Logical and Relational Learning

Description

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. The author introduces the machine learning and representational foundations of the field and explains some important techniques in detail by using some of the classic case studies centered around well-known logical and relational systems. The book is suitable for use in graduate courses and should be of interest to graduate students and researchers in computer science, databases and artificial intelligence, as well as practitioners of data mining and machine learning. It contains numerous figures and exercises, and slides are available for many chapters. Front Matter....Pages I-XV Introduction....Pages 1-15 An Introduction to Logic....Pages 17-39 An Introduction to Learning and Search....Pages 41-70 Representations for Mining and Learning....Pages 71-114 Generality and Logical Entailment....Pages 115-155 The Upgrading Story....Pages 157-186 Inducing Theories....Pages 187-221 Probabilistic Logic Learning....Pages 223-288 Kernels and Distances for Structured Data....Pages 289-324 Computational Aspects of Logical and Relational Learning....Pages 325-343 Lessons Learned....Pages 345-350 Back Matter....Pages 351-387