Inductive logic programming (ILP) studies the learning of (Prolog) logic programs and other relational knowledge from examples. Most machine learning algorithms are restricted to finite, propositional, feature-based representations of examples and concepts and cannot learn complex relational and recursive knowledge. ILP allows learning with much richer representations. Our work has focussed on applications of ILP to various problems in natural language and theory refinement for logic programs.