Logical rules are widely used to represent domain knowledge and hypothesis, which is fundamental to symbolic reasoning-based methods. Despite the potential benefits of logical rules, they are usually obtained through a labor-intensive process in the early days. In this section, we look into the problem of learning logic rules automatically from KGs, which can be broadly divided into two categories: (1) traditional search-based methods and (2) more recent neuro-symbolic methods. Despite achieving remarkable performance in learning logical rules, neuro-symbolic integration approaches heavily depend on observed data to identify rules. This makes it challenging for these methods to identify rules that lack sufficient instances to support them. To overcome this limitation, the state-of-the-art approach called RLogic [1] has been introduced. RLogic does not solely rely on rule instances but suggests learning logical rules directly at the schema level and pushing deductive reasoning deep into the learning process.