Iusethetermlogicalandrelationallearning torefertothesub?eldofarti?cial intelligence,machinelearninganddataminingthatisconcernedwithlearning in expressive logical or relational representations. It is the union of inductive logic programming, (statistical) relational learning and multi-relational data mining, which all have contributed techniques for learning from data in re- tional form. Even though some early contributions to logical and relational learning are about forty years old now, it was only with the advent of - ductive logic programming in the early 1990s that the ?eld became popular. Whereas initial work was often concerned with logical (or logic programming) issues,thefocushasrapidlychangedtothediscoveryofnewandinterpretable knowledge from structured data, often in the form of rules, and soon imp- tant successes in applications in domains such as bio- and chemo-informatics and computational linguistics were realized. Today, the challenges and opp- tunities of dealing with structured data and knowledge have been taken up by the arti?cial intelligence community at large and form the motivation for a lot of ongoing research. Indeed, graph, network and multi-relational data mining are now popular themes in data mining, and statistical relational learning is receiving a lot of attention in the machine learning and uncertainty in art- cial intelligence communities. In addition, the range of tasks for which logical and relational techniques have been developed now covers almost all machine learning and data mining tasks.

First textbook on multirelational data mining and inductive logic programming Includes supplementary material: sn.pub/extras

Autorentext

Luc De Raedt is a full professor of computer science at the KU Leuven (Belgium), where he is director of the Lab for Declarative Languages and Artificial Intelligence, and where he also obtained his Ph.D. He is also a former professor of computer science of the Albert-Ludwigs-University Freiburg (Germany) and chair of its lab for Natural Language Processsing and Machine Learning. Luc De Raedt's research interests are in artificial intelligence, machine learning, and data mining, as well as their applications. He is currently working on probabilistic logic learning (sometimes called statistical relational learning), which combines probabilistic reasoning methods with logical representations and machine learning, the integration of constraint programming with data mining and machine learning principles, the development of programming languages for machine learning, and analyzing graph and network data. He is also interested in applications of these methods to chemo- and bio-informatics,to natural language processing, vision, robotics, and action and activity learning. He was program (co)-chair of the 7th ECML Machine Learning (1994, Catania, Sicily), the 5th ILP (1995, Leuven, Belgium), the first ECMLPKDD (2001, Freiburg, Germany), the 22nd ICML Learning (2005, Bonn, Germany) and the 20th ECAI (2012, Montpellier, France). He is an area/action editor of TPLP, JMLR, MLJ, AIJ, and formerly of JAIR. He is also a member of the editorial boards of NGC, AI Communications, Informatica, DMKD, and the Journal of Applied Logic. He was an elected and founding member of the board of the International Machine Learning Society from 2004-2011. In 2005, he was elected as an ECCAI fellow and four of his students have won the ECCAI dissertation award for the best European dissertation in AI.Luc De Raedt is a full professor of computer science at the KU Leuven (Belgium), where he is director of the Lab for Declarative Languages and Artificial Intelligence, and where he also obtained his Ph.D. He is also a former professor of computer science of the Albert-Ludwigs-University Freiburg (Germany) and chair of its lab for Natural Language Processsing and Machine Learning. Luc De Raedt's research interests are in artificial intelligence, machine learning, and data mining, as well as their applications. He is currently working on probabilistic logic learning (sometimes called statistical relational learning), which combines probabilistic reasoning methods with logical representations and machine learning, the integration of constraint programming with data mining and machine learning principles, the development of programming languages for machine learning, and analyzing graph and network data. He is also interested in applications of these methods to chemo- and bio-informatics, to natural language processing, vision, robotics, and action and activity learning. He was program (co)-chair of the 7th ECML Machine Learning (1994, Catania, Sicily), the 5th ILP (1995, Leuven, Belgium), the first ECMLPKDD (2001, Freiburg, Germany), the 22nd ICML Learning (2005, Bonn, Germany) and the 20th ECAI (2012, Montpellier, France). He is an area/action editor of TPLP, JMLR, MLJ, AIJ, and formerly of JAIR. He is also a member of the editorial boards of NGC, AI Communications, Informatica, DMKD, and the Journal of Applied Logic. He was an elected and founding member of the board of the International Machine Learning Society from 2004-2011. In 2005, he was elected as an ECCAI fellow and four of his students have won the ECCAI dissertation award for the best European dissertation in AI.Sriraam Natarajan is an assistant professor at Indiana University. He was previously an assistant professor at Wake Forest School of Medicine, a post-doctoral research associate at University of Wisconsin-Madison, and graduated with his Ph.D. from Oregon State University. His research interests lie in the field of artificial intelligence, with emphasis on machine learning, stati

Klappentext

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.

Inhalt
An Introduction to Logic.- An Introduction to Learning and Search.- Representations for Mining and Learning.- Generality and Logical Entailment.- The Upgrading Story.- Inducing Theories.- Probabilistic Logic Learning.- Kernels and Distances for Structured Data.- Computational Aspects of Logical and Relational Learning.- Lessons Learned.