Inductive logic programming is a type of machine learning in which logic programs are learned from examples. This learning typically occurs relative to some background knowledge provided as a logic program. This dissertation introduces bottom preprocessing, a method for generating initial constraints on the programs an ILP system must consider. Bottom preprocessing applies ideas from inverse entailment to modern ILP systems. Inverse entailment is an influential early ILP approach introduced with Progol. This dissertation also presents $\bot$-Popper, an implementation of bottom preprocessing for the modern ILP system Popper. It is shown experimentally that bottom preprocessing can reduce learning times of ILP systems on hard problems. This reduction can be especially significant when the amount of background knowledge in the problem is large.

Relational learning can be described as the task of learning first-order logic rules from examples. It has enabled a number of new machine learning applications, e.g. graph mining and link analysis in social networks. The CILP++ system is a neural-symbolic system which can perform efficient relational learning, by being able to process first-order logic knowledge into a neural network. CILP++ relies on BCP, a recently discovered propositionalization algorithm, to perform relational learning. However, efficient knowledge extraction from such networks is an open issue and features generated by BCP do not have an independent relational description, which prevents sound knowledge extraction from such networks. We present a methodology for generating independent propositional features for BCP by using semi-propositionalization of bottom clauses. Empirical results obtained in comparison with the original version of BCP show that this approach has comparable accuracy and runtimes, while allowing proper relational knowledge representation of features for knowledge extraction from CILP++ networks.

Learning probabilistic logic programming languages is receiving an increasing attention and systems are available for learning the parameters (PRISM, LeProbLog, LFI-ProbLog and EMBLEM) or both the structure and the parameters (SEM-CP-logic and SLIPCASE) of these languages. In this paper we present the algorithm SLIPCOVER for "Structure LearnIng of Probabilistic logic programs by searChing OVER the clause space". It performs a beam search in the space of probabilistic clauses and a greedy search in the space of theories, using the log likelihood of the data as the guiding heuristics. To estimate the log likelihood SLIPCOVER performs Expectation Maximization with EMBLEM. The algorithm has been tested on five real world datasets and compared with SLIPCASE, SEM-CP-logic, Aleph and two algorithms for learning Markov Logic Networks (Learning using Structural Motifs (LSM) and ALEPH++ExactL1). SLIPCOVER achieves higher areas under the precision-recall and ROC curves in most cases.