Foundations of Inductive Logic Programming

This book constitutes the thoroughly refereed post-conference proceedings of the 26th International Conference on Inductive Logic Programming, ILP 2016, held in London, UK, in September 2016.

This book constitutes the thoroughly refereed post-conference proceedings of the 26th International Conference on Inductive Logic Programming, ILP 2016, held in London, UK, in September 2016.

Rules – the clearest, most explored and best understood form of knowledge representation – are particularly important for data mining, as they offer the best tradeoff between human and machine understandability. This book presents the fundamentals of rule learning as investigated in classical machine learning and modern data mining. It introduces a feature-based view, as a unifying framework for propositional and relational rule learning, thus bridging the gap between attribute-value learning and inductive logic programming, and providing complete coverage of most important elements of rule learning.

This textbook covers logical and relational learning in depth, and hence provides an introduction to inductive logic programming (ILP), multirelational data mining (MRDM) and (statistical) relational learning (SRL). These subfields of data mining and machine learning are concerned with the analysis of complex and structured data sets that arise in numerous applications, such as bio- and chemoinformatics, network analysis, Web mining, natural language processing, within the rich representations offered by relational databases and computational logic.