KGs usually suffer from severe incompleteness. Knowledge graph completion aims to predict missing facts by reasoning with existing facts. In this chapter, we give a comprehensive introduction to various methods for KG completion, including (1) traditional symbolic reasoning methods, (2) recent representation learning-based methods, and (3) neuro-symbolic integration-based methods. Although many attempts have been made to combine neural and symbolic methods through probabilistic programming frameworks, they are limited by scalability issues. To address these limitations, UniKER [1] algorithm is introduced as a state-of-the-art (SOTA) method that effectively and efficiently combines symbolic reasoning and representation learning for KG completion tasks.