Relational structured data is a powerful way of representing knowledge that captures meaning in a structured form suitable for machine learning. Compared to vision and natural language data, relational structured data excels in representing and manipulating structured knowledge, making it ideal for tasks that require reasoning or inference. Human-like Learning is a set of methods that leverage relational structures to rapidly acquire and generalize knowledge to new tasks and situations. In this talk, I will explore efficient and adaptive human-like learning algorithms that are crucial in scenarios where environments may change unpredictably. Additionally, these models are more easily interpretable by humans, promoting transparency in AI algorithms.