In this talk, we explores how such hierarchical ontological components in knowledge graphs are incorporated into KG representation learning. We present multiple practical machine learning methods, such as hierarchical graph modeling, graph neural networks, self-supervised learning, and language models, that can effectively and efficiently capture ontological information, given different knowledge graph formulations. As a result, our proposed approaches address various real-world challenges in multiple domains, from knowledge graph itself to diverse disciplines including natural language processing (language models), recommender systems, bioinformatics, and societal studies, and expand ML frontiers to knowledge graphs to multi-modal applications.