An Inductive Logic Programming Approach to Statistical Relational Learning
Proceedings of the 2005 conference on An Inductive Logic Programming Approach to Statistical Relational Learning
Logical bayesian networks and their relation to other probabilistic logical models
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
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Inductive Logic Programming (ILP) [4] combines techniques from machine learning with the representation of logic programming. It aims at inducing logical clauses, i.e, general rules from specific observations and background knowledge. Because of focusing on logical clauses, traditional ILP systems do not model uncertainty explicitly. On the other hand, state-of-the-art probabilistic models such as Bayesian networks (BN) [5], hidden Markov models, and stochastic context-free grammars have a rigid structure and therefore have problems representing a variable number of objects and relations among these objects. Recently, various relational extensions of traditional probabilistic models have been proposed, see [1] for an overview. The newly emerging field of stochastic relational learning (SRL) studies learning such rich probabilistic models.