Knowledge Graphs (KGs) attract increasing interest in research in various KG-driven AI-related fields, such as question answering, information recommendation, dialogue, etc. KGs are effective well-structural relational databases for knowledge acquisition. This book presents our research and developments on knowledge graph learning and its applications. Specifically, we mainly focus on: 1) knowledge graph completion, including entity and relation prediction, and entity typing, and 2) the KG applications, including its utilization in fine-grained entity typing, and stock movement prediction.