This paper proposes a unifying framework for inductive rule learning algorithms. We suggest that the problem of constructing an appropriate inductive hypothesis (set of rules) can be broken down in the following subtasks: rule construction, body construction, and feature construction. Each of these subtasks may have its own declarative bias, search strategies, and heuristics. In particular, we argue that feature construction is a crucial notion in explaining the relations between attribute-value rule learning and inductive logic programming (ILP). We demonstrate this by a general method for transforming ILP problems to attribute-value form, which overcomes some of the traditional limitations of propositionalisation approaches.