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.

翻译：知识图谱（KGs）中的特征选择正日益广泛应用于生物医学研究、自然语言处理（NLP）和个性化推荐系统等多个领域。本文深入探讨了知识图谱内部特征选择的方法，重点阐述了其在提升机器学习（ML）模型效能、假设生成与可解释性方面的作用。通过本次全面综述，我们旨在推动知识图谱特征选择领域的进一步创新，为跨领域构建更具洞察力、更高效且更可解释的分析模型铺平道路。我们的探索揭示了特征选择技术中可扩展性、准确性与可解释性的至关重要性，并倡导整合领域知识以优化选择过程。我们强调了多目标优化与跨学科合作在推进知识图谱特征选择方面日益增长的应用潜力，并指出此类方法对精准医疗等领域的变革性影响。本文最后展望了未来发展方向，包括开发可扩展的动态特征选择算法，以及整合可解释人工智能原则，以增强知识图谱驱动模型的透明度与可信度。