UCLA

Large-scale Knowledge Graphs (KGs), such as Wikipedia and many enterprises or other domain-specific KGs, contain large numbers of real-world facts and are ubiquitous and foundational to many downstream knowledge-driven AI applications. Many existing techniques have applied state-of-the-art machine learning (ML) techniques in knowledge graph modeling to improve the performance in these applications with the KG backend, but the semantic structures especially the hierarchical ontological information inside the KGs are sparsely investigated and therefore relatively less leveraged into KG learning.

In this dissertation, we demonstrate a series of research results that systematically 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, recommender system, bioinformatics, and societal studies, and expand ML frontiers to knowledge graphs.