This special issue focuses on the field of Inductive Logic Programming (ILP), which is a subfield of machine learning that uses logic as a uniform representation language for examples, background knowledge and hypotheses. From these roots, ILP's scope has grown to encompass different approaches that address learning from structured relational data. One notable example is the area of statistical relational learning which focuses on extending ILP to model uncertainty. The special issue is also in conjunction with the 24th International Conference on Inductive Logic Programming (ILP), which was held from September 14th to 16th, 2014, in Nancy, France in co-location with ECML/PKDD-2014. To avoid the redundancy between the conference proceedings and the special issue, authors with an accepted paper at ILP were asked to either have their paper appear in the conference proceedings or submit an extended version of the paper to the special issue. While associated with the ILP conference, there was an open call for submissions to this special issue. The special issue received six submissions of which three were originally submitted to the ILP conference. Ultimately, four were accepted to appear in the special issue. The papers offer a nice reflection on the strengths of ILP and relational learning and where the field is headed. Namely, the articles build off ILP's established track record of being particularly well suited to addressing important applications and the vibrant recent work that focuses on modeling uncertainty in relational data. One of the first application areas where ILP gained significant traction was in analyzing molecules, in particular for the task of drug design. Drugs are small molecules that affect disease by binding to a target protein in the human body. Approaches to drug design depend on whether properties and/or structure of the drug or target are known. Previously, ILP has been successfully applied to identify properties of a drug molecule responsible for it binding to