Large Scale Machine Learning over Knowledge Graphs

Knowledge graphs (KGs) have become popular across various fields, providing convenient access to web-based knowledge while storing and formalizing domain-specific information. By analyzing KGs, patterns, connections, and dependencies can be identified across different data sources, enabling the inference of new knowledge from given facts. As the use of KGs expands, the size of modern KGs has grown significantly, making them impossible to process within the main memory of a single computer. Distributed computing offers a viable solution to this challenge by leveraging the combined capabilities of multiple servers within a cluster. This thesis explores how distributed computing can be effectively utilized to perform machine learning over large knowledge graphs, with a focus on facilitating scalable analytics and accelerating downstream tasks. Specifically, this thesis addresses three core challenges: (i) how to facilitate analytics over Knowledge Graphs in Apache Spark, (ii) how to compute KG embeddings at scale, and (iii) how to accelerate and enhance a representative downstream task.