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.