Can predicate invention compensate for incomplete background knowledge?

In machine learning we are often faced with the problem of incomplete data, which can lead to lower predictive accuracies in both feature-based and relational machine learning. It is therefore important to develop techniques to compensate for incomplete data. In inductive logic programming (ILP) incomplete data can be in the form of missing values or missing predicates. In this paper, we investigate whether an ILP learner can compensate for missing background predicates through predicate invention. We conduct experiments on two datasets in which we progressively remove predicates from the background knowledge whilst measuring the predictive accuracy of three ILP learners with differing levels of predicate invention. The experimental results show that as the number of background predicates decreases, an ILP learner which performs predicate invention has higher predictive accuracies than the learners which do not perform predicate invention, suggesting that predicate invention can compensate for incomplete background knowledge.