Abstract: Inductive logic programming (ILP) is a subfield of symbolic rule learning that is formalized by first-order logic and rooted in first-order logical induction theories. The model learned by ILP is a set of highly interpretable first-order rules rather than black boxes; owing to the strong expressive power of first-order logic language, it is relatively easier to exploit domain knowledge during learning; the learned model by ILP can be used for modeling relationships between subjects, rather than predicting the labels of independent objects. However, due to its huge and complicated underlying hypothesis space, it is difficult for ILP to learn models efficiently. This paper tries to review most of the current researches in this area. Mainstream ILP approaches are introduced according to different categorizations of first-order logical induction theories. This paper also reviews the most recent progress in the ILP area, including ILP techniques based on second-order logical abduction, probabilistic inductive logic programming (PILP) and the ILP approaches that introduce differentiable components. This paper also introduces some representative applications of ILP approaches in practical problems, and then talks about its major challenges, and finally discusses about the prospects for future research directions.