© 2008

Logical and Relational Learning

  • Luc De Raedt

Part of the Cognitive Technologies book series (COGTECH)

Table of contents

  1. Front Matter
    Pages I-XV
  2. Luc De Raedt
    Pages 1-15
  3. Luc De Raedt
    Pages 17-39
  4. Luc De Raedt
    Pages 41-70
  5. Luc De Raedt
    Pages 71-114
  6. Luc De Raedt
    Pages 115-155
  7. Luc De Raedt
    Pages 157-186
  8. Luc De Raedt
    Pages 187-221
  9. Luc De Raedt
    Pages 223-288
  10. Luc De Raedt
    Pages 289-324
  11. Luc De Raedt
    Pages 345-350
  12. Back Matter
    Pages 351-387

About this book


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.


artificial intelligence data mining database intelligence learning machine learning programming relational database web mining

Editors and affiliations

  • Luc De Raedt
    • 1
  1. 1.Dept. of Computer ScienceKatholieke Universiteit LeuvenCelestijnenlaan 200ABelgium

Bibliographic information

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From the reviews:

"This book is an invaluable resource for anyone interested in exploiting the power of logical representations to learn from highly structured data. The book offers a systematic and innovative view of this important and rapidly developing area, combining technical depth and breadth of coverage. In Bristol, we use De Raedt's book as textbook for MSc students and as a reference for PhD students and researchers." (Peter A. Flach, University of Bristol)

"This book provides comprehensive coverage of logical and relational learning, with an overview of inductive logic programming, multi-relational data mining, and statistical relational learning. … The book is replete with examples, exercises, and case studies. The case studies use popular logical and relational systems and applications. The ample use of illustrations, tables, and bullet lists makes the book more readable and understandable. … very useful to students, researchers, and practitioners in the fields of machine learning, automated knowledge discovery, data mining, and related fields." (Alexis Leon, ACM Computing Reviews, July, 2009)