Logical and Relational LearningIusethetermlogicalandrelationallearning 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. |
Contents
Introduction 1 | 2 |
An Introduction to Logic | 17 |
An Introduction to Learning and Search | 41 |
Representations for Mining and Learning | 71 |
Generality and Logical Entailment | 115 |
The Upgrading Story 157 | 156 |
Inducing Theories | 187 |
Probabilistic Logic Learning | 223 |
Kernels and Distances for Structured Data | 289 |
Computational Aspects of Logical and Relational Learning | 325 |
Lessons Learned 345 | 344 |
References | 351 |
| 375 | |
| 381 | |
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Common terms and phrases
0-subsumption abductive abductive reasoning algorithm anti-monotonic applied Artificial Intelligence atoms attribute-value background theory Bayesian logic programs Bayesian networks Blockeel Bongard problem c₁ Chapter clausal clause logic compute Consider corresponds covers data mining decision tree defined definite clause denotes distance Dzeroski editors employed formally framework function Getoor graph heuristic illustrated inductive logic programming insert(X instance integrity constraints introduced item-sets kernels language learner learning from entailment learning from interpretations literals logical and relational machine learning Markov Decision Process Markov logic networks Markov models Markov networks metric Muggleton multi-instance negative examples nodes optimal PAC-learning parameters positive examples possible predicate probabilistic logic probability distribution problem Prolog proof propositional propositionalization Q-learning query Raedt refinement operator relational representations represented search space Sect sequence set of clauses specify Springer structure subgraph substitution subsumption techniques theory revision tuples typically upgrade variables variants